Surface Topography: Metrology and Properties

<figure><img src="image_11.png" /></figure>

CrossMark

PAPER

A multi-topographical-instrument analysis: the breast implant
texture measurement

RECEIVED
16 November 2016

REVISED
14 April 2017

ACCEPTED FOR PUBLICATION
11 May 2017

PUBLISHED
12 June 2017

Charles Garabédian<fnref n="1" />,<fnref n="2" />, Rémi Delille<fnref n="1" />, Raphaël Deltombe<fnref n="1" />, Karine Anselme<fnref n="1" /> Michael Atlan<fnref n="4" />
and Maxence Bigerelle<fnref n="1" />

E-mail: charles.garabedian@laboratoires-sebbin.fr

Keywords: breast implant, texturation, roughness

Abstract

Capsular contracture is a major complication after implant-based breast augmentation. To address this tissue reaction, most manufacturers texture the outer breast implant surfaces with calibrated salt grains. However, the analysis of these surfaces on sub-micron scales has been under-studied. This scale range is of interest to understand the future of silicone particles potentially released from the implant surface and the aetiology of newly reported complications, such as Anaplastic Large Cell Lymphoma.

The surface measurements were accomplished by tomography and by two optical devices based on interferometry and on focus variation. The robustness of the measurements was investigated from the tissue scale to the cellular scale.

The macroscopic pore-based structure of the textured implant surfaces is consistently measured by the three instruments. However, the multi-scale analyses start to be discrepant in a scale range between 50 µm and 500 µm characteristic of a finer secondary roughness regardless of the pore shape. The focus variation and the micro-tomography would fail to capture this roughness regime because of a focus-related optical artefact and of step-shaped artefact respectively.

1. Background

The fifth-generation breast implant outer surface in
contact with biological tissues features an irregular
texture supposed to stimulate tissue adherence on
the implant and subsequently to avoid an anatomical
misplacement of the prosthesis [1]. The pore size and
pore depth ranges reported by Atlan et al [2] are in
table 1.

Moreover, more and more clinical evidence is in
favor of a texturation on implant surfaces to reduce the
capsular contracture occurrence [3]. This complica-
tion results from the development of a thick and firm
biological layer namely capsule around the implant
[4]. Severe capsular contractures are responsible for
implant distortion and therefore for patient discom-
fort, breast pain and poor aesthetic outcome [3].

Another complication, namely Breast Implant
Associated-Anaplastic Large Cell Lymphoma (BIA-
ALCL), draws the attention of sanitary agencies in the

world because the case figures reported by Brody *et al* [5]
have sharply increased since 1997. No case of BIA-ALCL
has been reported on the Sebbin implants. The aetiology
of this cancer type remains purely speculative, however
‘*the fluid associated with ALCL tended to be cloudy and
debris filled*’ [5]. Danino *et al* [6] points out the micron-
sized silicone particle presence in the capsule. Moreover,
other newly reported complications were raised from
so-called ‘*macro-textured breast implants*’, such as late
seromas or double capsules [7].

According to Whitehouse [8], ‘*all structured sur-
faces are specifically designed to meet a specific functional
requirement*. For implanted devices, the surface func-
tionality is in term of biological integration. Specific
biological entity targeting is the ‘gold-standard’ to
promote implant integration. Therefore, interfaces
between implanted devices and the host are ideally
structured on different length-scale ranges according to
the biological targets. For example, the tissue response
modulation has to be performed on an approximate

<footnote n="1">¹ LAMIH, UMR CNRS 8201, UVHC, Valenciennes, France</footnote>

<footnote n="2">² Groupe Sebbin SAS, Boissy l'Aillerie, France</footnote>

<footnote n="3">³ IS2M, Mulhouse, France</footnote>

<footnote n="4">⁴ UPMC Faculté de Médicine, Paris, France</footnote>
<table><caption>**Table 1.** Pore sizes of two breast implant textures from [2].</caption><thead><tr><th></th><th>Pore size</th><th>Pore depth</th></tr></thead><tbody><tr><td>Biocell® (Allergan Medical Corporation, Santa Barbara, California)</td><td>100–400 µm</td><td>100–200 µm</td></tr><tr><td>Sebbin anatomically-shaped implant texture (Boissy l'Aillerie, France)</td><td>150–600 µm</td><td>100–200 µm</td></tr></tbody></table>

<figure><img src="image_12.png" /><figcaption>**Figure 1.** 'Open-salt-loss technique' (above) and 'coated-salt-loss technique' (below) (processes occur from left to right).</figcaption></figure>

scale of 300 µm, as reported in the literature relative to bone ingrowth promotion and consequently tissue integration of joint replacement [9]. Conversely, the characteristic scale involved in the sensing of nanometric spatial patterning by molecular mechano-sensors responsible for cell attachment on a substrate is around 3 µm [10, 11].

However, the reported measurement protocols of these interfaces were performed only at the scale of the surface target. A screening of measurement scales between the tissue scales and the cellular scales is understudied in the literature. Regarding breast implant surfaces, this analysis is all the more challenging because of the wide gap between the biological scales. Therefore, in a multi-instrument approach aiming at encompassing the scales from the pore size scale to the silicone particle scale, the accuracy of the measurement instrument will depend on the scale. Only a multi-scale analysis will be of value to compare the robustness between the measurement instruments.

# 2. Materials and methods

## 2.1. Sample preparation

Three Sebbin brand implants featuring three different textures as well as a Sebbin smooth implant were studied.

The Sebbin company uses three processes to texture their implant. The first texturation process based on a salt-loss technique pits the last silicone layers of cuboid pores with calibrated salt crystals. Then the salt is removed in a water bath (figure 1).

Alternatively, the process is extended by covering the silicone surface imprinted of salt with a last silicone layer. After curing, this layer is washed away (figure 1).

<figure><img src="image_13.png" /><figcaption>**Figure 2.** 'Sandblasting-replicating technique'.</figcaption></figure>

Finally, the texturation is also obtained by moulding silicone layers on a sandblasted mandrel (figure 2).

In respect to the International Standard [12] relative to breast implants, three sample sets were peeled from the dome, the radius and the base of the implant shell, each set consisting of three 3 mm-diameter samples. The samples were sonicated in a 10% alcoholic solution during 10 min.

The surface of interest in each sample is the 2 mm-side square inside the circular sample.

## 2.2. Surface characterizations

To encompass a large wavelength range, measurements were performed with x-ray micro-tomography (Skyscan™ 1172, Bruker, Billerica, USA), two optical devices, namely the White-Light Interferometer (NewView™ 7300, Zygo, Middlefield, USA) and a Focus variation microscope (Infinite Focus™, Alicaña Imaging GmbH, Grambach, Austria) and a Field Emission Gun-Scanning Electronic Microscope (JEOL 7100F TTLS, Tokyo, Japan).

### 2.2.1. X-ray micro-tomography.

The micro-tomography scan is a Skyscan™ 1172 with a resolution of 2.50 µm and a x-ray source at 80 kV and 100 µA. The scans are imaged on a 4 K × 2 K pixel CCD camera

The reconstruction of scans to a stack of cross-sections along the z-axis was performed after filtering
<figure><img src="image_14.png" /><figcaption>Table 2. Optical settings of White-Light Interferometer and Alicona Infinite Focus™ microscope.</figcaption></figure>

<table><thead><tr><th rowspan="2">Measured surfaces</th><th colspan="2">Interferometry</th><th colspan="2">Focus variation</th></tr><tr><th>Textured implant surface</th><th>Smooth implant surface</th><th>Salt-based textured implant surface</th><th>Sandblasted-replicated and smooth implant surface</th></tr></thead><tbody><tr><td>Objective</td><td>50x</td><td>100x</td><td>10x</td><td>50x</td></tr><tr><td>Vertical resolution</td><td>10 nm</td><td>10 nm</td><td>1 µm</td><td>210 nm</td></tr><tr><td>Lateral resolution</td><td>0.52 µm</td><td>0.32 µm</td><td>6.52 µm</td><td>4 µm</td></tr></tbody></table>

the noise peak. This stack is converted to a file of cross-sections in the (xy) plane. After binarization of the cross-sections, the topography is obtained by capturing the most outer point for each (x, y) position in a matrix (figure 3).

### 2.2.2. White-Light Interferometer and Alicona Infinite Focus™ microscope.

The interferogram bucket is imaged on each pixel of a black and white 640 × 480 pixel CCD camera. Each individual surface is gathered according to the stitching method [13]. The stitching matrix of interferograms consists of 15 × 20 images and the overlapping is 20%. To measure all the textures, the scan length of the electric Z-device has to be extended as high as 800 µm.

The focus variation was computed under a ring light source.

For the two instruments, the choice of the objective and the resulting lateral and vertical resolutions are summarized in the table 2 according to the texture type.

### 2.2.3. Field Emission Gun-Scanning Electronic Microscope (FEG-SEM).

The samples were pinned to the stage. The acceleration voltage is 1 kV and the working distance is approximately 15 mm.

## 3. Results

### 3.1. Surface topographies

The texture morphologies measured by the three instruments are similar (figures 4). Remarkably, sub-micrometric smooth droplets seem to spread on the smooth implant surface (figure 4(A)). The salt-based topographies exhibit randomly-arranged cuboid depressions. The open pores walls are thinner and higher in the 'coated-salt-loss texture' than in the 'open-salt-loss texture' (figures 4(B) and (C)). Many random peaks of maximal 100 µm height feature the 'sandblasting-replicating texture' (figure 4(D)).

In respect to the International Standard [12], Scanning Electronic Microscope (SEM) images were also performed to capture the three textures (figure 5).

### 3.2. Multi-scale analysis

In this study, all surfaces were flattened by a third degree polynomial fit to remove the form of the surface (Mountains Digital Surf™, Besançon, France). No post-measurement sampling and no refilling procedure was applied.

By applying High-Pass filters of lower and lower cut-off wavelengths to the surface, finer and finer
<figure><img src="image_15.png" /><figcaption>**Figure 4.** Topographies of the Sebbin smooth implant (A) and the three Sebbin textures (the ‘open-salt-loss texture’ (B), the ‘coated-salt-loss texture’ (C) and the ‘sandblasting-replicating texture’ (D)) measured by interferometry (first panel), by micro-tomography (second panel) and by Focus variation (third panel).</figcaption></figure>

roughness is isolated from the surface and smaller and smaller scales are therefore captured. Firstly, the 3D arithmetic mean height (Sa) was computed on each filtered surface. This processing was implemented on the set of nine surfaces measured at different locations on

the prosthesis, resulting in the below multi-scale curves (figure 6).

The Sa was retained as a first parameter for the multi-scale analysis, from which an insight on the frequential content of the surface can be drawn.
<figure><img src="image_16.png" /><figcaption>**Figure 5.** The ‘open-salt-loss technique’, the ‘coated-salt-loss technique’ and the ‘sandblasting-replicating technique’ (from left to right). Magnification 40×. Scale bar: 100 µm.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>**Figure 6.** Sa high-pass multi-scale decompositions of the smooth implant surface (A), the ‘open-salt-loss texture’ (B), the ‘coated-salt-loss texture’ (C) and the ‘sandblasting-replicating texture’ (D). The linear tendencies are pointed out by black arrows.</figcaption></figure>

### 3.2.1. Smooth implant surfaces.

A linear evolution—the hallmark of fractal regime— characterizes the curve measured on the smooth implant surface by Interferometer on scales superior to 40 µm (figure 6(A)). If the linear slope is mentioned *H* (Hurst exponent), the fractal dimension is then given by (1).

$$ \Delta = 2 - H \quad (1) $$

Practically, a fractal surface exhibits the same pattern on several scales. The figure 7(A) highlights the wide range of droplet sizes on the smooth implant surface measured by interferometry microscope, contrary to the one measured by Focus variation (figure 7(B)).

Fractal structures are expected to maintain their height-diameter ratio over length scales. Wolf pruning method [14] was implemented on all filtered smooth implant surfaces with screening of minimal height motif, ranging from 0.1% to 10% of the maximal peak height (Sz). If the peak height of each motif is plotted

in function of the peak equivalent diameter in log-log scale (figure 8), the peak fractal dimension is determined as previously (1) with the slope *H* [15].

As highlighted in the figure 8, both curves exhibit three behaviours:

The first stage features the scales inferior to 22 µm and 14 µm, as shown respectively by the Focus variation-related curve and the Interferometry-related curve. The fractal dimension is equal to 2. Under this threshold, topographical instruments fail to measure topography. At this small scale, the output signal from the instrument is a noise. Subsequently, the watershed algorithm results in a noise discretization. Moreover, by considering these thresholds as the instrument lateral resolutions, the Focus variation lateral resolution is approximately twice less precise than the Interferometry one (22 µm and 14 µm respectively). Remarkably, this ratio is consistent with the apparatus settings since the surface is imaged with a 100× and a 50× objective, respec-
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

tively in Interferometry and in Focus variation, result-
ing in the respective finest lateral resolutions 0.32 µm
and 0.64 µm.

The second stage ranges from 22 µm to 80 µm
and from 14 µm to 50 µm, respectively for the Focus
variation and the Interferometry. Both curves exhibit
a slope equal to 1. As a result, peak shapes are Euclid-
ian and not fractal. Practically, a range of homothetic

peaks was measured by Interferometer and by Focus
variation over these scales. Interestingly, the Focus vari-
ation measurement shifts this range to 100% upward
in height and to 20% upward in diameter. The over-
estimation of the peak size resulting from the Focus
variation measurement might be an optical artefact. By
diffusing extensively light, silicone would enlarge the
focus in height and in diameter compared to the real
<figure><img src="image_20.png" /><figcaption>**Figure 9.** Light diffusion-related focus enlargement occurring in translucent peaks.</figcaption></figure>

<table><caption>**Table 3.** Multi-scale parameters characteristic of the salt-based textured surfaces.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">'Open-salt-loss texture'</th><th colspan="2">'Coated-salt-loss texture'</th></tr><tr><th>Hurst exponent</th><th>Lower boundary (μm)</th><th>Hurst exponent</th><th>Lower boundary (μm)</th></tr></thead><tbody><tr><td rowspan="2">Interferometry</td><td>Second regime</td><td>0.052</td><td>35.594</td><td>0.124</td><td>45.512</td></tr><tr><td>Third regime</td><td>0.023</td><td>10.593</td><td>0.075</td><td>9.762</td></tr><tr><td rowspan="2">Micro-tomography</td><td>Second regime</td><td>0.061</td><td>47.992</td><td>0.175</td><td>73.619</td></tr><tr><td>Third regime</td><td>0.059</td><td>19.794</td><td>0.263</td><td>26.592</td></tr><tr><td rowspan="2">Focus variation</td><td>Second regime</td><td>0.066</td><td>40.824</td><td>0.146</td><td>46.626</td></tr><tr><td>Third regime</td><td>0.074</td><td>21.669</td><td>0.173</td><td>16.004</td></tr></tbody></table>

peak (figure 9). Consequently, focalisation technique may fail to capture some translucent peaks.

The Focus variation and the Interferometry measurements start the third stage from 80 µm and from 50 µm respectively. The slope almost equal to zero means that peak heights remain approximately constant whatever the motif size. Therefore, the roughness correlation is completed.

### 3.2.2. *Salt-based textured surfaces.*

The multi-scale analyses of the 'open-salt-loss texturation' (figure 6(B)) and the 'coated-salt-loss texturation' (figure 6(C)) feature for each instrument 3 regimes. The Hurst exponent and the wavelength boundary values are reported for each regime in table 3.

The first stage includes scales superior to approximately 500 µm. This stationary regime is the hallmark of the macroscopic pores pitted by salt grains. As illustrated in the previously mentioned figures, the resulting measurements from the three instruments are robust to capture the salt-related macroscopic topography. This robustness between the instruments is all the more valuable as interferometric measurement captures a very reduced number of points on full sharp slopes.

The second stage is characteristic of scales between 50 µm and 500 µm. Over these scales, roughness on

the bridging areas between the pits and on the bases of depressions is responsible for these fractal regimes. These surface features consist of silicone waviness and deformation and discrepancy in salt pitting depth. Interestingly, the Sa values are higher in the micro-tomography- and in the Focus variation-related curves than in the interferometric one. The small steps paving the micro-tomography-measured surfaces (figure 10) and the peak enlargement caused by a higher resolution in the Focus variation measurements, as mentioned previously, may be responsible for these higher Sa values.

The lower boundaries of the third stage are mentioned in the table 3 and represent the apparatus lateral resolutions. Therefore, once again the micro-tomography and the Focus variation lateral resolutions are much higher than the Interferometry one. These fractal regimes encompass the intrinsic elastomer roughness, regardless of grain shape.

### 3.2.3. 'Sandblasted-replicated surfaces'.

Surprisingly, no scaling law is identified on the 'sand-blasting-replicating texturation'-related curves (figure 6(D)), although stochastic processes, like sandblasting, are the 'gold-standard' to manufacture a multi-scale surface. All sandblasting scales may not be imprinted on
<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

the silicone replica (figure 11), resulting in roughness smoothing on the smallest scales.

### 3.3. Extension to other parameters

To refine the topographical characterization of such surfaces, a similar multi-scale procedure was implemented on four other height parameters: the root mean square height (Sq), the maximal height (Sz), the kurtosis (Sku) and the skewness (Ssk).

A linear correlation was established between the Sa and the Sq (figure 12) with a factor of 0.8. The factor was theoretically demonstrated on the 2D parameters [16]:

$$Ra = Rq \sqrt{\frac{2}{\pi}} \approx 0.8Rq. \quad (2)$$

The Sa and the Sz are also correlated (figure 12). Therefore, the approach led on the Sa may be easily extrapolated for the Sq and the Sz.

The multi-scale curves are in the figures 13 and 14 for the Sku and the Ssk respectively.

A scaling down of the smooth surface measured by Interferometer exhibits a decreasing Sku and Ssk to a length scale of 12 µm (figures 13(A) and 14(A)). Basically, the smooth surface features a range of isolated peaks. When the large scales are filtered, the highest peaks are less and less sharp (figure 15). Therefore, the Sku is expected to decrease during the filtering.

Moreover, the surface is more and more symmetrical because the peaks are less and less high, hence a reduction of the Ssk. Below the scale of 12 µm, the increases of the Sku and the Ssk are characteristic of the droplets described previously with a size range between 10 µm and 50 µm (figure 7). The Focus variation curves support the mentioned tendencies. However, the optical artefact of the focus is responsible for a downward shift of the values. Basically, as the peak base is wider, the Sku is lower.
<figure><img src="image_23.png" /><figcaption>Figure 12. Correlation between the Sa and the Sq and between the Sa and the Sz.</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>Figure 13. Sku high-pass multi-scale decompositions of the smooth implant surface (A), the ‘open-salt-loss texture’ (B), the ‘coated-salt-loss texture’ (C) and the ‘sandblasting-replicating texture’ (D).</figcaption></figure>

For the two salt-based textured surfaces, the multi-scale curves of the Sku are similar for the three instruments (figures 13(B) and(C)). The scale range above 500 µm captures the pore regime. Then the Sku increases between 500 µm and 10 µm. Below the pore scale, a massif of peaks appears at the boundaries of the pores. These peaks are more and more spiked during the filtering (figure 16). Therefore, the Sku is expected to increase. A decrease of the Sku for the Interferometer occurs at the detection limit measured on the multi-scale curve of the Sa (figure 6).

Regarding the Ssk curves of the salt-based textured surfaces (figures 14(B) and (C)) the Micro-tomography and the Focus variation measure an approximate

symmetrical surface at every scale with a Ssk close to 0. The reference plane is therefore translated upward. The Interferometer exhibits a significant increase of the Ssk at the small scales, characteristic of some silicone pore walls abraded during the manufacturing process (figure 17). The Ssk of the ‘coated-salt-loss texture’ is therefore consistently higher than the Ssk of the ‘open-salt-loss texture’.

As the sandblasted surface exhibits an increasing Sku (figure 13(D)), the peaks are sharper and sharper at the small sandblasting scales. Moreover, whatever the scale and the instrument, the surface is symmetrical, as proven by the small discrepancies in the Ssk values (figure 14(D)).
<figure><img src="image_25.png" /><figcaption>**Figure 14.** Ssk high-pass multi-scale decompositions of the smooth implant surface (A), the 'open-salt-loss texture' (B), the 'coated-salt-loss texture' (C) and the 'sandblasting-replicating texture' (D).</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>**Figure 15.** Blunting of the peaks on the smooth surface at small scales. The right picture is filtered with a cut-off of 36 µm.</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>**Figure 16.** Spiking of the peak regime on the salt-based textured surfaces. The middle and the right pictures are filtered with a cut-off of 53 µm and 20 µm respectively.</figcaption></figure>

## 4. Discussion

No extensive and comparative topographical analysis was performed on breast implant shells. Only preliminary surface measurements prior to biological characterizations were reported in the literature [17, 18].

Moreover, the current International Standard relative to breast implants [12] is elusive about the metrological material requirements.

This multi-instrument and multi-scale analysis highlights valuable metrological methodology and results for the editing of a breast implant texture
<figure><img src="image_28.png" /><figcaption>**Figure 17.** The abraded pore walls of the 'coated-salt-loss texture'.</figcaption></figure>

measurement procedure. This study is focused on three instruments: x-ray micro-tomography, White-Light Interferometry and Focus variation microscopy. However, the methodology which aims at comparing the instrument accuracies over a wide scale range encompassing the tissue and the cellular scales is easily extrapolated to other apparatus, such as laser confocal microscopy and atomic force microscopy (AFM).

In spite of an important non-measured point number on full sharp slopes, the White-Light Interferometer is the instrument which has the most complete insight into Sebbin textured breast implant surface topography. Three roughness regimes feature its multi-scale analysis: the pore-based macroscopic structure and two fractal regimes characteristic of secondary salt-derived roughness and of intrinsic silicone roughness. Moreover, contrary to Focus variation, Interferometry captures the fractal sub-micrometric droplets present on Sebbin smooth implant surface.

Usually computer-tomography (CT) is the 'gold-standard' of volumic measurement, especially of porosity computation [19]. Remarkably, the macroscopic topography resulting from the μ-CT measurement and from the downstream reconstruction procedure is robust and metrologically of value. Sebbin textures have been already measured by x-ray micro-tomography without topographical analysis [2]. The texturation scale is also consistent with the calibration specification of the manufacturer. However, a 20 μm-wide 'step-artefact' occurring all over the surface would overestimate the secondary roughness.

The Focus variation accurately captures the primary salt-based roughness. Yet, its measuring principle must be questioned for investigating sub-micrometric translucent features. The light reflection off the surface and the computation of a non-coherent light property—the focus—would result in a non-isotropic artefact led to a 100% motif height increase and a 20% motif diameter increase in Sebbin smooth implant surface and in a higher secondary roughness than the interferometric measurements in Sebbin textured implant surface. This focus enlargement could hinder the measurement of small surface roughness on uncharged-silicone replica.

