# 3D multiparametric ultrasound of spontaneous murine tumors for non-invasive tumor characterization

Jean-Baptiste Guillaumin, Aymeric Nadjem, Léa Vigouroux, Ana Sibleyras, Mickaël Tanter, Jean-François Aubry, Béatrice Berthon

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Jean-Baptiste Guillaumin, Aymeric Nadjem, Léa Vigouroux, Ana Sibleyras, Mickaël Tanter, et al. 3D multiparametric ultrasound of spontaneous murine tumors for non-invasive tumor characterization. Physics in Medicine and Biology, 2025, 70 (9), pp.095006. 10.1088/1361-6560/adc8f4. hal-05272590

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PAPER

3D multiparametric ultrasound of spontaneous murine tumors for non-invasive tumor characterization

Q