The multi-scale analysis performed on the 'sand-blasting-replicating surfaces' is not conclusive because of some silicone-replicating-related artefacts. The smallest sandblasting scales are blanked out on the silicone replica because the silicone gel used does not bridge the smallest mandrel roughness. A surface analysis of the mandrel will interestingly complete the results.

According to the multi-scale curves, the three measuring protocols are robust in the capture of the Sebbin textured breast implant macro-structures. Breast implants of other manufacturers were macroscopically investigated in the literature. Valencia-Lazcano *et al* [17] measured on an Allergan Biocell® implant a couple of topographical parameters on a 644 × 642 μm square, contrary to the International Standard requirements [12]. However, the reported Sa value (Sa = 18.83 ± 0.91 μm) [17] is widely underestimated because measuring pores with a largest size of 522 μm over an area of approximate 640 μm² is topographically irrelevant.

Valencia-Lazcano *et al* [17] and Kyle *et al* [18], both from the University of Manchester, also performed a surface analysis on a Mentor Siltex® implant (Mentor Corporation, Santa Barbara, California). A significant discrepancy in the maximal height of the surface (328 μm and 40 μm, respectively from [17, 18]) questions their measuring protocols and their samplings.

The secondary roughness regime is the first discriminating scale range between the three instruments. Barr *et al* [20] paved the way of a multi-scale analysis with a SEM image set capturing the Allergan Biocell® texturation over a surface size range from 2.5 × 2.0 mm to 27.8 × 37.5 μm. At the highest magnifications, Barr *et al* exhibits the 'lack of surface characteristics' between the pores and a 'finer wavy topography to its internal surface', which would be the hallmark of this secondary roughness regime.

Smooth Sebbin implant and smooth Mentor implant [18] surfaces feature random nano- and micro-scale peaks, contrary to ripples characteristic of smooth Allergan implant surfaces [20]. Different polymer curing processes may be responsible for these two patterns.

## 5. Conclusion

The White Light Interferometer captures the widest topographical content of the textured breast implant surfaces over the largest scale range (from the pore size to the scale of 10 nm). To improve the breast implant integration, which is a multi-scale challenge from the tissue scale to the cellular sensor scale, a measurement of the textures by Interferometer is the 'gold standard', compared to the Micro-tomography and the Focus variation.

## References

[1] Maxwell G P, Scheflan M, Spear S, Nava M B and Hedén P 2014 *Aesthetic Surg. J.* **34** 876–81

[2] Atlan M, Bigerelle M, Larreta-Garde V, Hindié M and Hedén P 2016 *Aesthetic Plast. Surg.* **40** 89–97

[3] Barnsley G P, Sigurdson L J and Barnsley S E 2006 *Plast. Reconstr. Surg.* **117** 2182–90
[4] Barr S and Bayat A 2011 *Aesthetic Surg.* **J. 31** 56–67

[5] Brody G S, Deapen D, Taylor C R, Pinter-Brown I.,
House-Lightner S R, Andersen J S, Carlson G, Lechner M G
and Epstein A L 2015 *Plast. Reconstr. Surg.* **135** 695–705

[6] Danino A M, Basmacioglu P, Saito S, Rocher F,
Blanchet-Bardon C, Revol M and Servant J M 2001 *Plast.
Reconstr. Surg.* **108** 2047–52

[7] Hall-Findlay E J 2011 *Plast. Reconstr. Surg.* **127** 56–66

[8] Whitehouse D J 2010 *Handbook of Surface and Nanometrology*
2nd edn (Boca Raton, FL: CRC Press)

[9] Kujala S, Ryhänen J, Danilov A and Tuukkanen J 2003
*Biomaterials* **24** 4691–7

[10] Stevenson P M and Donald A M 2009 *Langmuir*
**25** 367–76

[11] Li F, Li B, Wang Q M and Wang J M 2008 *Cell. Motil.*
*Cytoskeleton* **65** 332–41

[12] ISO 14607 2009 Non-active surgical implants—mammary
implants—particular requirements (Bruxelles European
Committee for Standardization)

[13] Roth J and de Groot P 1997 *Proc. of ASPE Spring Topical Meeting on Advances in Surface Metrology* (Annapolis, MD) pp 57–60

[14] Scott P J 2004 *Proc. R. Soc. Lond. A* **460** 2845–64

[15] Van Gorp A, Bigerelle M and Najjar D 2016 *Polym. Eng. Sci.*
**56** 103–17

[16] Bigerelle M 1999 *PhD Thesis* Ecole Nationale Supérieure
d'Arts et Métiers, Lille, France

[17] Valencia-Lazcano A A, Alonso-Rasgado T and Bayat A 2013
*J. Mech. Behav. Biomed. Mater* **21** 133–48

[18] Kyle D J T, Oikonomou A, Hill E and Bayat A 2015 *Biomaterials*
**52** 88–102

[19] Evans N T et al 2015 *Acta Biomater.* **13** 159–67

[20] Barr S, Hill E and Bayat A 2009 *Eplasty* **9** e22
However, in order to test the applicability of the ISO 14607:2018 standard, additional
measurements and analyses were performed.

Contrary to the specifications of the standard, a measurement over 4m² with AFM is impossible: the apparatus is set up for a maximal measurement of 100µm². Moreover, although non-stipulated in the standard, AFM is only able to measure smooth implant surface. Thus, the multi-scale analysis explicited in the article and designed for an inter-instrument comparison does not make sense with only AFM measurements.

## 2.2. MEASUREMENT AND FIRST ANALYSIS OF BREAST IMPLANT SURFACE WITH SEM AND 3D RECONSTRUCTION SOFTWARE

The SEM measurements must be processed with a specific reconstruction software in order to analyze the topography. Numerous techniques of SEM image reconstruction emerged over the past years. (172) They are based on the principle of stereoscopy, which consists in creating an illusion of relief by superimposing two captures of the same image shot under two slightly different angles. (173) Regarding the SEM 3D reconstruction, the tilt of the stage allows to capture the sample under different angles. The choice of the tilt value is of prime importance: it depends on the lateral resolution and roughness amplitude. (174) Moreover, the tilt must be eucentric: thus, if the tilt is performed on an axis of the stage (for example, the x-axis), only the disparity (which is the difference between the projection of a particular point of interest on the surface of the sample and the projection of the matched point on the tilted surface) on the other axis (the y-axis) is processed by the surface topography reconstruction algorithms. The mathematical details were given by Krishna et al. (175). The authors compared two of these algorithms: the one based on the Piazzesi model function and implemented in the Alicona's MeX software (174, 176) and the other based on the method of triangulation and configurated in the Digital Surf's MountainsMap (2017 MountainsMap Reference manual Digitalsurf.com (www.digitalsurf.com/en/usermanual.html)). The Working Distance (WD) is used as a reference of distance in the algorithms. Actually, instead of point-to-point matching,
a matching extended to the small window around the point was implemented in the
software. (2017 MountainsMap Reference manual Digitalsurf.com
(www.digitalsurf.com/en/usermanual.html))

The standard remains elusive on the choice of the reconstruction technique.
According to the comparative study led by Krishna et al. over a 750 x 500µm surface,
the topographies obtained by the two algorithms were consistent with the
measurements obtained by coherence scanning interferometer. However, the
topographies reconstructed by the MeX software appeared to be noisier and would
be more impaired by contrast and brightness settings than with MountainsMap. We
chose therefore to use the later.

Different techniques of 3D rendering or reconstruction of SEM images were implanted
on MountainsMap based on one, two or the four images provided by the four
quadrants BSE detector (as used in the ANSM-mandated report).

The analysis was led from the 2 image-based reconstruction technique. The SEM (JEOL
JSM-6480) was used with the Lower Electron Detection (LED) setting and an
acceleration voltage of 1kV in order to minimize the accumulation of electron on the
surface. As in the ANSM-mandated report, the Working Distance (WD) was fixed at
approximately 15mm. Regarding the tilt, Krishna et al. specified that the value was
typically comprised between 5 and 20° and that low angles were generally used for
rough surfaces. As we analyzed both smooth and textured implant surfaces, we chose
therefore an intermediary tilt value of 10°. Therefore, the software was implemented
to process an image with a tilt = 0° (T0) and an image with a tilt = 10° (T10). The size of
the window on the sample surface was fixed at 8x8pixel. The size of the matching
window was based on software suggestion.

As this technique was not very documented in the literature, a statistical multi-scale
analysis seemed to be premature at this stage. A preliminary study was however
proposed. Two different magnifications (x70 and x140/x160) were used in order to
screen two different surface areas (720x480µm and 1.5x1.mm). The length: width ratio
was consistent with the one used by Krishna *et al*. The analysis was led on the four types
of surface measured in the enclosed article.

After inputting the SEM images in the software, a first processing, consisting in filling
non-measured points, removing the form and adjusting the off-set in the (x,y) plane to
co-localize the different images, was implemented on MountainsMap. Then, the
topographies were reconstructed. Two reconstruction options were chosen:

* Removal of the outliers

* Use of a smoothing filter, of size 12

The Tables 14-16 exhibit the series of SEM images obtained for each magnification and for each type of surface, as well as the corresponding topographical reconstructions.

Regarding the reconstruction of Sebbin round textured implant surface, the amount
of detail captured was reasonable on the smallest surface areas. For example, the
topographies obtained on one cavity were comparable with the ones measured by
Micro-tomography. (article enclosed to the chapter) However, the discrepancy was
non-negligible for the largest surfaces. Over a surface area of 1.5x1mm, only the form
of the structuration was approximately captured. The bottom of some cavities was not
measured: an artefact comparable to an oil slick (due to the filling procedure) was
then visible inside some cavities. Moreover, the roughness as well as the micro-
roughness were not reconstructed.

Regarding the Sebbin round microtextured implant surface, whatever the
reconstructed surface area, the topography of peaks was very grossly captured and
much smoother than on the topographies depicted in the enclosed article to the
chapter.

The reconstruction of the Sebbin shaped textured implant surface was very poor on a
surface area of 720x480µm. Due to the very low measured area, numerous filling-
related artefacts dominate the resulting morphology. The reconstructions of the larger
surface areas – prone to be even more discrepant - were not then performed.
As a result, the technique could be used only to measure the topography of the Sebbin round textured implant surface on one cavity (over a maximal surface area of approximately 0.3mm²). This limitation was also highlighted in the ANSM-mandated report: by using a different reconstruction technique, they had to restrict their analysis to a surface area of 2.5mm² for surface exhibiting "fine texturation or without texturation".

## 2.3. CONCLUSIONS

This chapter allowed us to compare the relevancy of 5 instruments (namely SEM with a 3D reconstruction software based on two images, Interferometer, X-ray Micro-tomography and Focus variation microscope) to measure breast implant surface. Among the instruments suggested by the standard, only confocal microscope was not studied. The instrument, which seems from the multi-scale analysis to be the most robust on the widest scale range and for the greatest number of topographies, is Interferometry. However, the volume characterization of the samples captured by Micro-tomography (especially the cross-sections provided) allows to have further insight (such as the presence of over-hang or re-entrance) on some topographies than the Interferometry.

As the measurement methodology is validated, the obtained topographies will be then analyzed in order to select a couple of relevant surface parameters. These parameters will allow to establish robust classification of topographies and putative correlation between topographical data and biological or clinical outcomes.
<table><caption>Table 14: A series of SEM images and the corresponding reconstructed topography of the Sebbin round textured surface for each magnification used.</caption><thead><tr><th>Magnification</th><th colspan="2">x160</th><th colspan="2">x70</th></tr><tr><th>Surface area</th><th colspan="2">720x480µm</th><th colspan="2">1.5x1mm</th></tr><tr><th>Tilt</th><th>T0</th><th>T10</th><th>T0</th><th>T10</th></tr></thead><tbody><tr><th>Series of SEM images</th><td></td><td></td><td></td><td></td></tr><tr><th>Reconstructed topographies</th><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>Table 15: A series of SEM images and the corresponding reconstructed topography of the Sebbin round microtextured surfaces for each magnification used.</caption><thead><tr><th>Magnification</th><th colspan="2">x140</th><th colspan="2">x70</th></tr><tr><th>Surface area</th><th colspan="2">720x480µm</th><th colspan="2">1.5x1mm</th></tr><tr><th>Tilt</th><th>T0</th><th>T10</th><th>T0</th><th>T10</th></tr></thead><tbody><tr><th>Series of SEM images</th><td></td><td></td><td></td><td></td></tr><tr><th>Reconstructed topographies</th><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>Table 16: A series of SEM images and the corresponding reconstructed topography of the Sebbin anatomical textured surfaces for each magnification used.</caption><tr><td>Magnification</td><td colspan="2">x140</td></tr><tr><td>Surface area</td><td colspan="2">720x480µm</td></tr><tr><td>Tilt</td><td>T0</td><td>T10</td></tr><tr><td>Series of SEM images</td><td></td><td></td></tr><tr><td></td><td><img src="image1.png" alt="SEM image of T0 sample"></td><td><img src="image2.png" alt="SEM image of T10 sample"></td></tr><tr><td>Reconstructed topographies</td><td colspan="2">[Image of 3D topography]</td></tr></table>
3.1 INTRODUCTION

The multiplicity of terms used to classify the breast implant surfaces, such as "macrotexture", "texture", "microtexture" or "nanotexture", is as confusing as the measurement methodologies. Mostly, these designations rely on marketing or commercial considerations (such as the portfolio proposed by the manufacturers or presumed clinical benefits), without scientific basis. Before incriminating a type of surface as an increased risk factor of some rare pathologies (such as BIA-ALCL), a metrologically-relevant classification of breast implant surface must be established first. Basically, the categories of the classification must be consistent from a point of view of morphologies exhibited by the implant surfaces. Moreover, because of the paucity of reliable clinical data (such as the occurrence of BIA-ALCL cases or the risk of capsular contracture according to the type of surface) due to the late establishment of specific registries or standardized long-term clinical studies, no clinical validation of the classification is currently conceivable. Therefore, the nomenclature must be demonstrated from a biological point of view by *in vivo*, *in vitro* or *ex vivo* assays.

Recently, three major classifications based on topographies and surface parameters (such as Sa or surface area) and supported by biological evidence (such as *in vivo* assay on rats, *in vitro* bacterial attachment assay or *in vitro* macrophage-based assay) were published in the literature. (85, 86, 94) However, although these assays were designed to validate a hypothesis (such as respectively the tissue adherence to the implant surface, the proneness of implant surface to develop biofilm or to elicit inflammatory reactions), it is quite challenging to extrapolate outcomes on the clinical performance and biocompatibility of the device from *in vivo* studies carried out on small animals and from *in vitro* studies which consist of culturing cells or bacteria of interest on implant samples. Given the clinical problematic (i.e. the impact of the different breast implant surfaces on the clinical outcomes, such as the Baker's grade),
an ex vivo study consisting of harvesting and analyzing specific human tissues seems
to us much more valuable and will be developed in this chapter.

Before biological testing, a classification according to the characteristic
morphological structures featured by the measured topographies and cross-sections
was proposed. The discriminant analysis, on which is based the selection of relevant
surface parameters and the clustering of implant surface into consistent categories,
will be detailed in the Chapter IV. Then a molecular approach led on human breast
capsular tissues was chosen to validate our nomenclature. The classification of breast
implant surfaces as well as the transcriptomic analyses of the biological samples were
recently accepted for publication (article below). The acceptation mail from the
editor is also enclosed.

Briefly, our analysis targets some genes involved in the inflammatory and
immunological reactions and in the remodeling of the ECM, which were
demonstrated as significative in two studies comparing non-contracted (Baker I or II)
and contracted (Baker III or IV) capsular tissues. (165, 166) Transcriptomic studies
(qPCR) were performed to quantify the relative expression levels of these genes,
compared to a referent sample (chosen among the most uniform category). In order
to quantify the impact of surface topography on biological mechanisms and to
minimize bias, the samples were retrieved only on non-contracted capsular tissues and
around non-ruptured implant. Moreover, harvesting was performed by a unique
surgeon.

## 3.2 ARTICLE
Surface texturation of breast implant impacts extracellular matrix and inflammatory
gene expression in asymptomatic capsule

Isabelle **Brigaud**¹,²(PhD), Charles **Garabédian**³ (MSc), Nathalie **Bricout**⁴ (MD), Laurent **Pieuchot**¹,² (PhD), Arnaud **Ponche**¹,² (PhD), Raphaël **Deltombe**³ (PhD), Rémi **Delille**³ (PhD), Michael **Atlan**⁵,⁶ (MD, PhD), Maxence **Bigerelle**³ (PhD), Karine **Anselme**¹,² (PhD)

1. Université de Haute-Alsace, CNRS, IS2M UMR 7361, F-68100 Mulhouse, France

2. Université de Strasbourg, France

3. Université de Valenciennes et du Hainaut-Cambrésis, LAMIH UMR CNRS 8201,
Valenciennes, France

4. Private Hospital Saint Germain, Saint-Germain-en-Laye, France

5. Plastic Reconstructive Surgery, Microsurgery, Tissular Regeneration Department, Tenon Hospital Paris, F-75020 Paris, France.

6. Université de Médecine de la Sorbonne, Paris VI, F-75013 Paris, France.
**Corresponding authors:**

Brigaud Isabelle, PhD

Institut de Science des Materiaux de Mulhouse

IS2M - UMR CNRS 7361 - UHA

15 rue Jean Starcky, B.P. 2488

68057 Mulhouse Cedex, France

[isabelle.brigaud@uha.fr](mailto:isabelle.brigaud@uha.fr)

Karine Anselme, PhD

Institut de Science des Materiaux de Mulhouse

IS2M - UMR CNRS 7361 - UHA

15 rue Jean Starcky, B.P. 2488

68057 Mulhouse Cedex, France

[karine.anselme@uha.fr](mailto:karine.anselme@uha.fr)

**Financial disclosure statement: the authors have the following to disclose**

Dr Bricout is an R&D consultant for Groupe Sebbin. Mr Garabédian is employed by Groupe Sebbin. His PhD research is funded by Sebbin, as part of the French government CIFRE program (grant CIFRE 2015–0843). The other authors declare no potential conflicts of interest with respect to the research, authorship, and publication of this article.
**Presented at:**

1) **IMCAS**, Paris, France, 31<sup>th</sup> January-2<sup>nd</sup> February 2019. "Surface texturation of breast implant impacts extracellular matrix and inflammatory gene expression in asymptomatic capsule" (oral). I Brigaud, C Garabédian, N Bricout, K Anselme

2) **MATERIAUX**, Strasbourg, France, November 19-21<sup>th</sup> 2018; "New, simple and biologically validated breast implant classification" (poster). C. Garabédian, I. Brigaud, L. Pieuchot, A. Ponche, M. Bigerelle, K. Anselme

3) **ESB**, Maastricht, Holland, September 9-13 2018. Molecular biology-based breast implant surface classification (poster). C. Garabédian, I. Brigaud, K. Anselme, M. Bigerelle

4) **BIOMAT, Ambleteuse, France, June 12-16 2017**. Biomarker-based evidence of the breast implant texturation benefit (oral). C. Garabédian, I. Brigaud, M. Bigerelle, K. Anselme

**Short Running Head:** Breast implant surface texturing effects
ABSTRACT

**Background:** Texturing processes have been designed to improve biocompatibility and mechanical anchoring of breast implants. However, high texturing degree has been associated with severe pathologies. Here, we aimed to determine whether implant surface topography could also affect physiology of asymptomatic capsules.

**Methods:** We collected topographical measurements from 17 different breast implant devices by interferometry and X-ray microtomography. Morphological structures were statistically analyzed to obtain a robust breast implant surface classification. We obtained 3 topographical categories of textured implants ("peak and valleys", "open cavities", and "semi-opened cavities") based on the cross-sectional aspects. We simultaneously collected 31 Baker I capsules, sorted them according to the new classification, established their molecular profile, and examined the tissue organization.

**Results:** Each of the categories showed distinct expression patterns of genes associated with the extracellular matrix (Timp and Mmp members) and inflammatory response (Saa1, Tnsf11, Il8), despite originating from healthy capsules. Besides, slight variations were observed in the organization of capsular tissues at the histological level.

**Conclusions:** We combined a novel surface implant classification system and gene profiling analysis to show that implant surface topography is a bioactive cue that can trigger gene expression changes in surrounding tissue, even in Baker I capsules. Our new classification system avoids confusions around the word "texture", and could be transposed to implant ranges of every manufacturer. This new classification could prove useful in studies on potential links between specific texturations and the incidence of certain breast-implant associated complications.
INTRODUCTION

Breast implant design involves the selection and combination of several features such as implant filling (silicone gel or saline solution), shape (round or anatomically shaped), volume, and surface (smooth or textured). Implant surface is essential to the performance and safety of breast implant devices. Surface topography -the intricate relief of the outer implant shell- directly affects soft tissue reaction and fibrous tissue formation around the implant, which, if disrupted, could lead to post-surgery complications such as capsular contracture¹,². The link between texturation of implant surface and medical outcomes remains controversial. “Macrostructures” were designed to promote tissue adhesion to the implant.³ However, their impact on capsular contracture⁴⁻⁷ and rare long-term complications such as double capsule⁸, late seroma, and breast implant-associated anaplastic large cell lymphoma (BIA-ALCL)⁹⁻¹³ remains unclear. The relationship between the mechanisms governing breast implant biocompatibility associated with implant surface properties is therefore of great interest.

The formation of capsular tissue around an implant is a physiological wound-healing response to foreign elements. The host organism reacts to the presence of an implant by eliciting local extracellular matrix (ECM) reorganization. These events are tightly regulated by the TGFβ signaling pathway, which directly mediates downstream expression of about 60 ECM-related genes, including the expression balance of matrix-remodeling enzymes such as metalloproteinases (MMPs) or tissue inhibitors of proteinases (TIMPs).¹⁴ The inflammatory response plays a major role in implant integration¹⁵, which can also be promoted by MMPs.¹⁶ We hypothesized that breast implant topography could mediate cell signaling, particularly during the initial formation of capsular tissue. We assumed that different degrees of surface texturation would elicit specific gene activation/response profiles in peri-prosthetic cells, even in healthy tissues such as Baker I capsules.¹⁷
In this study, we conducted a statistically robust but simple and comprehensive classification of breast implants based on the degree of surface texturation. We then compared the impact of different categories of implants on the expression of a panel of ECM and inflammatory genes in healthy capsules. We also examined the effect of such implants on peri-prosthetic tissue organization.

# MATERIALS AND METHODS

## Substrate surface measurement

Surface topography of 17 implant types from 11 independent companies were measured and analyzed as a representative sample of available marketed implants (see table, supplemental digital content 1). The devices examined were new, sterile, and within their period of use. We collected 3 sets of samples from 3 separate locations (dome, edge, and base), obtaining 9 independent measures per implant. Each sample was taken from the implant shells by using a punch to obtain a final circular area of 10 mm², and sonicated in 10% alcohol solution for 10 minutes for cleaning. Samples were measured by interferometry (NewView™ 7300 Optical Surface Profiler; Zygo Corp., Middlefield, CT, USA) for “peak and valley” (PV)-patterned surfaces or X-ray Microtomography (SkyScan™ 1172; Bruker BioSpin Corporation, Billerica, MA, USA) for “open-cavities (OC)” and “semi-opened cavity” (SOC)-patterned surfaces, as described previously.¹⁸ Based on the international breast implant standard (ISO 14607:2018), we restricted our measurements to 4mm² within the circular sample.

## Classification

Topography-based breast implant classification was established using robust statistical discriminant analyses, which will be described in detail in a subsequent publication. Briefly, surface topography measurements were computed using Mountains® software (Digital Surf, Besançon, France) and MesRug® (MesRug Data System, Lieu Saint Amand, France). By
applying filters, surfaces were decomposed in elementary surfaces to scale down intricate
surface roughness properties. This process was applied independently to the 42 most common
roughness parameters (defined in ISO 25178 and EUR 15178N). Classification was established
based on the resulting values. Implants were categorized according to morphological features
observed from surface cross-section and topography analyses as “peak and valley” (PV), “open-
cavities (OC)”, and “semi-opened cavity” (SOC). Cross-sections based on samples analyzed by
X-ray microtomography were established after image reconstruction as described previously.¹⁸
For the interferometric measurements, a 2mm-long profile was extracted from the topography,
by using the Mountains® software, to construct the cross-section. Illustration of the
methods used to establish the final classification is given in supplemental digital content 2.

Patient information and related breast implant capsule sampling

Twenty female patients with 7 different types of textured breast implants, who underwent breast
revision surgeries between February 2017 and February 2018, were recruited for this study
(average age: 51 ± 15 years). A single surgeon performed all surgeries and tissue collection.
Ethical approval was obtained from the institutional review board. Patients gave informed
consent prior to participating. Thirty-one capsular tissue samples were collected based on uni
or bilateral capsulectomies; 5 were harvested from reconstructed breasts, and the rest from
aesthetic surgeries. Fig.1 shows the reasons for revision surgery. The mean duration of
implantation was 9 ± 6 years (range: 1–21 years). Implant location was subglandular for
11 implants and submuscular for 18 implants. Two devices were implanted under a flap while
the positions. Twenty-eight implants were silicone gel-filled and 3 were saline-filled. The
mean implant volume was 286 ± 96 ml (range: 120-525 ml). Samples from patients with
assessed implant rupture were excluded. For more clarity, data are summarized in Fig. 2. In
this study, we restricted our sampling to Baker I-classified capsular tissues¹⁷.
RNA extraction, cDNA synthesis, and quantitative PCR

Collected tissues (0.5cm in at least one dimension in size) were immediately stored in RNA
later solution (Invitrogen™) and preserved at -20°C until further use. For RNA extraction,
tissues were sequentially chopped into pieces with a razor, placed in an Eppendorf containing
glass beads and 300 µL of chilled Trizol, and crushed manually on ice for minutes using sterile
and RNAsse free pestles. Mixtures supplemented with additional 300µL chilled Trizol were then
homogenized using a high speed shaking machine. Total RNA was extracted using the Direct-
zol™ RNA MiniPrep (Ozyme), according to the manufacturer's instructions, including a
DNAse treatment step. RNA purity and concentration were evaluated using a NanoDrop
One® (Thermo Fischer Scientific) based on absorbance ratios A260/A280 and A260/A230. RNA
integrity was assessed on 1% agarose gel electrophoresis. cDNAs were synthesized from
samples normalized to 350 ng RNA, by using the iScript™ cDNA synthesis Kit (Bio-Rad).
qPCR was carried out as described previously.¹⁹ Calibration sample was facing an
OC-patterned topography and served to calculate relative gene quantifications for all
the samples, irrespectively of texture group.

qPCR statistical analyses

Molecular gene expression profiles were generated as boxplots with mean value, standard error of the mean (SEM), and outliers, by using the Plotly software. Statistical analyses were performed by using Student *t*-test and non-parametric Mann-Whitney post-test (GraphPad Prism Software, San Diego, CA, USA). From these analyses, the level of significance was considered to be *p* < 0.001. Because of intrinsic constraints linked to the qPCR technique, we considered samples as *biologically* different for expression ratios higher (overexpression) or lower (repression) than ×3, even if statistical tests were positive.
## RESULTS

### CLASSIFICATION

Fig. 3 illustrates topographical features shared by implants grouped in the same category, giving a representative aspect of the outer implant shell which directly contacts breast tissues. PV-patterned surfaces displayed regular peak and valley structures with heights of low amplitude (<100 µm). In comparison, OC-patterned surfaces mostly presented curve-shaped open cavities features of amplitude ranging from 50 µm to 300 µm. Topographies and cross-sections of SOC-surfaces showed repetitive and regularly distributed unsealed cuboid-like patterns of high amplitude (>400 µm). Cuboid structures protruding from the main surface exposed thin and angled edges at the implant interface. This specific cuboid pattern is generated by salt-loss technique followed by surface brushing treatment.

### GENE EXPRESSION

We evaluated the effects of the 3 breast implant topographical classes in healthy samples on expression of foreign body reaction and inflammatory related genes, and capsular tissue organization. We excluded any chance of expression of the BIA-ALCL markers *Cd30* and *Alk* in our sampling.²⁰ These two markers were not significantly present in our entire sample set (data not shown).

### Relative expression of matrix metalloproteinases (MMPs)

Figure 4 shows the relative expression of *Mmp2*, *MMp9*, and *Mmp12*. No significant change in *Mmp2*, *Mmp9*, or *Mmp12* expression levels were observed in peri-prosthetic cells in contact with PV-patterned surfaces compared to the OC group. On the contrary, *Mmp2* levels were unchanged (Fig. 4A), and *Mmp9* and *Mmp12* were significantly up-regulated (44 ± 11 and 46 ± 21-fold respectively) in tissues in contact with SOC-patterned implants, compared to those in
contact with PV- and OC-patterned implants (Fig. 4B and 4C). Thus, SOC-patterned implants
likely trigger *Mmp9* and *Mmp12*, but not *Mmp2*, in the surrounding tissues.

**Relative expression of tissue inhibitor of metalloproteinases (TIMPs)**

Because TIMP activity closely balances that of the MMPs, we focused our analyses on this gene category (Fig. 5). In tissues in contact with PV-patterned implants, we found that *Timp4* gene expression was significantly up-regulated (4.48 ± 0.94-fold; Fig. 5C) compared to that in tissues associated with OC (1.42 ± 0.20-fold) or SOC (0.35 ± 0.04-fold) patterned implants. In tissues in contact with SOC-patterned implants, *Timp1* and *Timp4* gene expression varied inversely, with *Timp1* expression significantly up-regulated (4.0 ± 0.61-fold; Fig. 5A) and *Timp4* expression down-regulated (0.35 ± 0.04-fold) compared to the PV and OC groups (Fig. 5C). *Timp2* gene expression was not significantly different in the three groups (Fig. 5B). We concluded that contact of PV and SOC-patterned implants with peri-prosthetic tissues affects *Timp1* and/or *Timp4* expression.

**Relative expression of genes related to the inflammatory response**

Immunological response mechanisms are essential for implant integration. We therefore examined the expression of different genes involved in these processes (Figure 6). Variations in *Tgfb1* (Figure 6A) and *Saa1* (Figure 6B) expression were not significant among the samples tested. Capsular tissues located in the vicinity of SOC-patterned implants exhibited up-regulated expression of *Il8* (3.95 ± 0.96-fold) compared to those in the OC and PV-patterned implant groups (Figure 6C). *Tnfs11* was drastically down-regulated in the SOC-patterned implant group (0.19 ± 0.04-fold) compared to the OC (1.15 ± 0.20-fold) and PV (0.88 ± 0.09-fold) groups. Thus, peri-prosthetic cells elicit immunological responses, when in contact with an implant surface, which depend on the topography of the implant.
Elucidating the relationship between degree of breast implant surface texturation and clinical outcomes could greatly improve implant integration. In this study, we linked for the first time characteristics of implant surface topography to gene response in host healthy tissues. We used a new breast implant classification system based on robust statistical analyses, with simple and meaningful nomenclature. Detailed comparison with existing classifications (21,22,23), although important, are beyond the scope of this report and will be discussed in a subsequent paper. Briefly, our new nomenclature provides a robust tool to standardize breast implant prostheses into topographical categories by giving accurate variables to define and properly discriminate between different texture degree. In light of our new classification, we examined the expression profiles of genes that are essential to foreign body reaction. The formation of capsular tissue around an implant involves inflammatory and ECM remodeling responses mediated by TGFβ1. *Tgfβ1* expression was found at a basal level, indicating that wound healing was completed and the peri-prosthetic capsule had reached a steady state. Thereafter, we determined whether the establishment of appropriate bio-interfaces between peri-prosthetic cells and dissimilar breast implant topographies was associated with specific reprogramming of dynamic MMP/TIMP balance activity. ECM-related gene expression levels were significantly affected by the different surfaces. Compared to OC-patterned surfaces, our internal reference for gene variations, only PV-patterned surfaces were correlated with *Timp4* up-regulation, indicating that host organisms recognize these two types of surfaces as relatively similar.

The gene expression profile of peri-prosthetic cells in contact with SOC-patterned surfaces was drastically different. *Timp4* and *Tnsfs11* expression was down regulated. Further, *Mmp9*, *Mmp12*, *Timp1*, and *Il8* were considerably up regulated. We concluded that establishment of an adequate bio-interface for breast implant integration requires adaptation of peri-prosthetic cells to implant topography, which is molecularly modulated through differential and specific
*Mmp/Timp* gene expression. Two studies<sup>24,25</sup> have shown that Baker III capsules also exhibit significant changes in *Timp1*, *Timp4*, and *Tnfs11* expression. It is striking that these asymptomatic capsules share molecular hallmarks with contracted (fibrotic) capsules. However, accurate comparison of the gene expression profiles observed in this study with those described previously is not possible because previous studies did not distinguish between degrees of implant texturation and used different internal calibrators for qPCR calculations. Therefore, we could not conclude which type of surface favors capsular contracture. To elucidate this point, further experiments comparing expression levels of these markers in the context of texturation degree in contracted capsules, using our internal calibrator, are required.

At the tissue level, capsules interfacing SOC-patterned implants were associated with constant higher occurrence of capillary vessels than were OC-patterned implants (Supplemental digital content 3). Interestingly, *Mmp9* gene expression, which is increased in these capsules, dominates the regulation of angiogenesis through several catalytic and non-catalytic functions. Notably, it releases proangiogenic chemokines such as IL8, whose gene expression was also up-regulated.<sup>26-28</sup> IL8, originally identified as a neutrophil chemoattractant<sup>29</sup>, is also known for its pro-inflammatory activity. However, we could not confirm this role, since this type of immune cell was not distinguished in histological sections. This issue could be addressed in further work based on immunohistochemistry analyses for example. Nevertheless, we observed an accumulation of histiocytes at the interface for any surface (Supplemental digital content 3), as described in previous studies.<sup>25</sup>

Finally, we observed drastic down-regulation of *Tnsf11*, which is expressed in fibroblasts<sup>30</sup> and is involved in various physiological processes including immunity. TNFSF11 interacts with the immune system via induction of pro-inflammatory cytokines, acting on several immune cells including monocytes, T cells, and B cells. Notably, TNFSF11 is expressed on activated T-cells and activates mature dendritic cells (<sup>31</sup>), two types of immune cells widely represented in
fibrotic capsules formed around silicone breast implants (³²). Besides a role in activating
the adaptive immune response, it is known to regulate immune tolerance,³³ a process by
which the body reduces or eliminates an immune response to chronic agents³⁴. Considering its
pleiotropic activity and pivotal role in immune system, further experiments are required
to elucidate TNFSF11 activity in capsular tissues.

Restricted to the subset of genes we focused on in this study, we showed that at the molecular
level, peri-prosthetic cells facing PV or OC-patterned surfaces react similarly, while
SOC-patterned surfaces promote drastic gene expression variations. Nevertheless, only future
larger-scale studies both in term of tissue sampling and gene expression analyses (e.g
RNAseq) will allow to first confirm this pilot study and second to discriminate a global
texturation effect at the transcriptome level.

Giot et al⁸ showed that “macro-texturation” (herein classified in the SOC group) clearly induces
strong tissue in-growth into its cavities, which accounts for the exceptional stability of the
capsule-implant complex. As illustrated in our implant cross-sections, tissue ingrowth occurs
inside unsealed cuboid-like patterns with possibly fragile thin walls. Thus, the shearing forces
between implant and capsule caused by natural body movements, applied on strongly attached
ingrowth tissue, could induce tissue alterations. The host body is therefore forced to undergo
constant tissue remodeling as highlighted by *Mmp/Timp* gene expression modulation.

Our data show that surface topography features are bioactive cues differentially integrated by
surrounding healthy periprosthetic capsules and could, in combination with other factors,
contribute to the development of breast implant-associated complications. Further studies on
the combinatorial effects of surface topography with other factors such as presence of a biofilm,
degree of capsule adhesion, and implant positioning ⁶⁻³⁸ are required. In conclusion, design of
surface topography is of prime importance to ensure safe and long-term physiological
integration of breast implants. Finally, we recommend the use of our classification to
classify breast implant surfaces into standardized categories.
**Figure 1.** Recapitulative diagrams of the clinical reasons for implant removal. Percentage (%) of collected capsules A) after aesthetic surgery (n = 26) and B) after breast reconstruction (n = 5).

**Figure 2.** Recapitulative tab gathering implant characteristics and patient clinical data established from the periprosthetic breast capsule (Baker I) tissue sampling. Implant positions: subglandular (SG), submuscular (SM).

**Figure 3.** Illustration of the main surface structures shared by breast implant devices binding in each of the 3 categories, namely PV, OC and SOC. Top view topography and corresponding cross-section is given for one device (underlined market reference) as a representative example of the entire category. Note that in the course of this study, CE mark of Biocell™ breast implants has not been renewed and is now under recall within Europe ³⁸. A color calibration bar in micrometer (µm) indicates the height of the relief on the topographies where black and red represents respectively the lowest and the highest amplitude of topography. Bar scale represents 1,0 mm in length.

**Figure 4.** Boxplots depicting relative gene expression levels Mmp2 (A), Mmp9 (B), and Mmp12 (C) in healthy tissue (Baker I grade) in contact with peak and valley (PV), open cavities (OC), or semi-opened cavity (SOC)-patterned breast implants. Empty circles correspond to outlier values, dotted and full lines correspond to the mean and median values respectively. * p < 0.001 (Student t-test and non-parametric Mann Whitney tests).

**Figure 5.** Boxplots depicting relative gene expression levels of tissue inhibitors of metalloproteinases Timp1 (A), Timp2 (B), and Timp4 (C) from healthy tissue (Baker I grade) in contact with peak and valley (PV), open cavities (OC), or semi-opened cavity (SOC)-patterned breast implants. Empty circles correspond to outlier values, dotted and full lines correspond to the mean and median values respectively. * p < 0.001 (Student t-test and non-parametric Mann Whitney test).
**Figure 6.** Boxplots depicting relative gene expression levels of the pro-fibrotic transforming growth factor beta 1 (Tgfβ1) (A), TNF superfamily member11 (Tnfs11) (B), C-X-C motif chemokine ligand 8 (IL8) (C), and inflammatory markers serum amyloid 1 (Saa1) (D) from healthy tissue (Baker I grade) in contact with peak and valley (PV), open cavities (OC), or semi-opened cavity (SOC)-patterned breast implants. Empty circles correspond to outlier values, dotted and full lines correspond to the mean and median values respectively. \* p < 0.001 (Student t-test and non-parametric Mann Whitney tests).

**Supplemental digital content 1.** Breast implant sampling used to establish topography-based classification. Commercial designation and company for each sample are specified.

**Supplemental digital content 2.** Surface analyses processing overview. 9 samples on 3 independent localizations per breast implant examined are collected to undergo surface topography measurement (X-Ray Microtomography or interferometry). The 9 surface measurements are then filtered (high-pass (HP) or low-pass (LP) filters) at 8 different scales of depth (ranging from 8µm to 952µm). A pool of 672 multi-scale parameter values per surface measurement is obtained by computing 42 surface roughness parameters on each filtered surface. Finally, these 672 multi-scale parameters are 100 times bootstrapped and then paired. An index of classification was designed in order to rank in descending relevance all the couples of multi-scale parameters and to retain the most discriminative classifications.

**Supplemental digital content 3.** Hematoxilin-eosin staining of histological sections (3µm thickness) revealing discrete tissue organization differences between Baker I breast tissues when in contact either with OC-patterned implants or SOC-patterned implants. Images from 2 independent patients for each condition. No malignancies were identified. Tissues facing OC- or SOC- patterned implants exhibit tightly woven fibroblast fibers oriented parallel to the implant surface. Irrespective of the implant texturation, tissues commonly exhibited a layer of mononucleated cells at the implant interface (dotted line) together with discrete and scattered
distribution of histiocytes within the inner part of the capsules. Compared to tissues facing OC-
patterned surfaces, tissues facing SOC-patterned surfaces exhibited more fibro-hyaline
structures together with small congestive capillary vessels (black arrows). ×20 objective.

Scale bar: 100 µm.
**Acknowledgments** We thank Nayana TusamdaWakhloo for feedback on this work and Dr Maria Rosa Katunar (PhD) for help with statistics. We also thank the « NANOTRANSMED » project co-funded by the European Regional Development Fund (ERDF) in the framework of the INTERREG V Upper Rhine program (« Transcending borders with every project ») and by the Swiss Confederation and the Swiss cantons of Aargau, Basel-Landschaft and Basel-Stadt."

The topographical measurements were supported by the Project TRIBOSURF and the Platform MORPHOMECA from the ELSAT2020 project, co-financed by the European Union with the European Regional Development Fund, the French state, and the Hauts de France Region Council.
REFERENCES

1. Abramo AC, De Oliveira VR, Ledo-Silva MC, De Oliveira EL. How texture-inducing contraction vectors affect the fibrous capsule shrinkage around breasts implants? *Aesthetic Plast Surg.* 2010;34(5):555-560. doi:10.1007/s00266-010-9495-9

2. Arpino V, Brock M, Gill SE, et al. Capsular contracture after breast augmentation: An update for clinical practice. *Arch Physiol Biochem.* 2015;116(2):532-543. doi:10.1038/onc.2013.568

3. Wynn T a, Wolfram D, Rabensteiner E, et al. Physico-chemical characteristics of coated silicone textured versus smooth breast implants differentially influence breast-derived fibroblast morphology and behaviour. *Aesthetic Plast Surg.* 2015;34(5):81-91. doi:10.1016/j.jaut.2004.03.005

4. Wong CH, Samuel M, Tan BK, Song C. Capsular contracture in subglandular breast augmentation with textured versus smooth breast implants: A systematic review. *Plast Reconstr Surg.* 2006;118(5):1224-1236. doi:10.1097/01.prs.0000237013.50283.d2

5. Liu X, Zhou L, Pan F, Gao Y, Yuan X, Fan D. Comparison of the postoperative incidence rate of capsular contracture among different breast implants: A cumulative meta-analysis. *PLoS One.* 2015;10(2):1-18. doi:10.1371/journal.pone.0116071

6. Poeppl N, Schreml S, Lichtenegger F, Lenich A, Eisenmann-Klein M, Prantl L. Does the surface structure of implants have an impact on the formation of a capsular contracture? *Aesthetic Plast Surg.* 2007;31(2):133-139. doi:10.1007/s00266-006-0091-y

7. Headon H, Kasem AA, Mokbel K. Capsular contracture after breast augmentation: An update for clinical practice. *Arch Plast Surg.* 2015;42(5):532-543. doi:10.5999/aps.2015.42.5.532

8. Giot JP, Paek LS, Nizard N, et al. The double capsules in macro-textured breast implants. *Biomaterials.* 2015;67:65-72. doi:10.1016/j.biomaterials.2015.06.010
9. Ramos-Gallardo G, Cuenca-Pardo J, Rodríguez-Olivares E, et al. Breast Implant and Anaplastic Large Cell Lymphoma Meta-Analysis. *J Investig Surg*. 2017;30(1):56-65.
doi:10.1080/08941939.2016.1215576

10. Loch-Wilkinson A, Beath KJ, Knight RJW, et al. Breast Implant-Associated Anaplastic Large Cell Lymphoma in Australia and New Zealand: High-Surface-Area Textured Implants Are Associated with Increased Risk. *Plast Reconstr Surg*. 2017;140(4):645-654.
doi:10.1097/PRS.0000000000003654

11. De Boer M, Van Der Sluis WB, De Boer JP, et al. Breast implant-associated anaplastic large-cell lymphoma in a transgender woman. *Aesthetic Surg J*. 2017;37(8):NP83-NP87.
doi:10.1093/asj/sjx098

12. De Boer M, Van Leeuwen FE, Hauptmann M, et al. Breast implants and the risk of anaplastic large-cell lymphoma in the breast. *JAMA Oncol*. 2018;4(3):335-341.
doi:10.1001/jamaoncol.2017.4510

13. Rupani A, Frame JD, Kamel D. Lymphomas associated with breast implants: A review of the literature. *Aesthetic Surg J*. 2015;35(5):533-544.
doi:10.1093/asj/sjv016

14. Bonnans C, Chou J, Werb Z. Remodelling the extracellular matrix in development and disease. *Nat Rev Mol Cell Biol*. 2014;15(12):786-801.
doi:10.1038/nrm3904

15. Steiert AE, Boyce M, Sorg H. Capsular contracture by silicone breast implants: Possible causes, biocompatibility, and prophylactic strategies. *Med Devices Evid Res*. 2013;6(1):211-218.
doi:10.2147/MDER.S49522

16. Issa R, Zhou X, Constandinou CM, et al. Spontaneous recovery from micronodular cirrhosis: Evidence for incomplete resolution associated with matrix cross-linking. *Gastroenterology*. 2004;126(7):1795-1808.
doi:10.1053/j.gastro.2004.03.009

17. Spear SL; Baker JL Jr. Classification of capsular contracture after prosthetic breast reconstruction. *Plast Reconstr Surg*. 1995;96:1119-1124.
18. Garabédian C, Delille R, Deltombe R, Anselme K, Atlan M, Bigerelle M. A multi-topographical-instrument analysis: the breast implant texture measurement. *Surf Topogr Metrol Prop.* 2017;5(2):025004. doi:10.1088/2051-672X/aa7290

19. Brigaud I, Agniel R, Leroy-Dudal J, et al. Synergistic effects of BMP-2, BMP-6 or BMP-7 with human plasma fibronectin onto hydroxyapatite coatings: A comparative study. *Acta Biomater.* 2017;55:481-492. doi:10.1016/j.actbio.2017.04.013

20. Brody GS, Deapen D, Taylor CR, et al. Anaplastic large cell lymphoma occurring in women with breast implants: Analysis of 173 cases. *Plast Reconstr Surg.* 2015;135(3):695-705. doi:10.1097/PRS.0000000000001033

21. Barr S, Hill EW, Bayat A. Functional biocompatibility testing of silicone breast implants and a novel classification system based on surface roughness. I. Barr, S., Hill, E. W. & Bayat, A. Functional biocompatibility testing of silicone breast implants and a novel classification sys. *J Mech Behav Biomed Mater.* 2017;75(May):75-81. doi:10.1016/j.jmbbm.2017.06.030

22. Atlan M, Nuti G, Wang H, Decker S, Perry TA. Breast implant surface texture impacts host tissue response. *J Mech Behav Biomed Mater.* 2018;88:377-385. doi:10.1016/j.jmbbm.2018.08.035

23. Jones P, Mempin M, Hu H, et al. The Functional Influence of Breast Implant Outer Shell Morphology on Bacterial Attachment and Growth. *Plast Reconstr Surg.* 2018;142(4):837-849. doi:10.1097/PRS.0000000000004801

24. Ulrich D, Ulrich F, Pallua N, Eisenmann-Klein M. Effect of tissue inhibitors of metalloproteinases and matrix metalloproteinases on capsular formation around smooth and textured silicone gel implants. *Aesthetic Plast Surg.* 2009;33(4):555-562. doi:10.1007/s00266-009-9335-y

25. Kyle DJT, Harvey AG, Shih B, Tan KT, Chaudhry IH, Bayat A. Identification of
molecular phenotypic descriptors of breast capsular contracture formation using
informatics analysis of the whole genome transcriptome. *Wound Repair Regen.*
2013;21(5):762-769. doi:10.1111/wrr.12077

26. Alaseem A, Alhazzani K, Dondapati P, Alobid S, Bishayee A, Rathinavelu A. Matrix Metalloproteinases: A challenging paradigm of cancer management. *Semin Cancer Biol.* 2017;(November):0-1. doi:10.1016/j.semcancer.2017.11.008

27. Li A, Dubey S, Varney ML, Dave BJ, Singh RK. IL-8 Directly Enhanced Endothelial Cell Survival, Proliferation, and Matrix Metalloproteinases Production and Regulated Angiogenesis. *J Immunol.* 2003;170(3):3369-3376. doi:10.4049/jimmunol.170.6.3369

28. Rundhaug JE. Matrix metalloproteinases and angiogenesis. *J Cell Mol Med.* 2005;9(2):267-285. doi:10.1111/j.1582-4934.2005.tb00355.x

29. Baggiolini M, Loetscher P, Moser B. Interleukin-8 and the chemokine family. *Int J Immunopharmacol.* 1995;17(2):103-108. doi:10.1016/0192-0561(94)00088-6

30. Fouque-Aubert A, Chapurlat R. Influence of RANKL inhibition on immune system in the treatment of bone diseases. *Jt Bone Spine.* 2008;15(1):5-10. doi:10.1016/j.jbspin.2007.05.004

31. Wong BR, Josien R, Choi Y. TRANCE is a TNF family member that regulates dendritic cell and osteoclast function. *J Leukoc Biol.* 1999. doi:10.1002/jlb.65.6.715

32. Wolfram D, Rainer C, Niederegger H, Piza H, Wick G. Cellular and molecular composition of fibrous capsules formed around silicone breast implants with special focus on local immune reactions. *J Autoimmun.* 2004;23(1):81-91. doi:10.1016/j.jaut.2004.03.005

33. Taishin Akiyama, Yusuke Shimo JQ. RANKL signaling controls the develop- ment of regulatory T cells RANKL signaling regulates lymph node development RANKL signaling regulates the microen- vironment of the thymus. 2009;23(1):258-262.
34. Rao S, Cronin SJF, Sigl V, Penninger JM. RANKL and RANK: From Mammalian Physiology to Cancer Treatment. *Trends Cell Biol.* 2017;28(3):213-223. doi:10.1016/j.tcb.2017.11.001

35. Chong SJ, Deva AK. Understanding the Etiology and Prevention of Capsular Contracture. *Clin Plast Surg*. 2015;42(4):427-436. doi:10.1016/j.cps.2015.06.007

36. Brown T, Harvie F, Stewart S. A Different Perspective on Breast Implant Surface Texturization and Anaplastic Large Cell Lymphoma (ALCL). *Aesthetic Surg J*. 2019;39(1):56-63. doi:10.1093/asj/sjy091

37. Mempin M, Hu H, Chowdhury D, Deva A, Vickery K. The A, B and C's of Silicone Breast Implants: Anaplastic Large Cell Lymphoma, Biofilm and Capsular Contracture. *Materials (Basel)*. 2018;11(12):2393. doi:10.3390/ma11122393

38. Cohen D. Breast implants linked to rare cancer are removed from European market. *BMJ*. 2018;363(December):k5401. doi:10.1136/bmj.k5401
<figure><img src="image_29.png" /></figure>
<figure><img src="image_30.png" /></figure>
<table><thead><tr><th rowspan="2">reference to classification</th><th rowspan="2">substrate types of the harvested tissues</th><th colspan="2">capsules</th><th rowspan="2">age or mean age of the patients (years)</th><th colspan="4">implant position</th></tr><tr><th>total sampling = 31 capsules including 20 patients</th><th>duration or mean duration of implantation (years)</th><th>SG</th><th>S</th><th>M</th><th>F</th><th>L</th><th>A</th><th>P</th></tr></thead><tbody><tr><td rowspan="2">peaks and valleys (PV)</td><td>round microtextured implant (Sebbin)</td><td>2 (including 1 patient)</td><td>1</td><td>25</td><td>-</td><td>2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Cereform® (Ceraplas)</td><td>5 (including 3 patients)</td><td>7.2 (from 4 to 9)</td><td>43.6 (from 34 to 47)</td><td>5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">open cavities (OC)</td><td>Siltex® (Mentor)</td><td>1 (including 1 patient)</td><td>18</td><td>43</td><td>-</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>round textured implant (Sebbin)</td><td>9 (including 6 patients)</td><td>10.3 (from 1 to 15)</td><td>54.8 (from 20 to 79)</td><td>5</td><td>4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>TRUE Texture® (Sientra)</td><td>2 (including 1 patient)</td><td>21</td><td>58</td><td>-</td><td>2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">semi-opened cavities (SOC)</td><td>shaped textured implant (Sebbin)</td><td>8 (including 6 patients)</td><td>7.8 (from 5 to 10)</td><td>58.1 (from 46 to 71)</td><td>1</td><td>5</td><td>2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Biocell™ (Allergan)</td><td>4 (including 2 patients)</td><td>5 (from 2 to 8)</td><td>47 (from 46 to 48)</td><td>-</td><td>4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
<table><caption>**Figure 3.** illustration of the main surface structures shared by breast implant devices binding in each of the 3 categories, namely PV, OC and SOC. Top view topography and corresponding cross-section is given for one device (underlined market reference) as a representative example of the entire category. A color calibration bar in micrometer (μm) indicates the height of the relief on the topographies where black and red represents respectively the lowest and the highest amplitude of topography. Bar scale represents 1,0 mm in length.</caption><thead><tr><th>classes</th><th>prostheses sampling</th><th>topographies</th><th>cross-sections</th></tr></thead><tbody><tr><td>peak and valley-patterned surfaces (PV)</td><td><ul><li>SilkSurface™ (Motiva)</li><li>Round microtextured implant (Arion)</li><li>Perthese® (Perouse)</li><li>Cereform® (Cereplas)</li><li>Round microtextured implants (Sebbin)</li></ul></td><td></td><td></td></tr><tr><td>open cavity-patterned surfaces (OC)</td><td><ul><li>Round textured implant (Sebbin)</li><li>TRUETexture® (Silimed)</li><li>MESMO® sensitive (Polytech)</li><li>Nagotex® (Nagor)</li><li>Microcell™ (Allergan)</li><li>Cristalline Micro-textured (Eurosilicone)</li><li>POLYtxt® (Polytech)</li><li>Siltex® (Mentor)</li></ul></td><td></td><td></td></tr><tr><td>semi-opened cavity-patterned surfaces (SOC)</td><td><ul><li>Cristalline Textured (Eurosilicone)</li><li>Shaped textured implant (Sebbin)</li><li>Shaped textured implant (Arion)</li><li>Biocell™ (Allergan)</li></ul></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_31.png" /></figure>
<figure><img src="image_32.png" /></figure>
<figure><img src="image_33.png" /></figure>
<figure><img src="image_34.png" /></figure>
<figure><img src="image_35.png" /></figure>
<figure><img src="image_36.png" /></figure>
<figure><img src="image_37.png" /></figure>
<figure><img src="image_38.png" /></figure>
<figure><img src="image_39.png" /></figure>
<figure><img src="image_40.png" /></figure>
<table><thead><tr><td>Implant type</td><td>Company</td></tr></thead><tbody><tr><td>Biocell™</td><td>Allergan (Dublin, Ireland)</td></tr><tr><td>Microcell™</td><td></td></tr><tr><td>Round microtextured implant</td><td></td></tr><tr><td>Round textured implant</td><td>Sebbin (Boissy, l'Aillerie, France)</td></tr><tr><td>Shaped textured implant</td><td></td></tr><tr><td>Round smooth implant</td><td></td></tr><tr><td>Cereform®</td><td>Cereplas (Sailly lez Cambrai, France)</td></tr><tr><td>Siltex®</td><td>Mentor (Irvine, CA, USA)</td></tr><tr><td>TRUE Texture®</td><td>Sientra (Santa Barbara, CA, USA)</td></tr><tr><td>Cristalline Micro-textured</td><td>Eurosilicone (Apt, France)</td></tr><tr><td>Cristalline Textured</td><td></td></tr><tr><td>POLYtxt®</td><td>Polytech Health and Aesthetics (Dieburg, Germany)</td></tr><tr><td>MESMO® sensitive</td><td>Motiva/Establishment Labs (Alajuela, Costa Rica)</td></tr><tr><td>SilkSurface™</td><td></td></tr><tr><td>Nagotex®</td><td>Nagor (Glasgow, UK)</td></tr><tr><td>Perthese®</td><td>Perouse Plastic (Bornel, France)</td></tr><tr><td>Round microtextured implant</td><td>Arion (Mougins Sophia-Antipolis, France)</td></tr><tr><td>Shaped textured implant</td><td></td></tr></tbody></table>
<figure><img src="image_41.png" /></figure>
<figure><img src="image_42.png" /></figure>
<figure><img src="image_43.png" /></figure>

# PRS Decision

1 message

PRS Journal Editorial Team <em@editorialmanager.com>

Réponde à : PRS Journal Editorial Team <prs@plasticsurgery.org>

À : Charles Garabédian <charlesgarabedian59@gmail.com>

31 juillet 2019 à 22:36

You are being carbon copied ("cc:"d") on an e-mail "To" "Isabelle Brigaud" isabelle.brigaud@uha.fr

CC: "Charles Garabédian" charlesgarabedian59@gmail.com, "Nathalie Bricout" bricout.nathalie@wanadoo.fr, "Laurent Pieuchot" laurent.pieuchot@uha.fr, "Arnaud Ponche" arnaud.ponche@uha.fr, "Raphael Deltombe" raphael.deltombe@uphf.fr, "Remi Delille" remi.delille@uphf.fr, "Mickael Atlan" drmichaelatlan@gmail.com, "Maxence Bigerelle" maxence.bigerelle@gmail.com, "Karine Anselme" karine.anselme@uha.fr

RE: PRS-D-19-00779R2, entitled "Surface texturation of breast implant impacts extracellular matrix and inflammatory gene expression in asymptomatic capsule"

Dear Dr Brigaud,

I am pleased to inform you that your work has now been accepted for publication in *Plastic and Reconstructive Surgery*. Congratulations! All manuscript materials will be forwarded immediately to the production staff for placement in an upcoming issue. The Editorial Office will request any remaining necessary materials and inform you of the publication date once your manuscript has been assigned to a specific issue.

There are many authorized ways for you to increase your work's reach, visibility, and potential citations. Review our **new accepted author toolkit** today to see how you can help us promote your article. Download the toolkit here: http://links.lww.com/PRS/B45.

Start now, by spreading the good news about your article's acceptance:

* Facebook - http://goo.gl/4p6auA

* Twitter - http://goo.gl/3hKWW0

## NOTES:

* You can include your article title or short title in your social media post.

* Here's a ready-made Facebook post you can copy and paste: "PRS just accepted my paper! Read "Surface texturation of breast implant impacts extracellular matrix and inflammatory gene expression in asymptomatic capsule" on PRSJournal.com soon."

* To increase visibility of your post:

* On Twitter, add #PlasticSurgery

* On Facebook, add @Plastic and Reconstructive Surgery (and click on our icon when it appears)

* You **should not** include your article itself or abstract in your post.

Important: if you have not already done so, please do the following:

1) Inform us immediately if you or any co-authors have any financial relationships with any company whose products are mentioned in your article or a company making a competing product if not already stated in the manuscript.

2) Have each author sign and return the copyright release form if it is not already attached to your submission.

Acceptance for publication is made with the understanding that the material in your submission has not been previously published and will not be submitted for publication elsewhere before it appears in the journal.

Thank you for submitting your interesting and important work to the journal. We look forward to receiving more
manuscripts from you in the future.

https://www.editorialmanager.com/prs/

username: ******

password: ******

With Kind Regards,

Rod J. Rohrich, M.D.
Editor-in-Chief
Plastic and Reconstructive Surgery

<figure><img src="image_44.png" /></figure>

In compliance with data protection regulations, you may request that we remove your personal registration details at any time.
(Remove my information/details). Please contact the publication office if you have any questions.
As a result, the surface morphologies highlighted by the classification elicit a specific expression pattern on the selected genes. Even though the pathways (in which are involved the genes studied) are potentially upstream from clinical complications or pathologies, it is out of the scope of the article to conclude on the causative link between some surface topographies and the onset of pathologies because only a restricted set of genes was analyzed. For example, the genetic pool should be extended to the 60 ECM-related genes to have a comprehensive view on the role of fibrosis initiator or inhibitor of some implant surfaces. Moreover, as the biofilm was the most reported causative hypothesis for BIA-ALCL, transcriptomic studies might also be performed on bacterial RNA to identify and quantify bacteria within capsular tissues. Thus, we will be able to test this hypothesis and to determine whether the obtained expression pattern is more correlated with the presence of a specific bacteria than with topographical characteristics of implant surface or vice versa.

In addition, our transcriptomic study might be validated on the protein level by tissue-scale analysis. More particularly, immunohistochemistry could be performed on a piece of capsular tissue using specific antibodies targeting the selected gene-derived proteins. Thus, the protein expression might be localized within the tissue cross-section and correlated with some histological hallmarks (such as the presence of inflammatory or immune cells) or clinical characteristics (such area of adherence between implant and tissue and local thickness of capsular tissue).

Finally, even though the proposed nomenclature is morphologically consistent, it is of outmost importance to select surface indicators and to determine distinct range of values to define each category in order to discontinue the confusions around the current terms of implant surfaces and to rationalize the choice of these designations. Thus, a manufacturer of implant (which is not included in our analysis) could know unequivocally in each category their implant falls. That will be the focus of the next chapter.
4.1. INTRODUCTION

A classification of breast implant surface must be based at least on one topographical
parameter in order to be relevant. Four different classifications of breast implant
surfaces were established in this manner yet. All these classifications include 4
categories. Three classifications were based on only one topographical parameter,
which was either the average roughness amplitude (Sa) (86), the surface area or the
surface area ratio (85, 94). The surface area and the surface area ratio are custom-
made indicators: they quantify the area of the developed surface, which is either
absolute or relative to a "smooth" implant surface. These parameters were arbitrarily
chosen by the authors. In addition to its first parameter (Sa), Barr et al. introduced a
sub-level in its classification, by determining the presence or absence of porosity. (86)
Two linear combinations of parameters obtained by Principal Component Analysis
(PCA) were used for the fourth classification. (https://ansm.sante.fr/S-informer/Points-
d-information-Points-d-information/Surveillance-des-implants-mammaires-par-l-
ANSM-publication-d-un-avis-d-experts-de-CSST-et-d-une-etude-sur-la-texturation-
Point-d-information) This statistical analysis was designed to reduce the initial number
of parameters to only two variables called Principal Components (PC1 and PC2),
which are a linear combination of the initial parameters. The PC are mathematically
built in order to maximize the variance. This technique allows to group the samples
according to a set of relevant variables. Then, to define the different categories, a
linear equation of the frontiers between each scatterplot was then performed. Three
classifications reuse the designations of the manufacturers (such as "smooth",
"nanotexture", "microtexture" and "macrotexture") with slight differences (such as
"mesotexture" and "macrotexture +") while the nomenclature of the fourth one is
completely different ("minimal", "low", "intermediate" and "high") (85). The pool of
implant analyzed in each study is slightly larger than our sampling. For example, the
authors measured a large range of smooth implants.
The boundary values of each category are specified in the Table 17. When a category does not exist in a classification, the corresponding cases are shaded. The equations of the PC are also given.

<table><thead><tr><th></th><th>Barr et al. (86)</th><th>Atlan et al. (94)</th><th>ANSM-mandated report</th></tr></thead><tbody><tr><td>Smooth</td><td>Sa &lt; 5 µm</td><td>80 &lt; surface area &lt; 100mm²</td><td>PC1* &gt; 0.7</td></tr><tr><td>Nanotexture</td><td></td><td></td><td></td></tr><tr><td>Mesotexture</td><td>Sa &lt; 15µm</td><td></td><td></td></tr><tr><td>Microtexture</td><td>10 &lt; Sa &lt; 75 µm</td><td>Porous<br/>Non-porous</td><td>100 &lt; surface area &lt; 200mm²</td><td>PC2** &lt; -2xPC1** -2</td></tr><tr><td>Macrotexture</td><td>Sa &gt; 75 µm</td><td>Porous<br/>Non-porous</td><td>200 &lt; surface area &lt; 300mm²</td><td>PC2** &gt; -2xPC1** - 2</td></tr><tr><td>Macrotexture +</td><td></td><td></td><td>surface area &gt; 300mm²</td><td></td></tr><tr><td>PU</td><td></td><td></td><td>statistical analysis not performed</td></tr></tbody></table>

<table><caption>Table 17: Existing classifications of breast implants based on topographical parameters. A second table is used for Jones et al. because they use another system of classification. The two couples of PC used in the ANSM-mandated classification are distinguished by one or two asterisks.</caption><thead><tr><th></th><th>Jones et al. (85)</th></tr></thead><tbody><tr><td>Minimal</td><td>surface area ratio &lt; 2</td></tr><tr><td>Low</td><td>2 &lt; surface area ratio &lt; 3</td></tr><tr><td>Intermediate</td><td>3 &lt; surface area ratio &lt; 5</td></tr><tr><td>High</td><td>surface area ratio &gt; 5</td></tr></tbody></table>

A comparison between the four classifications is not straight-forward because the parameters or the calculus of parameters are different. For example, contrary to Barr et al., Atlan et al. used absolute values of surface area, which were measured over a 10-mm diameter disk. (94) Noteworthily, the "smooth" and the "mesotexture" categories of the Barr's classification overlap.

Even if these nomenclatures enable the ranking of implant according to relevant surface parameters, the ANSM classification is the only one which statistically justifies the choice of its parameters. (https://ansm.sante.fr/S-informer/Points-d-information-Points-d-information/Surveillance-des-implants-mammaires-par-l-ANSM-publication-d-un-avis-d-experts-de-CSST-et-d-une-etude-sur-la-texturation-Point-d-information)

Basically, the parameters of the PC are graded with a coefficient, which reflect their significance in the analysis. The retained parameters are therefore the most graded
ones. Moreover, by a segregation of the different groups on the 2D mapping, they
also justify the boundaries between each category

Even though the choice of the parameters and the boundaries are more rationalized
in the ANSM report, none of the 4 nomenclatures assessed statistically the robustness
of their classification nor its comparison with the others.

It's the reason why in this section we will assess the statistical robustness of the
nomenclature we proposed and validated biologically in the previous chapter.
Basically, we will describe how we raised a statistically relevant and comprehensive
classification from which we were able to optimally discriminate between different
classes of breast implant surfaces.

As a reminder, the proposed nomenclature is mentioned again in the Figure 9, that
was the Figure 3 of the article enclosed to the Chapter III.
<table><thead><tr><th>classes</th><th>prostheses sampling</th><th>topographies</th><th>cross-sections</th></tr></thead><tbody><tr><td>peak and valley-patterned surfaces (PV)</td><td><ul><li>SilkSurface™ (Motiva)</li><li>Round microtextured implant (Arion)</li><li>Perthese® (Perouse)</li><li>Cereform® (Cereplas)</li><li>Round microtextured implants (Sebbin)</li></ul></td><td></td><td></td></tr><tr><td>open cavity-patterned surfaces (OC)</td><td><ul><li>Round textured implant (Sebbin)</li><li>TRUETexture® (Silimed)</li><li>MESMO® sensitive (Polytech)</li><li>Nagotex® (Nagor)</li><li>Microcell™ (Allergan)</li><li>Cristalline Micro-textured (Eurosilicone)</li><li>POLYtxt® (Polytech)</li><li>Siltex® (Mentor)</li></ul></td><td></td><td></td></tr><tr><td>semi-opened cavity-patterned surfaces (SOC)</td><td><ul><li>Cristalline Textured (Eurosilicone)</li><li>Shaped textured implant (Sebbin)</li><li>Shaped textured implant (Arion)</li><li>Biocell™ (Allergan)</li></ul></td><td></td><td></td></tr></tbody></table>

Figure 9: Illustration of the main surface structures shared by breast implant devices binding in each of the 3 categories, namely PV, OC and SOC. Top view topography and corresponding cross-section is given for one device (underlined market reference) as a representative example of the entire category. A color calibration bar in micrometer (μm) indicates the height of the relief on the topographies where black and red represents respectively the lowest and the highest amplitude of topography. Bar scale represents 1.0 mm in length.

## 4.2. METHODOLOGY

In order to establish statistically robust breast implant classification, we implemented sequential discriminant analyses using SAS v9.3 software (SAS Institute, Cary, NC, USA). As a first step, we performed a one-variable discriminant analysis on the 42 roughness parameters (such as previously defined in international standards ISO 25178 and EUR 15178N) computed over the whole surface area. This approach evaluated the possibility to classify breast implant topography when considering the global range of measured roughness scales. Values of parameters were then oversampled by being bootstrapped 100 times and the resulting averages were noted m(XX*). At each roughness parameter was associated an "index value". This index provides the probability for each parameter to be properly assigned into their respective category.
In order to discriminate more accurately the topographies (i.e. to have higher index values), we took into consideration the decomposition of the surfaces into elementary surfaces of roughness (providing multi-scale parameters). Basically, the topographies were first analyzed using the Mountains® software (Digital Surf, Besançon, France) from the raw measurements. Two Gaussian spatial filters, namely High-Pass (HP) and Low-pass (LP) filters, were applied to decompose all the roughness scales contained in each measured surface. By applying an HP filter, we remove the roughness scales above a defined threshold and obtain only the lowest roughness scales. Conversely, an LP filter extracts the roughness scales higher than the defined threshold. By sequentially increasing or decreasing the threshold value, we are able to reveal the spectrum of all the topographical scales included in each measured surface, namely the surface roughness with HP filter and the surface waviness with LP filter. Then, we calculated for each surface of the spectrum a large number of topographical parameters.

Then, we performed a second one-variable discriminant analysis, using bootstrapped and multi-scale parameter values (the resulting averages of each parameter will be designated by $m(XX*_filter(HP/LP)_cut-off(in \ \mu m)))$. The multi-scale decomposition of topographies was described in the Chapter II. At each multi-scale parameter was associated a new index value. From the analysis, we retained the multi-scale parameters with the first 100 index values only.

Since the one variable discriminant analysis was not satisfying to classify breast implant surfaces, we underwent a two-variable discriminant analysis of the bootstrapped multi-scale parameters. From this analysis, we retained only couples of parameters 1) belonging to the 100 best ones and 2) with a HP and a LP filter of the same cut-off value (i.e. to not have any rupture scale).

An overview of the surface analysis process is illustrated in the Figure 10 that was the Supplemental Digital Content 2 of the article enclosed in the Chapter III.
<figure><img src="image_45.png" /><figcaption>Figure 10: Surface analyses processing overview. 9 samples on 3 independent localizations per breast implant examined are collected to undergo surface topography measurement (X-Ray Microtomography or interferometry). The 9 surface measurements are then filtered (high-pass (HP) or low-pass (LP) filters) at 8 different scales of depth (ranging from 8µm to 952µm). A pool of 672 multi-scale parameter values per surface measurement is obtained by computing 42 surface roughness parameters on each filtered surface. Finally, these 672 multi-scale parameters are 100 times bootstrapped and then paired. An index of classification was designed in order to rank in descending relevance all the couples of multi-scale parameters and to retain the most discriminative classifications.</figcaption></figure>
### 4.3.1. ONE VARIABLE DISCRIMINANT ANALYSES

As a first step, we evaluated if one single topographical parameter, directly retrieved from surface measurement and without multi-scale analysis implementation, was sufficient to discriminate between the 17 different prostheses sampled herein. From this analysis, the 42 bootstrapped roughness parameters were ranked according to an index value. We found that the parameter $m(Sdq*)$ which characterizes the mean quadratic gradient (calculated as the root mean square of slopes at all points in the examined area), has the highest index (0.54), meaning that the highest-score parameter has only 54% of probability to discriminate properly between the different implant topographies. In comparison, the parameter $m(Sa*)$ (arithmetic mean height) or $m(Sdr*)$ (developed surface ratio), used by Barr et al. and Atlan et al. to classify breast implant topography, have both an index value reaching 50% (Fig. 11A). Interestingly, this approach allowed to segregate our sampling into 3 independent classes, named I, II and III with Sdq values ranging from [0], [0.6 to 3.9] and [5.6 to 8.9] for each of the 3 categories that comprised respectively 1, 12 and 4 implants. (Fig. 11B) Although fruitful to discriminate between classes of breast implant surface topography, this approach was not conclusive to distinguish singularities between the implant topography. Indeed, among class II and III, we found distribution curves overlapping partially or entirely between each other (e.g Arion shaped textured implant versus Sebbin shaped texture implant in the Class III group). Therefore, the stringency of this classification was not sufficient to discriminate between the different implant characteristics within a same class.

Since this approach was not conclusive, we implemented the same process (one-variable discriminant analysis) but using multi-scale values. The parameter ranking according to their index values is given for the first 100 positions in Fig. 12A. The robustness of the classification is slightly higher than without multi-scale analysis: $m(Sal*_HP_105.8)$ has the highest index (62%). This parameter characterizes the autocorrelation length (which is a measure of the distance over the surface such that the new coordinates will have minimal correlation with the original coordinates) of the microscopic roughness (i.e. over scales lower than 105.8µm).
However, this analysis failed to discriminate any classes of breast implant surfaces. (Fig. 12B)
<figure><img src="image_46.png" /><figcaption>Figure 11: One-variable discriminant analyses. Ranking of the 42 bootstrapped roughness parameters depending on their index value. Parameters are plotted with their rank (# position) and corresponding index value in descending order (A). Distribution curves of the 17 breast implant surfaces sampled in this study using the highest ranked parameter Sdq led to the discrimination of 3 implant classes (I, II and III) but failed to discriminate between implants within a same class (B).</figcaption></figure>
<figure><img src="image_47.png" /><figcaption>Figure 12: One-variable discriminant analyses. Ranking of the 100 best bootstrapped multi-scale roughness parameters depending on their index value. Parameters are plotted with their rank (# position) and corresponding index value in descending order (A). Distribution curves of the 17 breast implant surfaces sampled in this study using the highest ranked parameter (Sal HP 105.8) failed to discriminate any implant class (B).</figcaption></figure>
The discriminant analysis was then extended to two variables. Basically, we searched for two independent bootstrapped multi-scale parameters (non-correlated) able to recapitulate an appropriate classification.

### 4.3.2. TWO VARIABLES DISCRIMINANT ANALYSES

Once again, the analysis was led on multi-scale parameters. We then chose the best couple of parameters able to discriminate between and within different prostheses classes and without scale rupture between the two elements of the couple. Finally, the parameter ranking according to their index values for the first 100 positions over 226 128 possible combinations was performed. The robustness is much higher than the previous one-variable discriminant analyses (first index value = 99%).

The couple $m(Sdq\*)_{LP\_105.8} / m(Sfd\*)_{HP\_105.8}$ is the first couple in the ranking (70th position), which does not present scale rupture between the two elements of the couple. The first element of the couple characterizes the mean quadratic gradient (i.e. the mean slope for all the points) of the macroscopic roughness (i.e. over scales higher than $105.8\mu m$). The second element is the fractal dimension (or tortuosity) of the microscopic roughness (i.e. over scales lower than $105.8\mu m$). The fractal dimension is an adimensional number comprised between 2 and 3 for a surface, which quantifies the degree of tortuosity ($Sfd = 2$ for a smooth surface and $Sfd = 3$ for a highly tortuous surface). It was estimated by the “box counting method”, which consists of paving the surface with elementary motifs of given dimension (eps). Then, for each dimension of the motif, the number of motifs used is counted (N). $Sfd$ is the slope of the linear regime of the curve $log (N) = f(log(eps))$. Therefore, more $Sfd$ is important, more the surface is tortuous on the small scales.

Interestingly, this approach allowed to segregate our sampling into 4 independent classes, named I, II, III and IV with $Sdq\_LP\_105.8$ values ranging from [0], [0 to 0.4], [0.4 to 1.4] and [1.6 to 2.7] and with $Sfd\_HP\_105.8$ values ranging from [2.17 to 2.32], [2.5 to 2.8], [2.25 to 2.6] and [2.3 to 2.6] respectively for each of the 3 categories that comprised respectively 1, 5, 8 and 4 implants (An additional breast implant surface (Cristalline Textured surface manufactured by Eurosilicone) was included in the
analysis as an *a posteriori* control). (Fig. 13) Moreover, this approach is able to
discriminate between the different implant characteristics within a same class.

<figure><img src="image_48.png" /><figcaption>Figure 13 : Two-variable discriminant analysis. Classification of the 17 breast implant surfaces sampled in this study using the highest ranked parameter couple (m(Sdq*_LP_105.8) / m(Sfd*_HP_105.8)) led to the discrimination of 4 implant classes and to the segregation between implants within a same class. An additional breast implant surface (Cristalline Textured surface manufactured by Eurosilicone) was included in the analysis as a posteriori control.</figcaption></figure>

Importantly, this analysis validates the topographical consistency of the nomenclature
proposed in the Chapter III because the classes I, II, III and IV exactly encompass the
smooth surface category, the PV-patterned surface category, the OC-patterned
surface category and the SOC-patterned surface category. The categories are mainly
discriminated by Sdq_LP_105.8 and therefore by the slope of the macroscopic
roughness: OC- and SOC- surfaces have the highest slope because their LP-filtered
surfaces exhibit pore walls, which are almost vertical, contrary to PV- and smooth
surfaces. (Fig. 14) The slope of SOC-surfaces is more important than OC-surfaces
because they present deeper structure. PV-surfaces have the highest fractal
dimension of the microscopic roughness because contrary to the others, their HP-filtered surfaces do not feature any smooth area.

<table><caption>Figure 14: Measured and filtered surfaces of a characteristic implant type for the 4 categories. A color calibration bar in micrometer (µm) indicates the height of the relief on the topographies where black and red represents respectively the lowest and the highest amplitude of topography.</caption><thead><tr><th>CATEG ORY</th><th>Characteristic implant type</th><th>Measured surface</th><th>HP-filtered surface (cut-off = 105.8µm)</th><th>LP-filtered surface (cut-off = 105.8µm)</th></tr></thead><tbody><tr><td>SMOOTH</td><td>Sebbin round smooth implant</td><td></td><td></td><td></td></tr><tr><td>PICS AND VALLEYS</td><td>Perthese</td><td></td><td></td><td></td></tr><tr><td>OPEN CAVITIES</td><td>Cristalline Micro-textured</td><td></td><td></td><td></td></tr><tr><td>SEMI-OPEN CAVITIES</td><td>Biocell</td><td></td><td></td><td></td></tr></tbody></table>

## 4.3. DISCUSSION

The discriminant analysis statistically justifies the choice of the parameters and the boundaries, on which the proposed nomenclature is based. Moreover, the robustness of the classification was validated by means of an index. In comparison to the index
value obtained with our classification (99%), the classifications published by Barr et al., Atlan et al. and Jones et al. have an index of 50%. Moreover, our classification is the first one able to discriminate between AND within different classes of breast implant surfaces. Basically, almost all the implant surfaces are discriminated between each other with the retained couple of parameters. Thus, the classification is so accurate that it is able to represent even the lowest differences between two types of texture and therefore to reveal the specificity of each one.

Even though they are not able to discriminate all the implant surfaces between each other and the designations of the categories are different, the grouping performed by Barr et al., Jones et al. and Atlan et al. are consistent with ours. The classification of implants into the different categories of each nomenclature is detailed in Table 14. A color was assigned for each category of our classification. We can remark that the "smooth" and the "nanotexture" categories as well as the "minimal" category gather mainly the smooth implants. Most of the implants in the "microtexture" category in the ANSM-mandated report have PV-patterned surface, whereas they have OC-patterned surface for Barr et al. and Atlan et al. Similarly, the "low category" in Jones et al. groups only OC implants. The "macrotexture" categories in Barr et al. and Atlan et al. as well as the "intermediate" category in Jones et al., exhibit primarily SOC-patterned implants whereas Atlan et al. finds more OC implants in the "macrotexture" category than in the "macrotexture +" category. PU-coated implants are also ranked in the "macrotexture +" category, whereas the ANSM-mandated report and Jones et al. added a specific category for those implants.
<table><thead><tr><th></th><th>Barr et al. (86)</th><th>Atlan et al. (94)</th><th>ANSM-mandated report</th></tr></thead><tbody><tr><td>Smooth</td><td>Mentor smooth</td><td>Allergan smooth<br>SilkSurface<br>VelvetSurface</td><td>Arion smooth<br>Allergan smooth<br>Sebbin smooth<br>Silimed smooth<br>Eurosilicone smooth<br>Nagor smooth<br>Polytech smooth<br>Mentor smooth</td></tr><tr><td>Nanotexture</td><td></td><td></td><td></td></tr><tr><td>Mesotexture</td><td>SilkSurface</td><td></td><td></td></tr><tr><td>Microtexture</td><td>Porous<br>Non-porous</td><td>TrueTexture<br>Eurosilicone<br>Microtexture<br>PIP<br><br>Siltex<br>Polytxt<br>Cereplas<br>VelvetSurface</td><td>MESMO<br>Siltex<br>Microcell</td><td>Arion Microtextured<br>Sebbin Microtextured<br>VelvetSurface<br>SilkSurface</td></tr><tr><td>Macrotexture</td><td>Porous<br>Non-porous</td><td>Biocell<br>Sebbin</td><td>Biocell<br>TrueTexture<br>Eurosilicone<br>textured</td><td>Arion Textured<br>Microcell<br>Biocell<br>Polytxt<br>MESMO<br>Nagotex<br>Sebbin Macrotextured<br>Sebbin Textured<br>Silimed Textured<br>Eurosilicone textured<br>Eurosilicone<br>Microtextured<br>Siltex</td></tr></tbody></table>
<table><tr><td>Macrotexture +</td><td></td><td>Polytech PU<br/>Nagotex<br/>Polytxt</td><td></td></tr><tr><td>PU</td><td></td><td></td><td>Polytech PU<br/>Silimed PU</td></tr></table>

<table><caption>Table 14: Comparison of the 4 existing classifications (in color the implants analyzed in our classification: SMOOTH, PV, OC and SOC)</caption><thead><tr><th></th><th>Jones et al. (85)</th></tr></thead><tbody><tr><th>Minimal</th><td>Mentor smooth<br/>Allergan smooth<br/>VelvetSurface<br/>SilkSurface</td></tr><tr><th>Low</th><td>Nagotex<br/>Siltex</td></tr><tr><th>Intermediate</th><td>Eurosilicone textured<br/>Biocell<br/>Polytxt</td></tr><tr><th>High</th><td>Silimed PU</td></tr></tbody></table>

The slight discrepancies between classifications would be due to the measurement methodologies. Thus, the "smooth" or "nanotexture" or "minimum" category mainly corresponds to our smooth category. SilkSurface (a PV-patterned surface) is the only inconsistency in these categories, which could be explained by the instrument used. Indeed, the X-ray Micro-tomography with a voxel size of 2.2µm or 15µm is not appropriate for these types of surface. Then, the "microtexture" or "low" category primarily encompasses the OC-patterned implants. Only Cereplas (a PV-patterned surface) is not an OC implant in these categories, probably because of the overlap between the boundaries. Then, the "macrotexture" or "intermediate" category principally gathers SOC-patterned implants. TrueTexture and Polytxt (OC-patterned surfaces) are the only discrepancies in these categories. Similarly, Polytxt was also
upgraded to the "Macrotexture +" in the classification proposed by Atlan et al. Their singularity (described by Atlan et al. and Jones et al. as "sequestered surface area" or "enclosed pore") was also observed on our classification map. Effectively, a sub-category of the OC-group containing TrueTexture and Polytxt could be easily added. Finally, contrary to our study, Atlan et al. and Jones et al. were able to include PU-coated implants in their last category probably because they used custom-made indicators (such as "surface area" or "surface area ratio") whereas we used standardized surface parameters.

This can be confirmed by our approach. The Figure 15 plots the distribution curves of the 17 implants surfaces obtained by one variable discriminant analysis with the metrological parameter used by Barr et al. (i.e. Sa) and with a metrological parameter similar to the indicator used by Atlan et al. and Jones et al. (i.e. Sdr). The 4 categories (SMOOTH, PV, OC and SOC) are identified on the two charts.
<figure><img src="image_49.png" /><figcaption>Figure 15: Distributions of the 17 implant types analyzed according to the parameters Sdr (A) and Sa (B) (respective index value = [50% ; 50%]) retained after discriminant analysis performed only on parameters computed on measured surfaces, i.e. on m(param*)_LP_8. Identification of 4 categories of implant types corresponding to the 4 differently-colored areas on the Sa-distribution.</figcaption></figure>
Finally, the classification released in the ANSM-mandated report is not more consistent
than the others because the PCA, which is an exploratory technique, is not designed
to discriminate categories. The obtained categories ("smooth", "microtexture" and
"macrotexture") are even more discrepant than the groups identified without
statistical analysis. For example, the "macrotexture" category gathers both OC-
patterned and SOC-patterned implants. This inaccuracy is probably due to the SEM
measurements used in the ANSM-mandated report, which have to be stereoscopically
reconstructed before to be analyzed. (cf. CHAPITRE II).

In addition to be an exploratory technique, the topographical meaning of the PC
(even simplified to 2 terms) are difficult to determine, whereas a correlation between
our classification and the topographical characteristics is much more straight-forward.
The identification of morphologically-relevant group on the classification is therefore
much easier.

4.4 CONCLUSIONS

Although one topographical parameter (such as the Sa or the developed surface) is
able to recapitulate the proposed classification, these parameters do not allow us to
discriminate each texture between each other. For this purpose, the discriminant
analysis has to be based at least on a couple of two parameters. The analysis allowed
us to select the couple constituted of the mean slope of the macroscopic roughness
and the fractal dimension of the microscopic roughness. Contrary to the
nomenclatures published, the statistical technique used validated the robustness of
this couple of parameters.

Silicone debris release from the implant surface could be involved in the pathogenesis
of BIA-ALCL. It is of primer importance to study the putative damage on the surface of
breast prostheses once implanted, which has been performed in the literature yet. A
such ex vivo study should firstly verify whether our classification is able to be extended
to explants, in term of morphologies and obtained values on the retained parameters.
In other words, we will be able to test the *in vivo* robustness of our classification,
Basically, do an implant exhibiting a PV structure remains identifiable as a PV structure, after implantation? Then, in order to assess the biological criticality of such silicone particles, damage must be measured and compared between the different categories of our biologically-validated classification. That will be the focus of the next chapter.
# CHAPTER V: IN VIVO DAMAGES OF DIFFERENT TEXTURED BREAST IMPLANTS

## 5.1 INTRODUCTION

The surface patterning of breast implants (also called texture) is a highly topical subject since, even if the causal link remains to be definitively demonstrated, certain types of texture would represent an increased risk factor for the development of BIA-ALCL. (110)

All these cases raised the question of the etiology of BIA-ALCL. It is acknowledged that BIA-ALCL results from an inflammatory process. Indeed, among the five articles selected in the review of the CHAPTER I, "chronic inflammation" is explicitly reported in 4 articles. (58, 67, 177, 178) Some hypotheses are mentioned in the literature, such as the biofilm or possibly silicone debris released from the surface of the implant. (58, 67, 177) Indeed, Brody et al. noted that "the fluid associated with ALCL tended to be cloudy and debris filled". (35) Moreover, Danino et al. observed micrometric particles in the tissue, which would be released from the Biocell texture. (88) (Fig. 16)

<figure><img src="image_50.png" /><figcaption>Figure 16 : Micrometric particles observed in the capsule. (SEM x7500) (Danino et al.)(88)</figcaption></figure>

For the understanding of biological mechanisms, it is of primer importance to evaluate the mechanical stability of these surface structures. Two in vitro wear simulations were published on breast implants: Webb et al. characterized qualitatively by SEM the damage of three different textures (Biocell, Siltex and TRUE Texture) by peeling an adhesive polymer off the implant surface. (179) The other one performed by Ramaio
et al. imaged also by SEM the damage of the implants of two brands after immersion
in a phosphate-buffered saline solution and in a potassium hydrogen phthalate buffer
solution at controlled temperature and pH for 12 weeks. (171) Briefly, Webb et al.
observed a particulate shedding for the three textures, which is particularly
emphasized for Biocell texture. On the contrary, Ramaio et al. failed to see any
change on the implants' shell structures after immersion during 12 weeks.

However, it is noteworthy that the mechanical stresses that the surface structures could
undergo locally in the breast are not known. They are estimated only approximately
and globally. (180) Moreover, in general biomechanical studies on soft tissues
«oversimplify the structure of the tissues». (168)

Therefore, in the absence of valid biomechanical data on the tissue surrounding
breast implants, it seems somewhat pretentious to want to reproduce the wear
conditions of the textures and therefore to propose an in vitro wear protocol.

In addition to being weakly representative from a biomechanical and anatomical
point of view, a study of the *in vivo* wear of textures on animals is difficult to carry out
if we take into account the long-life of the implants (which is approximately 10 years).

Consequently, the best compromise to characterize the structural evolution of breast
implant textures once implanted in the body of the patient is to topographically
analyze the surface of implants removed from the human body (called "explant").
However, until now there was no article in the literature relating a post-mortem (or ex
vivo) topographical study of breast implant surface although such studies are very
numerous on orthopedic implants. This is all the more surprising since the numbers of
breast implantations and hip procedures are similar, as evidenced by the statistics of
2017 in the USA (322,932 breast augmentations (https://www.isaps.org/wp-
content/uploads/2018/10/ISAPS_2017_International_Study_Cosmetic_Procedures.pdf
) versus 313 246 hip procedures (https://www.ajrr.net/media-news/press-releases/500-
ajrr-releases-2017-annual-report-on-hip-and-knee-arthroplasty-data).
A bibliometric analysis on the Web Of Science has quantitatively substantiated this fact over the last 20 years with "hip implant" (or "prosthesis") and "breast implant" (or "prosthesis") as keywords. (Appendix 1) Briefly, over the last 20 years, there is 10 times less articles on breast implants than on orthopedic implants.

Actually, several barriers still persist to carry out a topographical expertise of the surface of the breast explants.

* Some barriers are related to handling in the operating room: it is not usual to store explants in the breast surgery field.

* Other barriers concern the protocol of explant decontamination, which does not provide for a systematic *a posteriori* analysis of the explant. For example, it is common that after these procedures, fragments of biological tissue remain, which, once hardened, become adherent to the surface of the explant. Topographical measurement of the explant surface at these locations is therefore impossible.

* Traceability is not systematically implemented in breast surgery. Thus, the characteristics of the explant (such as the name of the manufacturer and the device reference) are hardly known because they can be poorly readable on the device or unknown to the patient. Consequently, a straight-forward implant / explant comparison, by measuring the surface of the same prosthesis before and after implantation, would require an average traceability of 10 years, which is rarely performed.

* One of the most impeding barriers is the weak clinical follow-up existing on breast implants, and therefore the few robust statistical elements available. This can be explained by the "aesthetic" connotation of breast implants, which reduces the number of research topics on these implants in favor of implants considered more "functional" such as orthopedic implants.

* Finally, the last barrier, which is all the more blocking as the previous one, is linked to the new version of the ISO 14607: 2018 standard which presents metrological aberrations, in particular on the choice of measuring instruments. For example, according to the standard, the AFM (which is used for measurements of extremely fine roughness and very small surfaces) should be
able to measure all types of textures (including those with almost vertical cavity walls) over a 2mm² area.

In order to be able to characterize for the first time the topographical evolution of breast implant textures after implantation, a study has been set up. As detailed below, our approach is based on a topographical and clinical expertise.

* We relied on a preliminary study that allowed us to validate the choice of our methodology for measuring and analyzing breast implant surfaces. (80) Textures obtained by mold replication was characterized by interferometry. The latter becoming inadequate for textures obtained by "salt-loss" processes because of their strong topographical slopes, X-ray micro-tomography was chosen to measure these last textures.

* A nomenclature of implant textures in three categories, namely Peaks and Valleys (PV), Open Cavities (OC) and Semi-Open Cavities (SOC), has been defined, allowing harmonization of the different designations using terms independent from those used in marketing denominating a texture and that are topographically meaningless.

* To collect the explants of this study, a scientific collaboration was set up with a clinician with 40 years of experience in the field of breast implantation. Statistical analysis was therefore facilitated by the fact that the surgeon explanted what she had implanted, minimizing the loss of information about the device and the patient.

* This approach allowed us to present the first results on a possible ex vivo degradation of different types of texture. A comparison of the degradations between the different textures was also possible.

## 5.2 MATERIALS & METHODS

In this study the patients were followed by the surgeon from the placement to the explantation. The reasons for reintervention could be related to aesthetic reasons (such as too small cup or unsatisfactory breast shape) or postoperative complications
(such as implant rupture, hardening of the breast or pain). All the explants were filled with silicone gel.

These data categorized according to the implant nomenclature we proposed are summarized in Table 15. The texturing processes of the implants are also detailed.

As described by Garabedian et al., surface measurements were made with an interferometer for the PV-assimilated explants and with a micro-tomograph for the OC- and SOC-assimilated explants. (80) Interferometer measurements were performed under white light and with a x50 objective. Regarding the micro-tomograph, the X-ray source was powered by a voltage of 80kV and an intensity of 100µA. The size of the voxels was set at 2.5µm. As often only a fragment of the explant's apex was harvested, three measurements of 4mm² were performed by explant.

For the implant / explant comparison, one implant for each type of texture detailed in the Table 15 (Cereform, Sebbin round implant texture, Micro-textured Crystalline, Nagotex, Sebbin shaped implant texture and Biocell) was analyzed.
<table><caption>Table 15: Implant characteristics and patient clinical data of the explant sampling. For clarity, the color height scale for the PV category was not represented. The approximate maximal height of this type of structure is 100µm.</caption><thead><tr><th>Reference to implant category</th><th>Implant topographies</th><th>Types of textures (manufacturer)</th><th>Texturing process</th><th>Total sampling = 41 explants including 29 patients</th></tr></thead><tbody><tr><td>PV</td><td></td><td>Cereform (Cereplas)</td><td>Mould replication</td><td>3 explants (including 3 patients)</td></tr><tr><td rowspan="3">OC</td><td rowspan="3">[Image of implant topography with color bar showing µm scale]</td><td>Sebbin round implant texture (Sebbin)</td><td>« Salt-loss »</td><td>21 explants (including 15 patients)</td></tr><tr><td>Micro-textured Cristalline (Eurosilicone)</td><td>« Salt-loss »</td><td>2 explants (including 1 patient)</td></tr><tr><td>Nagotex (Nagor)</td><td>« Salt-loss »</td><td>1 explant (including 1 patient)</td></tr><tr><td rowspan="2">SOC</td><td rowspan="2">[Image of implant topography with color bar showing µm scale]</td><td>Sebbin shaped implant texture (Sebbin)</td><td>« Salt-loss »</td><td>13 explants (including 8 patients)</td></tr><tr><td>Biocell (Allergan)</td><td>« Salt-loss »</td><td>1 explant (including 1 patient)</td></tr></tbody></table>

To determine the scale at which the implant / explant comparison is the most relevant in regards to biological phenomena, a multi-scale decomposition of the surfaces was conducted. Basically, the topographies were first analyzed using the Mountains®
software (Digital Surf, Besançon, France) from the raw measurements. Two Gaussian spatial filters, namely High-Pass (HP) and Low-pass (LP) filters, were applied to decompose all the roughness scales contained in each measured surface. By applying a HP filter, we remove the roughness scales above a defined threshold and obtain only the lowest roughness scales. Conversely, an LP filter extracts the roughness scales higher than the defined threshold. (Fig. 17)

<figure><img src="image_51.png" /><figcaption>Figure 17: Principle of surface filtering (example given with a threshold of 105µm). (A) Measured surfaces containing the full range of roughness scales, once filtered by a High-Pass (HP) filter, retain the roughness scales below the threshold value and up to the instrument resolution. (B) Low-Pass filter (LP) filtered surfaces only contain roughness scales above the threshold value.</figcaption></figure>

By sequentially increasing or decreasing the threshold value, we are able to reveal the spectrum of all the topographical scales included in each measured surface, namely
the surface roughness with HP filter and the surface waviness with LP filter. Then, we calculated for each surface of the spectrum a large number of topographical parameters. We are able to easily obtain for each of these parameters its evolution according to the filter threshold and therefore according to the scale. Basically, these plots describe their multi-scale behavior.

In this publication, we restricted our study to the multi-scale analysis of Sa by HP filter because the latter is sufficient to describe the multi-scale behavior of breast implant surfaces. (80)

The damage was finally quantified as the relative difference between the Sa of the implant and that of the explant over the entire wavelength range, as explained in formula [1].

$$ \text{Damage amplitude} = \frac{Sa_{implant} - Sa_{explant}}{Sa_{implant}} \quad [1] $$

An analysis of variance (ANOVA) between the Sa values of the implants and those of the explants, as well as the calculation of the Fisher value (F) was conducted on all the scales studied. The multi-scale curve of the F superimposed on those of the Sa allowed us to graphically represent an area of relevance and an area of non-relevance defined from a threshold of relevance (which is hereafter fixed at 1).

## 5.3 RESULTS

From the multi-scale analysis, we were able to compute the average value of the Sa at a given scale and to plot its evolution according to the scales. At small scales, the Sa characterized only the micro-roughness, which is low. The latter was completed by waviness on the large scales, especially the roughness of the cavities which has a much larger Sa. Thus, this evolution, which will be represented in a logarithmic coordinate system, was increasing. The confidence intervals obtained on the averages were represented by dashed lines.
We thus obtained the multi-scale curve of the average Sa and its confidence interval for the implants, as well as for the explants. We then were able to draw the F value according to the scales, to quantify the degree of significance. Thus, the higher the F value, the larger the difference between the Sa<sub>implant</sub> and Sa<sub>explant</sub>. Conversely, the closer the F value is to 1, the more non-significant the difference.

The combined multi-scale analysis of Sa<sub>implant</sub> and Sa<sub>explant</sub> as well as the one of the F
were initially explained separately for each type of texture. (Fig. 18, 20, 22, 24, 26, 28)
The differences between the topographies of the implants and those of the
corresponding explants were illustrated in parallel. (Fig. 19, 21, 23, 25, 27, 29)

**5.3.1 CEREFORM TEXTURE (CEREPLAS) (PV)**

<figure><img src="image_52.png" /><figcaption>Figure 18 : Multi-scale curves of Sa<sub>implant</sub> and Sa<sub>explant</sub> and F for Cereform texture (PV)</figcaption></figure>
At all scales, the Sa of Cereform explants is smaller than the Sa of the Cereform implant, which is characteristic of a wear phenomenon. The measurements are extremely homogeneous. Given the small dispersions, this difference is significant over all the scales studied, as evidenced by the F curve which is constantly above the threshold of relevance. The wear of the Cereform explants is therefore multi-scale. The damage is gradually less and less pronounced by increasing the scales.

By examining the topographies, we observe a "smoothing" of the micro-roughness, as shown in the Figure 19.

<figure><img src="image_53.png" /><figcaption>Figure 19: "Smoothing" of micro-roughness on Cereform explants (left: implant, right: explant)</figcaption></figure>
5.3.2 SEBBIN ROUND IMPLANT TEXTURE (SEBBIN) (OC)

<figure><img src="image_54.png" /><figcaption>Figure 20 : Multi-scale curves of Sa<sub>implant</sub> and Sa<sub>explant</sub> and F for Sebbin round implant texture (OC)</figcaption></figure>

Once again, the explant curve is below the implant curve except for the very low and the very high scales, which are characterized respectively by a small peak and by an exponential rise on the F-curve. Therefore, the multi-scale analysis of this texture reveals a small increase in roughness amplitude at the low scales and a sharp increase in roughness amplitude at the high scales.

On the other scales, as the dispersion of the measurements on the implant surface is
larger than for PV implant, this difference remains within the significance limit. The
multi-scale curve of Sa calculated on these explants does not attest much damage.
In addition, we note on this scale range a fractal regime (characterized in a
logarithmic coordinate system by a linear evolution of the Sa according to the spatial
length) on the implant curve as well as on the explant curve.
This type of texture, obtained by indentation of calibrated salt crystals on the surface of the non-crosslinked silicone envelope is characterized by cuboidal cavities. It is therefore distinguished by a good stability (and therefore by low damage) at all scales. The damage to the characteristic structures of these cavities (such as the edges, the bottom of the cavities and the plateaus) is topographically confirmed. Indeed, the edges are almost at right angles and the bottom of the cavities and plateaus exhibit a step pattern even on the explant. (Fig. 21)

<figure><img src="image_55.png" /><figcaption>Figure 21 : Damage on Sebbin round textured explants (left: implant, right: explant)</figcaption></figure>
5.3.3 CRISTALLINE MICRO-TEXTURE (EUROSILICONE) (OC)

<figure><img src="image_56.png" /><figcaption>Figure 22 : Multi-scale curves of Saimplant and Saexplant and F for Eurosilicone Cristalline Micro-texture (OC)</figcaption></figure>

A similar implant / explant behavior is observed on the scales higher than 100 µm with a fractal behavior and perfectly superimposable curves on Cristalline Micro-texture. Below 100µm, the decline of the implant curve is more important than for the explant curve. This deviation gradually increases on the very low scales. Then, this increase in the Sa value of the explant (which is almost double at the very low scales) reflects a phenomenon of micro-roughness "creation", which is confirmed by the F-curve. Indeed, the threshold of relevance is exceeded on the scales lower than 60µm and a peak at the very low scales is again observed.

Topographically, the explants are characterized by the homogeneous appearance of very small peaks on the bottom of the cavities and on the plateaus between the
cavities, which is not observed on the implant surface. The edges of these cavities remain almost at right angles (Figure 23).

<figure><img src="image_57.png" /><figcaption>Figure 23 : Appearance of very small irregularities on the Eurosilicone Cristalline Micro-texture explant (left: implant, right: explant)</figcaption></figure>
5.3.4 NAGOTEX TEXTURE (NAGOR) (OC)

<figure><img src="image_58.png" /><figcaption>Figure 24 : Multi-scale curves of Saimplant and Saexplant and F for Nagotex texture (OC)</figcaption></figure>

Contrary to Eurosilicone explant, the Nagor explant exhibits a good stability of its
texture on the low scales (less than 100µm), although it displays a larger dispersion than
the implant. However, on the higher scales, the roughness of the explant is increased,
while having the same dispersion. The roughness difference becomes significant from
200µm, as indicated by the threshold crossing of the F-curve. Once again, a peak is
present on the F-curve at the very low scales. It represents the creation of micro-
roughness.

Based on the Figure 25, the topography of the explant is characterized by an increase
in the width of the cavities with some cavities 4-5 times wider than those on the implant.
Those cavities are also deeper with a depth value of about 270µm on the explant and
200µm on the implant. In addition, cavities on the explant are much more heterogeneous in size and less cubic and more irregular in shape than on the implant.

Noteworthily, the roughness present on the edges of the cavities of the implant is preserved on the explant.

<figure><img src="image_59.png" /><figcaption>Figure 25 : Macroscopic irregularity of the cavities on the Nagor explant (left: implant, right: explant)</figcaption></figure>
5.3.5 SEBBIN SHAPED IMPLANT TEXTURE (SEBBIN) (SOC)

<figure><img src="image_60.png" /><figcaption>Figure 26 : Multi-scale curves of Sa<sub>implant</sub> and Sa<sub>explant</sub> and F for Sebbin shaped implant texture (SOC)</figcaption></figure>

The multi-scale plot for Sebbin shaped implant texture reveals three regimes. The first regime extends on the scales less than 30µm. It is characterized by a more important decrease on the implant curve than on the explant curve. The second regime is between 30µm and 200µm. The behavior of the two curves is fractal and the gap between the curves is in the range of the dispersion. Since 200µm, a third regime starts, featured by an inflection of the implant curve. The multi-scale curve of the explants thus demonstrates a larger roughness on the very low scales (less than 30µm) and a larger roughness on the very high scales (higher than 200µm). The F-curve confirms these 3 regimes. There is a sharp increase in F below 30µm and above 200µm and an intermediate regime characterized by a plateau slightly above the threshold of relevance.
The surface of Sebbin shaped implants is more complex and heterogeneous than OC implants: it exhibits more or less semi-open cavities. Topographically, the explants feature the appearance of very small peaks (their amplitude is in the range of ten microns) within the majority of the cavities, whereas they were completely smooth on the implant. On the higher scales, there is a destruction of their "cap". As only the vertical walls of the cavities remain, the latter appear topographically more open. (Fig. 27)

<figure><img src="image_61.png" /><figcaption>Figure 27 : Macroscopic opening of cavities on the explants (left: implant, right: explant)</figcaption></figure>
<figure><img src="image_62.png" /><figcaption>Figure 28 : Multi-scale curves of Saimplant and Saexplant and F for Biocell texture (Allergan)</figcaption></figure>

Compared with the previous curves, the multi-scale dispersion of the Allergan explant (dashed curves) is much larger, which indicates a very important variability in topographical measurements.

As a result, the multi-scale behavior of the explant is not significantly different from that of the implant over the entire wavelength range, with the exception of small scales, where a peak is once again observed.

The topographies of the Figure 29 illustrate the heterogeneity in size of the cavities present on the surface of the explants, compared to the implant. If you draw a diagonal on the topography of the explant, two areas can be easily identified. In addition, apparently the damage of the “cap” is much more heterogeneous than on the Sebbin shaped explant.
<figure><img src="image_63.png" /><figcaption>Figure 29 : Large heterogeneity of Allergan explant surface (left: implant, right: explant)</figcaption></figure>

**5.4 DISCUSSION**

**5.4.1 VALIDATION OF THE BREAST IMPLANT CLASSIFICATION**

On the basis of a much more extended sampling of implants (17 different types of textures from 7 manufacturers) and a more important database of topographical parameters, we have previously defined a new classification of implants. (cf. CHAPTER III) This classification relies on a complete and robust 2-variable discriminant analysis.

After data bootstrapping, screening of all possible couples of topographical parameters and calculation of a classification index, the classification was obtained with the selected parameter set:

* the average slope (Sdq) of the waviness that has a characteristic scale higher than 105µm

* the tortuosity (Sfd) of the roughness that has a characteristic scale lower than 105µm

The classification allowed us not only to discriminate 4 categories of implants (SMOOTH, PV, OC, SOC), but also to determine the singularities of each type of texture
(which had never been performed until now). These four categories were determined according to the values of these two parameters, as detailed in the Table 16.

<table><caption>Table 16: Determination of categories of the breast implant classification</caption><thead><tr><th></th><th>Average slope of waviness (cut-off=105µm, LP filter) (no unit)</th><th>Tortuosity of roughness (cut-off=105µm, HP filter) (no unit)</th></tr></thead><tbody><tr><td>SMOOTH</td><td>0</td><td>[2.17 to 2.32]</td></tr><tr><td>PV</td><td>[0 to 0.4]</td><td>[2.5 to 2.8]</td></tr><tr><td>OC</td><td>[0.4 to 1.4]</td><td>[2.25 to 2.6]</td></tr><tr><td>SOC</td><td>[1.6 to 2.7]</td><td>[2.3 to 2.6]</td></tr></tbody></table>

Once the different categories of implant textures have been quantitatively identified, it is necessary to wonder whether a prosthesis with a given initial texture is maintained post implantation in its category. For example, does an implant with a PV structure remain identifiable as a PV structure, even after a long implantation time? This interrogation is crucial because it implies that the putative *in vivo* damage to the prosthesis does not alter the discriminating classification structure, for example a SOC prosthesis manufactured by the manufacturer Allergan remains a SOC prosthesis. An affirmative answer to this question would imply that the positive or negative impact of surface texture remains effective during the entire placement of the prosthesis (varying from 1 to 15.3 years in our experimental layout).

On our explant sampling, the selected parameters were calculated and then bootstrapped, in order to apply our classification matrix to the explants. (Fig. 30) The dispersion of the explant scatterplots is comparable to that of implants, with the exception of Allergan. This suggests heterogeneous damage to the Allergan implants. When comparing the scatterplots of the Sebbin shaped implant texture, the Nagotex texture and the Cereform texture with the corresponding explants, there is a slight decrease in the tortuosity of roughness and a slight increase in the average slope of waviness. Regarding the Cristalline Micro-texture, there is only a small increase in the macroscopic slope. However, despite these slight deviations, the categories (as defined on the implants in the Table 16) remain valid on the explants. Unlike orthopedic implants for which friction parts create a new topography after abrasion (181, 182), the initial topographies of breast implants remain the same. Breast
prostheses therefore retain their classification once implanted. Lastly, this structural
stability allows us to validate the robustness of the parameters retained for the
classification since they even are able to discriminate the textures post-implantation.

<figure><img src="image_64.png" /><figcaption>Figure 30: Classification of implants obtained by discriminative analysis (left) and projection of our explants database on this classification (right)</figcaption></figure>

5.4.2 THE TRANSFER FUNCTION OF THE IMPLANT / EXPLANT ROUGHNESS

The comparative multi-scale analysis of the Sa allowed us to specify the ranges of
scale on which the roughness is preserved, destroyed or created. The implant / explant
study gathers together three categories depicted in our classification (PV, OV and
SOC). As the explants remain identifiable in their category, we grouped the explants
according to these categories namely PV, OC and SOC. Thus, we were able to
quantify and therefore to compare the multi-scale damage of the different categories
of texture defined in the classification (Fig. 31).

Thus, the PV<sub>explant</sub> category consists of the Cereform explant, the OC<sub>explant</sub> category of the Sebbin round textured explants, the Nagor explant and the Eurosilicone explant and the SOC<sub>explant</sub> category of the Sebbin shaped textured explants and the Allergan explant.

The multi-scale curves of the damage of these 3 categories allowed us to identify
different regimes, as indicated in the Figure 31.
<figure><img src="image_65.png" /><figcaption>Figure 31: Identification of the damage regimes of the different categories of texture and associated topographical characteristics</figcaption></figure>

### 5.4.3 DAMAGE OF PV STRUCTURE

As the PV category is only represented by the Cereform texture, the damage curve entirely reflects the multi-scale behavior of this texture, i.e. a decline of the Sa on all the scales studied but which gradually becomes more important on the low scales.
Due to the texturing process (sandblasting), the Cereform texture is a multi-scale surface. (80) Indeed, the impact of the grains of sand creates craters first. Then, since sandblasting is a stochastic process, craters of much smaller amplitude appear on the initial craters.

According to the multi-scale curves, these are firstly the smallest peaks for which the amplitude decreases. This decrease in amplitude of the peaks becomes smaller and smaller on the high scales, ending up almost stable. This amplitude drop on the low scales is symptomatic of a material removal, which is much more favored for small than for large peaks. This suggests a mechanical action on the surface, which is attributable to wear. Thus, a damage of these prostheses in the body by multi-scale abrasion is worth considering. If this hypothesis is confirmed, a generation of small debris is expected. Given that a 40% decrease in Sa occurs for a spatial scale of 9µm, it is reasonable to assume a release of debris smaller than this size.

Noteworthily, for scales between 10µm and 25µm, the transfer function has a U-shape curve. Although the transfer coefficient remains negative, we distinguish a rise in the transfer coefficient for a critical length of 15µm. A U-shape curve is often the hallmark of two antagonistic mechanisms. In our case, another mechanism (which will also be noted for OC- and SOC-patterned prostheses) simultaneously occurs. Thus, while we have an erosion more and more important at the low scales due to a preferential erosion of small peaks, a micro-roughness is created due to an abrasion between the silicone and the biological tissue.

In order to validate this hypothesis, a 2-D phenomenological model was applied to reproduce the effect of different damage mechanisms. This model is similar to that implemented to simulate the abrasion of metal components. (183) It remains, however, to analyze more finely whether the wear is distributed all over the contact which, given the softness of the connective tissues, seems highly likely. In our model, we assumed that the wear was concentrated on the positive curvature part of the roughness profile.
To model the damage of PV implants, only a mechanism called “debris mill” was taken into account. Basically, our model allowed us to simulate the 2-D roughness attributable to the debris mill mechanism and to visualize the impact of this roughness on the initial 2-D roughness of the implant. In our case, the model was applied on a profile characteristic of the PV implant. (Fig. 32)

<figure><img src="image_66.png" /><figcaption>Figure 32 : 2-D damage modelling of the debris mill mechanism on a PV implant.</figcaption></figure>

### 5.4.4 DAMAGE OF OC AND SOC STRUCTURES

For the other textured surfaces (i.e. OC and SOC), the following features are common and define three regimes:

* An increase in roughness below 25µm: intra-pore roughness regime

* A multi-scale stabilization of roughness between 30 and 200µm: pore roughness regime

* An increase of the roughness from 200µm: extra-pore roughness regime

These behaviors are completely different from that of PV structure. Indeed, for the latter, no increase in roughness and no stabilized regime was observed.

There are, however, two fundamental differences between SOC and OC structures:

* The SOC structure exhibits an increase in roughness (of around 10%) in the stabilized regime (between 30 and 200µm), whereas the OC structure presents a 5% decrease in roughness in the same regime.
* The amplitude rise on the high scales (superior to 1 mm) is larger for OC structure (50%) than for SOC structure (35%).

5.4.4.1 INTRA-PORE ROUGHNESS REGIME

Concerning the damage curve of OC and SOC structures, there is a very sharp increase in roughness for the low scales which decreases further to stabilize at a common spatial scale of approximately 30µm. We note that this type of damage on these spatial ranges also occurred on the PV prostheses. Importantly, we therefore have a common mechanism of damage between all the textures, which we will call the debris mill. The abrasion (probably coupled with adhesive wear) creates a micro-roughness with release of elastomeric particles of size inferior to 30µm. As this roughness is always increasing (and tends to an exponential growth) as the scale decreases, it appears that the damage only occurs on the scales much lower than 10µm, suggesting the generation of submicron debris. The amplitude or severity of this mechanism (characterized for example by the volume of debris released per unit time) cannot be deduced from a purely geometric study. However, it is possible, from the surface morphology, to propose a first quantification of the prosthesis' resistance regarding the mechanisms of abrasive damage. If we assume that the wear is abrasive, then a larger roughness created by this abrasive mechanism will potentially create a larger particle flow. OC and SOC-patterned prostheses have a sharp increase of roughness at the low scale (a rise of 40% at a scale of 10µm). This would suggest that the damage is important on these textured prostheses. The presence of a crenel-shaped structure could lead to a concentration of shear on the plateaus of the surface morphology thus facilitating abrasion. However, not all prostheses (manufacturers) have the same degree of roughness creation. (Table 17)
<table><caption>Table 17: Difference between the Sa<sub>explant</sub> and the Sa<sub>implant</sub> (or Delta) for each texture at a scale of 10µm.</caption><thead><tr><th></th><th>Delta at a scale of 10µm (µm)</th></tr></thead><tbody><tr><td>Sebbin shaped implant texture</td><td>0.711</td></tr><tr><td>Cristalline Micro-texture</td><td>0.266</td></tr><tr><td>Nagotex</td><td>0.247</td></tr><tr><td>Sebbin round implant texture</td><td>0.072</td></tr><tr><td>Biocell</td><td>-0.024</td></tr><tr><td>Cereform texture</td><td>-0.095</td></tr></tbody></table>

The Sebbin shaped implant texture has the most important degree of roughness creation, as confirmed topographically (Fig. 33).

<figure><img src="image_67.png" /><figcaption>Figure 33: Appearance of microscopic irregularities within cavities of the Sebbin shaped explants</figcaption></figure>

This difference in term of damage can be caused by either the silicone mechanical properties or the macroscopic (shearing and normal) shear forces generated by the macroscopic shape (superior to 200µm) of the surface topography. In summary, the topographical analysis on the low scales of explants can help to determine the basic mechanism of abrasion damage on breast implants and the potential origin of debris release.

As previously, we modeled damage to OC and SOC-patterned prostheses including the debris mill (Fig. 34 and 35). However, other mechanisms characteristic to the damage of these types of implant (such silicone heterogenous stress relaxation,
silicone homogeneous stress relaxation, wear of the pore wall and wear of the "cap")
were included in the model.

The results of the model will be commented later on with the study of the two other
regimes (pore roughness regime and extra-pore roughness regime).

<figure><img src="image_68.png" /><figcaption>Figure 34 : 2-D damage modelling of the damage mechanisms on an OC implant</figcaption></figure>

<figure><img src="image_69.png" /><figcaption>Figure 35 : 2-D damage modelling of the damage mechanisms on a SOC implant</figcaption></figure>

5.4.4.2 PORE ROUGHNESS REGIME

This regime is described by a multi-scale stabilization of the roughness between 30 and
200µm. The SOC structure exhibits a slight increase in roughness (of approximately 10%)
in this stabilized regime, whereas the OC structure presents over the same regime a
slight decrease of 5% in roughness.

This spatial range characterizes the roughness of a pore. Remarkably, the transfer
function remains constant, therefore the transfer is uniform in this roughness range.

For OC-patterned prostheses, the transfer function is negative (a 5% decrease in
amplitude) which is therefore characteristic of wear. However, contrary to PV-
patterned prostheses, this wear is constant. This constant regime could be explained
only if the damage is uniform on this range. The only mechanical explanation to that
observation would consist in an erosion of the walls, which has an almost constant
surface with depth, thus presenting a constant wear over a wide range of spatial
scales (Fig. 31). Once again, wear is generated by the debris mill previously described
on the very low spatial scales.

For SOC-patterned prostheses, the transfer function is positive (a 10% rise of amplitude), which seem counterintuitive compared to the wear mechanism previously presented for SOC structure. However, the morphology of SOC prevents an integral measurement of the pore roughness. Indeed, the presence of a "cap" precludes an integral estimation of the roughness on the bottom of the pore. No roughness parameter is designed to characterize this morphology. Likewise, with the exception of a particular analysis methodology specific for the 4-D morphology, very few instruments are able to measure a complete topography with micrometric resolution. Therefore, a bias will appear in the measurement of Sa. (Fig. 36) The opening of the explant pores shown on the measurements of the Figure 27, will therefore increase the Sa by revealing a larger mass of valley (bottom of the pore). A more detailed analysis is proposed in the Appendix 2 by the multi-scale study of Skewness (Ssk). Once again, wear is generated by the debris mill previously described on the very low spatial scales. It should be noted, according to the Figure 29, that the Allergan implants (particularly incriminated in the BIA-ALCL appearance (110) exhibit a more heterogeneous damage to the "cap" causing a very strong dispersion on the roughness, which is not observed on the Sebbin prostheses. Noteworthily, the skewness of the micro-roughness of the Allergan explant is positive (Ssk = 0.1) while it is negative (Ssk=-0.2) for the Sebbin
shaped textured explants (result not shown). This reflects the presence of sharper
roughness peaks for Allergan explants than for Sebbin shaped textured explants (Fig.
27 and 29). The Ssk study also showed that the Allergan implant has more open pores
than Sebbin shaped textured implants. It could be hypothesized that the stronger
constraints applied during manufacturing to the Allergan prosthesis to open the pores
may be responsible for the heterogeneity of the explants. This heterogeneity is liable
to generate locally more sharp micro-roughness and therefore more intense debris
mill.

<figure><img src="image_70.png" /><figcaption>Figure 36 : Schematic representation of the bias, which will appear on the Sa values during the opening of SOC structures</figcaption></figure>

5.4.4.3 EXTRA-PORE ROUGHNESS REGIME

For OC and SOC-textured surfaces, they have in common an increase in roughness above 200µm: an extra-pore roughness. However, the increase in amplitude on the high scales (superior to 1 mm) is larger for OC structure (50%) than for SOC structure (35%). This behavior is completely different from that of the PV structure for which no increase in roughness was observed.

Several hypotheses can be made to explain this increase in extra-pore roughness for OC and SOC-textured surfaces. Wear at high scale must be rejected since it is clearly impossible to increase topography by wear (scratch) at this scale and if present, it would clearly appear on topographical map. Also, it could be shown that this extra-
pore roughness does not depend on the implantation time (Fig. 37) meaning that a
pure homogeneous process of materials creep or relaxation must be rejected.

<figure><img src="image_71.png" /><figcaption>Figure 37 : The Sa evolution computed on the topographies measured (i.e. before multi-scale analysis) according to the implant duration</figcaption></figure>

Another hypothesis can be proposed to explain the increase in extra-pore roughness
for OC and SOC-textured surfaces.

Due to irregularities and stochastic nature of the salt-imprint process of texturation,
stress heterogeneities may be created during curing. After implantation, and under
normal stress, shear stress and activation energy, local stress heterogeneities may
disappear and during this stress relaxation, a waviness greater than the mean size
between 2 consecutives salt imprints (> 500µm) may appear. Then, this waviness may
increase the Sa on a critical length higher than the individual salt- imprint size. This
phenomenon was named "heterogeneous stress relaxation". The visualization of the
OC explant's undulation shows this relaxation well (Fig. 38). We have
phenomenologically modelled this heterogeneous stress relaxation (Fig. 34) showing a
persistent wave on the global profile. Contrary to OC and SOC-textured surfaces, the
amplitude does not increase for long wavelength on PV structures. That can be
explained by their quite homogeneous structure due to the multi-scale aspect of the
grinded surfaces that minimizes the effect of stress relaxation.

<figure><img src="image_72.png" /><figcaption>Figure 38 : OC explant's undulation characteristic of a "heterogenous stress relaxation"</figcaption></figure>

**5.5 CONCLUSION AND OUTLOOK**

This study is the first study of *in vivo* damage. With the use of the most optimal
techniques (i.e. interferometry and micro-tomography), the damage of different types
of breast implant texture was quantified, compared and justified topographically on
all the scales. These two experimental techniques are not mentioned in the ISO 14607
standard.

Various conclusions could be drawn from this study:

* Damage is described by a morphology at all the scales.

* Despite their damage, prostheses retain their original morphological appearance. There is no complete destruction of their initial morphological structure.

* The proposed universal damage mechanism for all these prostheses is an abrasion mechanism (according to the debris mill mechanism), which releases debris on micrometric or even submicrometer scales.

* The debris mill erodes peaks and valleys for PV structures, pore walls for OC structures, and pore "cap" for SOC structures. A 2-D model has been proposed
to qualify and quantify it, which will have to be extended in 3-D and with a
refinement of the physical constants.

Once the *in vivo* mechanical, biological and chemical context of the implant is further
defined, tribological tests, which will reproduce this context as accurately as possible,
will enable the characterization of granulometry and the tribological elementary
mechanisms responsible for erosion.
## 6.1 GENERAL CONCLUSIONS

The biocompatibility of breast implant textures is a highly topical subject, which is becoming more and more critical, both in the media and medically. We have shown that surface measurements and nomenclatures of breast implant surfaces are numerous in the literature. However, a lack of scientific rigor plagued most of these studies.

This thesis is located at the forefront of these studies. Indeed, a first justification of the methodology of breast implant surface measurement and a first biologically and topographically relevant classification of the topographies of these implants were published within the framework of this thesis. Moreover, a first analysis of the states of implant surfaces post implantation will be submitted for publication soon. These different results have all been obtained through a statistical approach, which is based on the multiscale analysis of surface topographies.

A summary of the main findings of these 3 different studies is presented below.

One of our first conclusions is that it is necessary to use two measuring instruments to encompass the range of the topographies of implant surfaces. This simple fact nullifies the ISO 14607 standard that proposes to use only one instrument among 4 (SEM with a 2D and 3D surface reconstruction software, White-Light Interferometry, Laser Confocal Microscopy, High Range AFM. In addition to the instruments, the standard is metrologically incorrect at the sampling level: an AFM measurement over 2mm² is simply out of the scope of the apparatus.

Once the criticism of the standard was topographically argued, we were able to propose a new methodology for measuring breast implant surfaces from a comparative multi-instrumental study. Taking into account the area imposed by the standard (2mm²), the interferometry is perfectly adapted to the surface structures obtained by sandblasting. It can also be used for the measurement of smooth
implants, provided to reduce the area to 1mm². Moreover, X-ray micro-tomography and Focus variation microscope completely measure the structures obtained by the "salt-loss" process, contrary to interferometry. Indeed, the latter would provide only very local measures for this type of texture. In addition, although artefacts have been identified, micro-tomography and Focus variation microscope are able to measure on these surfaces the 3 roughness regimes, which were highlighted by the multi-scale analysis of interferometric measurements. Micro-tomography was finally chosen on the following studies for the structures obtained by the "salt-loss" process because we thought that an X-ray measurement would allow us not to depend on the light-related problems inherent to optical measurements on a translucent material (such as the silicone).

Once a metrologically-validated measurement protocol was justified for each of the different structures, the used measurement, visualization and analysis techniques allowed us to identify complex 3D structures. Thanks to the instruments we used which are able to provide a view of the topography and a view of the section, these structures were entirely characterized for the first time. It was then possible to propose a classification based on these morphological characteristics (i.e. PV, OC and SOC).

It is then important to consider whether the fibroblasts and the surrounding biological tissues are sensitive to these characteristics. From a relevant selection of genes involved in both remodeling of extracellular matrix and inflammatory reactions and by an original retro-transcriptomic analysis of peri-prosthetic capsular tissues harvested from asymptomatic patients, the biological relevance of the classification proposed has been validated. This is the first classification based on an ex vivo study of healthy human tissues. It was then shown that the different categories of the proposed classification induce a specific pattern of expression of these genes. Basically, these molecular mechanisms are modulated by the topography of the implant. At the tissue level, differences in structure and organization were also observed on histological sections of peri-prosthetic capsule.

It then seemed important to us to define the classification topographically and quantitatively (otherwise than by morphological terms). An in-depth statistical analysis
was implemented for the first time to demonstrate the robustness of the selected
couple of parameters. Moreover, since the classification is based on a simple pair of
two parameters, the topographical meaning of the classification is easily extricable.
Finally, since we correlated the tissue response to the surface topography of the
prosthesis before implantation, it seemed interesting to us to confirm whether the
tissues remain in contact with the same state of surface of the prosthesis, once
implanted. It was therefore necessary to characterize the *in vivo* integrity of the
different structures. This first topographical study on explanted prostheses allowed us
to note the absence of major structural changes over the explant surface. More
precisely, by multi-scale analysis, a different wear regime was identified for each
category of the classification. Thus, PV-patterned explants exhibited multi-scale peak
erosion, while OC and SOC-patterned explants were characterized at small scales
(inferior to 30µm) by the formation of micro-roughness and at large scales (superior to
200µm) by a "heterogenous stress relaxation". Between these two phenomena, an
intermediate regime corresponding to wear of the wall of the OC structures and to an
opening and wear of the "cap" of the SOC structures.

6.2 OUTLOOK

While some regulatory authorities have banned certain types of textures and others
have recommended surgeons not to implant some of them, the assessment of the
clinical impact of textures and a putative link between BIA-ALCL and certain textures
has not still been resolved. It is then necessary to analyze the possible further work,
which will allow us to conclude on this link.

Firstly, the link must be considered statistically. However, the awareness of the
authorities and clinicians of the pathology has been recent and the specific registers
are not sufficiently mature to allow the published or reported data to be used for
statistical analysis. Indeed, they are often fragmented.

In order to enhance these studies, it would be necessary to be able to analyze all the
collected data. The only way to recover this data is to collaborate with public bodies
promoting these major national registers. Appropriate statistical models (used in
biostatistics, for example) will allow us, even in the presence of incomplete data, to
demonstrate a potential correlation between certain types of texture (or even certain
topographical parameters) and the incidence of BIA-ALCL.

For example, studies on capsular contractures are more completed than those on BIA-ALCL. Based on the risks calculated by the Kaplan-Meier methodology, a comparison between different textures is then possible. These data were collected and graphically summarized in a publication. (184)

Secondly, regarding the causal link, different hypotheses have been mentioned in the literature. We have shown that the biological reaction in contact with SOC-patterned implants is very different from those in contact with OC- or PV-patterned implants. However, it is too early to conclude on a higher clinical risk of these structures because the mechanism upstream of inflammation has not been elucidated. Many authors reported the biofilm hypothesis. In order to validate or refute a such hypothesis, an ex vivo study of the microbiome at the surface of the explant and within the capsular tissues will have to be conducted over a wide range of surface textures and completed by a correlation study between the microbiology data and the topographical data. In a much more sporadic way, some authors mentioned among others the hypothesis of debris. As mentioned previously, in the state of the biomechanical knowledge of the soft tissues, it is pretentious to want to reproduce in a tribological test the wear conditions of the surfaces. The potential release of debris by the surface can be verified only by physico-chemical analyzes of capsular tissue sections. This study should be conducted on a large number of new generation implants, for which gel bleed through the shell is very limited.

These two studies will be all the more valuable, as a partnership with an expert center
in pathology (such as the French National Cancer Institute or the LYMPHOPATH
network) is set up, because once the protocols have been amended and approved,
the analyzes could be performed on capsule samples harvested from patients with
BIA-ALCL.
APPENDIX 1: Comparative bibliometric study on Hip and Breast
Implants

<figure><img src="image_73.png" /><figcaption>Figure S1: Number of publications related to "breast implant" (or "prosthesis") and to" hip implant" (or "prosthesis") from 1998 to 2018 according to a bibliometric analysis on the Web Of Science.</figcaption></figure>
# APPENDIX 2: Multi-scale skewness analysis for SOC-patterned prostheses

The skewness (Ssk) quantifies the degree of asymmetry of the amplitude distribution curve. A surface exhibiting peaks and valleys will have a Ssk close to 0. A surface presenting mostly valleys will have a Ssk positive. Conversely, a surface with mainly plateaus will have a Ssk negative.

The mathematical formula of the skewness (Ssk) is given below. The general coordinates of points on the topography are (x,y,z). The surface area of the topography is designed by A. Sq corresponds to the root mean square height.

$$Ssk = \frac{\frac{1}{A} \iint_A Z^3(x, y) dx dy}{Sq^3} \quad [2]$$

<figure><img src="image_74.png" /><figcaption>Figure S2: Topographical meaning of the Ssk.</figcaption></figure>

As the skewness (Ssk) is a very sensitive parameter, the analysis has to be led on a comprehensive sampling. The Sebbin shaped implant texture was therefore chosen below to explain the mechanism of pore opening,
On the intra-pore roughness regime, the Ssk is negative. The micro-roughness exhibits a PV structure with valleys slightly deeper than the peaks. The explant, by a creation of a micro-roughness, has a slightly higher Ssk value due to the formation of small peaks during the wear.

On the pore roughness regime, the Ssk becomes positive as the scale increases. The walls are then taken into account in the filtering process, which introduces valleys and therefore makes Ssk positive. However, on this regime, at a same scale, Sskexplant is superior to Sskiplant. At a same scale, the pore opening decreases then the “mass” of roughness amplitude on the extreme amplitude (“cap”), and consequently increases the Ssk.

<figure><img src="image_75.png" /><figcaption>Figure S3: Multi-scale curves of Sskiplant and Sskexplant for Sebbin shaped implant texture (SOC)</figcaption></figure>
REFERENCES

1. Molitor M, Mestak O, Kalinova L, Krajcova A, Mestak J. The history and safety of breast implants. Acta chirurgiae plasticae. 2014;56(1-2):15-9. PubMed PMID: 25484272. Epub 2014/12/09. eng.

2. Peters W. The evolution of breast implants. Canadian Journal of Plastic Surgery. 2002;10(5):223-36.

3. Maxwell GP, Gabriel A. The evolution of breast implants. Plastic and reconstructive surgery. 2014 Jul;134(1 Suppl):12S-7S. PubMed PMID: 25057743. Epub 2014/07/25. eng.

4. Glicenstein J. [History of augmentation mammaplasty]. Annales de chirurgie plastique et esthetique. 2005 Oct;50(5):337-49. PubMed PMID: 16185804. Epub 2005/09/28. Histoire de l'augmentation mammaire. fre.

5. Berry MG, Davies DM. Breast augmentation: Part I--A review of the silicone prosthesis. Journal of plastic, reconstructive & aesthetic surgery : JPRAS. 2010 Nov;63(11):1761-8. PubMed PMID: 19713165. Epub 2009/08/29. eng.

6. Freeman BS. Successful treatment of some fibrous envelope contractures around breast implants. Plastic and reconstructive surgery. 1972 Aug;50(2):107-13. PubMed PMID: 5045376. Epub 1972/08/01. eng.

7. Barr S, Bayat A. Breast implant surface development: perspectives on development and manufacture. Aesthetic surgery journal. 2011 Jan;31(1):56-67. PubMed PMID: 21239673. Epub 2011/01/18. eng.

8. Gasperoni C, Salgarello M, Gargani G. Polyurethane-covered mammary implants: a 12-year experience. Annals of plastic surgery. 1992 Oct;29(4):303-8. PubMed PMID: 1466525. Epub 1992/10/01. eng.

9. Vazquez G. A ten-year experience using polyurethane-covered breast implants. Aesthetic plastic surgery. 1999 May-Jun;23(3):189-96. PubMed PMID: 10384018. Epub 1999/06/29. eng.

10. Brody GS, Conway DP, Deapen DM, Fisher JC, Hochberg MC, LeRoy EC, et al. Consensus statement on the relationship of breast implants to connective-tissue disorders. Plastic and reconstructive surgery. 1992;90(6):1102-5.

11. Gabriel SE, O'Fallon WM, Kurland LT, Beard CM, Woods JE, Melton LJ. Risk of connective-tissue diseases and other disorders after breast implantation. New England Journal of Medicine. 1994;330(24):1697-702.

12. Blackburn WD, Everson MP. Silicone-associated rheumatic disease: an unsupported myth. Plastic and reconstructive surgery. 1997;99(5):1362-7.

13. Ferguson JH. Silicone breast implants and neurologic disorders: report of the Practice Committee of the American Academy of Neurology. Neurology. 1997;48(6):1504-7.

14. Grigg M, Bondurant S, Ernster VL, Herdman R. Information for women about the safety of silicone breast implants: National Academies Press; 2000.

15. Deva AK, Cuss A, Magnusson M, Cooter R. The "Game of Implants": A Perspective on the Crisis-Prone History of Breast Implants. Aesthetic surgery journal. 2019;39(Supplement_1):S55-S65.

16. Spear SL, Parikh PM, Goldstein JA. History of breast implants and the food and drug administration. Clinics in plastic surgery. 2009 Jan;36(1):15-21, v. PubMed PMID: 19055957. Epub 2008/12/06. eng.

17. Batich C, Williams J, King R. Toxic hydrolysis product from a biodegradable foam implant. J Biomed Mater Res. 1989 Dec;23(A3 Suppl):311-9. PubMed PMID: 2613741. Epub 1989/12/01. eng.

18. Batiukov D, Podgaiski V, Ladutko D. Removal of Polyurethane Implants. Aesthetic plastic surgery. 2019 Feb;43(1):70-5. PubMed PMID: 30311035. Epub 2018/10/13. eng.

19. Hester TR, Jr., Ford NF, Gale PJ, Hammett JL, Raymond R, Turnbull D, et al. Measurement of 2,4-toluenediamine in urine and serum samples from women with Meme or Replicon breast
implants. Plastic and reconstructive surgery. 1997 Oct;100(5):1291-8. PubMed PMID: 9326795.
Epub 1997/11/05. eng.

20. Stevens WG, Nahabedian MY, Calobrace MB, Harrington JL, Capizzi PJ, Cohen R, et al. Risk factor analysis for capsular contracture: a 5-year Sientra study analysis using round, smooth, and textured implants for breast augmentation. Plastic and reconstructive surgery. 2013 Nov;132(5):1115-23. PubMed PMID: 24056498. Epub 2013/09/24. eng.

21. Gore SM, Lamberty BG. PERTHESE implant-identical cohesive-gel sizers in breast augmentation: a prospective report on 200 consecutive cases and implications for treatment of breast asymmetry. Aesthetic surgery journal. 2012 Mar;32(3):310-8. PubMed PMID: 22301618. Epub 2012/02/04. eng.

22. Radovan C. Breast reconstruction after mastectomy using the temporary expander. Plastic and reconstructive surgery. 1982 Feb;69(2):195-208. PubMed PMID: 7054790. Epub 1982/02/01. eng.

23. Radovan C, editor Reconstruction of the breast after radical mastectomy using a temporary expander. Plast Surg Forum; 1979.

24. Fabre G, Gangloff D, Fabie-Boulard A, Grolleau JL, Chavoin JP. [Breast reconstruction after prolonged tissue expansion. About 247 cases]. Annales de chirurgie plastique et esthetique. 2006 Feb;51(1):29-37. PubMed PMID: 16338234. Epub 2005/12/13. Reconstruction mammaire prothétique apres expansion prealable prolongee. A propos de 247 cas. fre.

25. Huttin-Marquelet C, Huttin C. Reconstruction mammaire par prothèse d'expansion temporaire. Annales de Chirurgie Plastique Esthétique. 2018 2018/11/01:/63(5):545-58.

26. Spiegelhalter D, Knight S, Sant T. Breast implants: The scandal, the outcry, and assessing the risks. Significance. 2012;9(6):17-21.

27. Cohen D. Breast implants linked to rare cancer are removed from European market. BMJ (Clinical research ed). 2018 Dec 20;363:k5401. PubMed PMID: 30573474. Epub 2018/12/24. eng.

28. McCarthy CM, Loyo-Berrios N, Qureshi AA, Mullen E, Gordillo G, Pusic AL, et al. Patient Registry and Outcomes for Breast Implants and Anaplastic Large Cell Lymphoma Etiology and Epidemiology (PROFILE): Initial Report of Findings, 2012-2018. Plastic and reconstructive surgery. 2019;143(3S):65S-73S.

29. Altekruse S. SEER cancer statistics review, 1975-2007. http://seer.cancer.gov/csr/1975_2007/results_merged/sect_13_leukemia.pdf. 2009.

30. Mulligan C, Salisbury E, Tschuchnigg M, Moradi P, Matthews A, Warrier S. ALK1-Negative Anaplastic Large Cell Lymphoma of the Breast from a Nonprosthesis Cyst. Plastic and reconstructive surgery Global open. 2014 Oct;2(10):e238. PubMed PMID: 25426355. PubMed Central PMCID: PMC4236383. Epub 2014/11/27. eng.

31. Yoon HJ, Choe JY, Jeon YK. Mucosal CD30-Positive T-Cell Lymphoproliferative Disorder Arising in the Oral Cavity Following Dental Implants: Report of the First Case. International journal of surgical pathology. 2015 Dec;23(8):656-61. PubMed PMID: 26261101. Epub 2015/08/12. eng.

32. Palraj B, Paturi A, Stone RG, Alvarez H, Sebenik M, Perez MT, et al. Soft tissue anaplastic large T-cell lymphoma associated with a metallic orthopedic implant: case report and review of the current literature. The Journal of foot and ankle surgery : official publication of the American College of Foot and Ankle Surgeons. 2010 Nov-Dec;49(6):561-4. PubMed PMID: 20870426. Epub 2010/09/28. eng.

33. Ozkaya N, Grogg KL, Dogan A. Seroma-associated anaplastic large-cell lymphoma arising on the background of subcutaneous calcinosis: beyond breast implants. Histopathology. 2016 Nov;69(5):890-2. PubMed PMID: 27271483. Epub 2016/06/09. eng.

34. Keech JA, Jr., Creech BJ. Anaplastic T-cell lymphoma in proximity to a saline-filled breast implant. Plastic and reconstructive surgery. 1997 Aug;100(2):554-5. PubMed PMID: 9252643. Epub 1997/08/01. eng.

35. Brody GS, Deapen D, Taylor CR, Pinter-Brown L, House-Lightner SR, Andersen JS, et al. Anaplastic large cell lymphoma occurring in women with breast implants: analysis of 173 cases. Plastic and reconstructive surgery. 2015 Mar;135(3):695-705. PubMed PMID: 25490535. Epub 2014/12/10. eng.
36. Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016 May 19;127(20):2375-90. PubMed PMID: 26980727. Pubmed Central PMCID: PMC4874220. Epub 2016/03/17. eng.

37. Jacobsen E. Anaplastic large-cell lymphoma, T-/null-cell type. The oncologist. 2006 Jul-Aug;11(7):831-40. PubMed PMID: 16880242. Epub 2006/08/02. eng.

38. Clemens MW, Medeiros LJ, Butler CE, Hunt KK, Fanale MA, Horwitz S, et al. Complete Surgical Excision Is Essential for the Management of Patients With Breast Implant-Associated Anaplastic Large-Cell Lymphoma. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2016 Jan 10;34(2):160-8. PubMed PMID: 26628470. Pubmed Central PMCID: PMC4872006 online at www.jco.org. Author contributions are found at the end of this article. Epub 2015/12/03. eng.

39. Gholam D, Bibeau F, El Weshi A, Bosq J, Ribrag V. Primary breast lymphoma. Leukemia & lymphoma. 2003 Jul;44(7):1173-8. PubMed PMID: 12916870. Epub 2003/08/15. eng.

40. Kim B, Roth C, Chung KC, Young VL, van Busum K, Schnyer C, et al. Anaplastic large cell lymphoma and breast implants: a systematic review. Plastic and reconstructive surgery. 2011 Jun;127(6):2141-50. PubMed PMID: 21358562. Epub 2011/03/02. eng.

41. Clemens MW, Miranda RN. Coming of Age: Breast Implant-Associated Anaplastic Large Cell Lymphoma After 18 Years of Investigation. Clinics in plastic surgery. 2015 Oct;42(4):605-13. PubMed PMID: 26408447. Epub 2015/09/27. eng.

42. Oishi N, Brody GS, Ketterling RP, Viswanatha DS, He R, Dasari S, et al. Genetic subtyping of breast implant-associated anaplastic large cell lymphoma. Blood. 2018 Aug 2;132(5):544-7. PubMed PMID: 29921615. Pubmed Central PMCID: PMC6073323. Epub 2018/06/21. eng.

43. Quesada AE, Medeiros LJ, Clemens MW, Ferrufino-Schmidt MC, Pina-Oviedo S, Miranda RN. Breast implant-associated anaplastic large cell lymphoma: a review. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. 2019 Feb;32(2):166-88. PubMed PMID: 30206414. Epub 2018/09/13. eng.

44. Miranda RN, Aladily TN, Prince HM, Kanagal-Shamanna R, de Jong D, Fayad LE, et al. Breast implant-associated anaplastic large-cell lymphoma: long-term follow-up of 60 patients. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2014 Jan 10;32(2):114-20. PubMed PMID: 24323027. Pubmed Central PMCID: PMC4062709. Epub 2013/12/11. eng.

45. Ye X, Shokrollahi K, Rozen WM, Conyers R, Wright P, Kenner L, et al. Anaplastic large cell lymphoma (ALCL) and breast implants: Breaking down the evidence. Mutation Research/Reviews in Mutation Research. 2014 10//;762:123-32.

46. Clemens MW. Discussion: Breast Implant-Associated Anaplastic Large Cell Lymphoma in Australia and New Zealand: High-Surface-Area Textured Implants Are Associated with Increased Risk. Plastic and reconstructive surgery. 2017 Oct;140(4):660-2. PubMed PMID: 28953714. Epub 2017/09/28. eng.

47. Kadin ME, Deva A, Xu H, Morgan J, Khare P, MacLeod RA, et al. Biomarkers Provide Clues to Early Events in the Pathogenesis of Breast Implant-Associated Anaplastic Large Cell Lymphoma. Aesthetic surgery journal. 2016 Jul;36(7):773-81. PubMed PMID: 26979456. Epub 2016/03/17. eng.

48. Blombery P, Thompson ER, Jones K, Arnau GM, Lade S, Markham JF, et al. Whole exome sequencing reveals activating JAK1 and STAT3 mutations in breast implant-associated anaplastic large cell lymphoma. Haematologica. 2016 Sep;101(9):e387-90. PubMed PMID: 27198716. Pubmed Central PMCID: PMC5060038. Epub 2016/05/21. eng.

49. Di Napoli A, Jain P, Duranti E, Margolskee E, Arancio W, Facchetti F, et al. Targeted next generation sequencing of breast implant-associated anaplastic large cell lymphoma reveals mutations in JAK/STAT signalling pathway genes, TP53 and DNMT3A. British journal of haematology. 2018 Mar;180(5):741-4. PubMed PMID: 27859003. Epub 2016/11/20. eng.

50. Letourneau A, Maerevoet M, Milowich D, Dewind R, Bisig B, Missiaglia E, et al. Dual JAK1 and STAT3 mutations in a breast implant-associated anaplastic large cell lymphoma. Virchows
Archiv : an international journal of pathology. 2018 Oct;473(4):505-11. PubMed PMID: 29637270.
Epub 2018/04/11. eng.

51. Adrada BE, Miranda RN, Rauch GM, Arribas E, Kanagal-Shamanna R, Clemens MW, et al. Breast implant-associated anaplastic large cell lymphoma: sensitivity, specificity, and findings of imaging studies in 44 patients. Breast cancer research and treatment. 2014 Aug;147(1):1-14. PubMed PMID: 25073777. Epub 2014/07/31. eng.

52. Clemens MW, Horwitz SM. NCCN Consensus Guidelines for the Diagnosis and Management of Breast Implant-Associated Anaplastic Large Cell Lymphoma. Aesthetic surgery journal. 2017 Mar 1;37(3):285-9. PubMed PMID: 28184418. Epub 2017/02/12. eng.

53. Fleming D, Stone J, Tansley P. Spontaneous Regression and Resolution of Breast Implant-Associated Anaplastic Large Cell Lymphoma: Implications for Research, Diagnosis and Clinical Management. Aesthetic plastic surgery. 2018 Jun;42(3):672-8. PubMed PMID: 29445921.
Pubmed Central PMCID: PMC5945759. Epub 2018/02/16. eng.

54. Adams Jr WP. Discussion: Breast Implant-Associated Anaplastic Large Cell Lymphoma in Australia and New Zealand High-Surface-Area Textured Implants Are Associated with Increased Risk. Plastic and reconstructive surgery. 2017;140(4):663-5.

55. de Jong D, Vasmel WL, de Boer JP, Verhave G, Barbe E, Casparie MK, et al. Anaplastic large-cell lymphoma in women with breast implants. Jama. 2008 Nov 5;300(17):2030-5. PubMed PMID: 18984890. Epub 2008/11/06. eng.

56. Largent J, Oefelein M, Kaplan HM, Okerson T, Boyle P. Risk of lymphoma in women with breast implants: analysis of clinical studies. European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP). 2012 May;21(3):274-80.
PubMed PMID: 22456426. Epub 2012/03/30. eng.

57. Wang SS, Deapen D, Voutsinas J, Lacey JV, Jr., Lu Y, Ma H, et al. Breast implants and anaplastic large cell lymphomas among females in the California Teachers Study cohort. British journal of haematology. 2016 Aug;174(3):480-3. PubMed PMID: 26456010. Pubmed Central PMCID: PMC4829485. Epub 2015/10/13. eng.

58. Doren EL, Miranda RN, Selber JC, Garvey PB, Liu J, Medeiros LJ, et al. U.S. Epidemiology of Breast Implant-Associated Anaplastic Large Cell Lymphoma. Plastic and reconstructive surgery. 2017;139(5):1042-50. PubMed PMID: 00006534-201705000-00003.

59. Duvic M, Moore D, Menter A, Vonderheid EC. Cutaneous T-cell lymphoma in association with silicone breast implants. Journal of the American Academy of Dermatology. 1995 Jun;32(6):939-42. PubMed PMID: 7751462. Epub 1995/06/01. eng.

60. Hu H, Jacombs A, Vickery K, Merten SL, Pennington DG, Deva AK. Chronic Biofilm Infection in Breast Implants Is Associated with an Increased T-Cell Lymphocytic Infiltrate: Implications for Breast Implant-Associated Lymphoma. Plastic and reconstructive surgery. 2015;135(2):319-29. PubMed PMID: 00006534-201502000-00001.

61. Laurent C, Delas A, Gaulard P, Haioun C, Moreau A, Xerri L, et al. Breast implant-associated anaplastic large cell lymphoma: two distinct clinicopathological variants with different outcomes. Annals of oncology : official journal of the European Society for Medical Oncology. 2016 Feb;27(2):306-14. PubMed PMID: 26598546. Pubmed Central PMCID: PMC4722894. Epub 2015/11/26. eng.

62. Chai SM, Kavangh S, Ooi SS, Sterrett GF, Cull G, Plunkett M, et al. Anaplastic large-cell lymphoma associated with breast implants: a unique entity within the spectrum of peri-implant effusions. Diagnostic cytopathology. 2014 Nov;42(11):929-38. PubMed PMID: 24687769. Epub 2014/04/02. eng.

63. Clemens MW, Clarke C, Fanale M, Miranda R, Madeiros J. Breast implant-associated anaplastic large t-cell lymphoma: Institutional experience and review of the literature. Journal of Plastic, Reconstructive & Aesthetic Surgery. 2014;67(10):1463.

64. Hu H, Johani K, Almatroudi A, Vickery K, Van Natta B, Kadin ME, et al. Bacterial Biofilm Infection Detected in Breast Implant-Associated Anaplastic Large-Cell Lymphoma. Plastic and reconstructive surgery. 2016 Jun;137(6):1659-69. PubMed PMID: 26890506. Epub 2016/02/19. eng.

65. Lista F, Tutino R, Khan A, Ahmad J. Subglandular breast augmentation with textured, anatomic, cohesive silicone implants: a review of 440 consecutive patients. Plastic and
reconstructive surgery. 2013 Aug;132(2):295-303. PubMed PMID: 23584627. Epub 2013/04/16. eng.

66. Kuehlmann B, Prantl L. Breast implants and possible association with ALCL: A retrospective study including a histological analysis of 296 explanted breast tissues and current literature. Clinical hemorheology and microcirculation. 2016 Oct 5;63(4):439-49. PubMed PMID: 27314443. Epub 2016/06/18. eng.

67. de Boer M, van Leeuwen FE, Hauptmann M, et al. Breast implants and the risk of anaplastic large-cell lymphoma in the breast. JAMA Oncology. 2018.

68. Stark B, Mureau M, Hopper I, Cooter R, Deva A, Perks G, et al. Letter on breast implant safety and the role of breast implant registries sent to the FDA in preparation of their public hearing in 2019-2019.

69. Collett DJ, Rakhorst H, Lennox P, Magnusson M, Cooter R, Deva AK. Current Risk Estimate of Breast Implant-Associated Anaplastic Large Cell Lymphoma in Textured Breast Implants. Plastic and reconstructive surgery, 2019;143(3S):30S-40S.

70. Rastogi P, Riordan E, Moon D, Deva AK. Theories of Etiopathogenesis of Breast Implant-Associated Anaplastic Large Cell Lymphoma. Plastic and reconstructive surgery. 2019;143(3S):23S-9S.

71. Cooter R, Barnett R, Deva A, Magnusson MR, McNeil J, Perks G, et al. In Defense of the International Collaboration of Breast Registry Activities (ICOBRA). Aesthetic surgery journal. 2016 Jul;36(7):NP225-7. PubMed PMID: 27053073. PubMed Central PMCID: PMC4911904. Epub 2016/04/08. eng.

72. McLaughlin JK, Lipworth L, Fryzek JP, Ye W, Tarone RE, Nyren O. Long-term cancer risk among Swedish women with cosmetic breast implants: an update of a nationwide study. Journal of the National Cancer Institute. 2006 Apr 19;98(8):557-60. PubMed PMID: 16622125. Epub 2006/04/20. eng.

73. Pukkala E, Boice JD, Hovi S-L, Hemminki E, Asko-Seljavaara S, Keskimaki I, et al. Incidence of breast and other cancers among Finnish women with cosmetic breast implants, 1970-1999. Journal of long-term effects of medical implants. 2002;12(4).

74. Formes A, Diehl B. Investigation of the silicone structure in breast implants using (1)H NMR. Journal of pharmaceutical and biomedical analysis. 2014 May;93:95-101. PubMed PMID: 24342752. Epub 2013/12/18. eng.

75. Moyer HR, Ghazi BH, Losken A. The effect of silicone gel bleed on capsular contracture: a generational study. Plastic and reconstructive surgery. 2012 Oct;130(4):793-800. PubMed PMID: 22691845. Epub 2012/06/14. eng.

76. O'Shaughnessy K. Evolution and update on current devices for prosthetic breast reconstruction. Gland Surg. 2015 Apr;4(2):97-110. PubMed PMID: 26005642. PubMed Central PMCID: PMC4409673. Epub 2015/05/26. eng.

77. Sitbon E. Fabrication des prothèses mammaires : une industrie de haute technologie. Annales de Chirurgie Plastique Esthétique. 2005 2005/10/01/;50(5):394-407.

78. Standardization BECf. ISO 14607 Non-active surgical implants—mammary implants—particular requirements (Bruxelles European Committee for Standardization). 2009.

79. Atlan M, Bigerelle M, Larreta-garde V, Hindie M, Heden P. Characterization of Breast Implant Surfaces, Shapes, and Biomechanics: A Comparison of High Cohesive Anatomically Shaped Textured Silicone, Breast Implants from Three Different Manufacturers. Aesthetic plastic surgery. 2016 Feb;40(1):89-97. PubMed PMID: 26746882. Epub 2016/01/10. eng.

80. Garabédian C, Delille R, Deltombe R, Anselme K, Atlan M, Bigerelle M. A multi-topographical-instrument analysis: the breast implant texture measurement. Surface Topography: Metrology and Properties. 2017;5(2):025004.

81. Chang EI, Hammond DC. Clinical Results on Innovation in Breast Implant Design. Plastic and reconstructive surgery. 2018 Oct;142(4S The Science of Breast Implants):31S-8S. PubMed PMID: 30252757. Epub 2018/09/27. eng.

82. O'Neill AC, Zhong T, Hofer SOP. Implications of Breast Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL) for Breast Cancer Reconstruction: An Update for Surgical
Oncologists. Annals of surgical oncology. 2017 Oct;24(11):3174-9. PubMed PMID: 28762113. Epub 2017/08/02. eng.

83. Calobrace MB, Schwartz MR, Zeidler KR, Pittman TA, Cohen R, Stevens WG. Long-Term Safety of Textured and Smooth Breast Implants. Aesthetic surgery journal. 2017 Sep 28. PubMed PMID: 29040370. Epub 2017/10/19. eng.

84. Loch-Wilkinson A, Beath K, Knight RJW, Wessels WLF, Magnusson M, Papadopoulos T, et al. Breast implant associated Anaplastic Large Cell Lymphoma in Australia and New Zealand - high surface area textured implants are associated with increased risk. Plastic and reconstructive surgery. 2017 May 05. PubMed PMID: 28481803. Epub 2017/05/10. eng.

85. Jones P, Mempin M, Hu H, Chowdhury D, Foley M, Cooter R, et al. The Functional Influence of Breast Implant Outer Shell Morphology on Bacterial Attachment and Growth. Plastic and reconstructive surgery. 2018 Oct;142(4):837-49. PubMed PMID: 30252806. Epub 2018/09/27. eng.

86. Barr S, Hill EW, Bayat A. Functional biocompatibility testing of silicone breast implants and a novel classification system based on surface roughness. Journal of the mechanical behavior of biomedical materials. 2017 Jun 27;75:75-81. PubMed PMID: 28697402. Epub 2017/07/12. eng.

87. Psillakis JM, Facchina PH, Kharmandayan P, Trillo L, Canzi WC, Aguiar HR. Review of 1,447 breast augmentation patients using PERTHESE silicone implants. Aesthetic plastic surgery. 2010 Feb;34(1):11-5. PubMed PMID: 19760452. PubMed Central PMCID: PMC2812707. Epub 2009/09/18. eng.

88. Danino A, Rocher F, Blanchet-Bardon C, Revol M, Servant JM. Étude au microscope électronique à balayage des surfaces des implants mammaires à texturation poreuse et de leurs capsules. Description de l'effet « velcro » à des prothèses à texturation poreuse. Annales de Chirurgie Plastique Esthétique. 2001 2001/02/01/46(1):23-30.

89. Danino AM, Basmacioglu P, Saito S, Rocher F, Blanchet-Bardon C, Revol M, et al. Comparison of the capsular response to the Biocell RTV and Mentor 1600 Siltex breast implant surface texturing: a scanning electron microscopic study. Plastic and reconstructive surgery. 2001 Dec;108(7):2047-52. PubMed PMID: 11743398. Epub 2001/12/18. eng.

90. Valencia-Lazcano AA, Alonso-Rasgado T, Bayat A. Physico-chemical characteristics of coated silicone textured versus smooth breast implants differentially influence breast-derived fibroblast morphology and behaviour. Journal of the mechanical behavior of biomedical materials. 2014 Dec;40:140-55. PubMed PMID: 25238227. Epub 2014/09/23. eng.

91. James GA, Boegli L, Hancock J, Bowersock L, Parker A, Kinney BM. Bacterial Adhesion and Biofilm Formation on Textured Breast Implant Shell Materials. Aesthetic plastic surgery. 2019 Apr;43(2):490-7. PubMed PMID: 30276456. Epub 2018/10/03. eng.

92. Kyle DJ, Oikonomou A, Hill E, Bayat A. Development and functional evaluation of biomimetic silicone surfaces with hierarchical micro/nano-topographical features demonstrates favourable in vitro foreign body response of breast-derived fibroblasts. Biomaterials. 2015 Jun;52:88-102. PubMed PMID: 25818416. Epub 2015/03/31. eng.

93. Barr S, Hill EW, Bayat A. Development, fabrication and evaluation of a novel biomimetic human breast tissue derived breast implant surface. Acta Biomaterialia. 2017 2//;49:260-71.

94. Atlan M, Nuti G, Wang H, Decker S, Perry T. Breast implant surface texture impacts host tissue response. Journal of the mechanical behavior of biomedical materials. 2018 2018/12/01/;88:377-85.

95. Valencia-Lazcano AA, Alonso-Rasgado T, Bayat A. Characterisation of breast implant surfaces and correlation with fibroblast adhesion. Journal of the mechanical behavior of biomedical materials. 2013 May;21:133-48. PubMed PMID: 23545265. Epub 2013/04/03. eng.

96. Paek LS, Giot J-P, Tétreault-Paquin J-O, St-Jacques S, Nelea M, Danino MA. The Impact of Postoperative Expansion Initiation Timing on Breast Expander Capsular Characteristics: A Prospective Combined Clinical and Scanning Electron Microscopy Study. Plastic and reconstructive surgery. 2015;135(4):967-74. PubMed PMID: 00006534-201504000-00005.

97. Barr S, Hill E, Bayat A. Current implant surface technology: an examination of their nanostructure and their influence on fibroblast alignment and biocompatibility. Eplasty. 2009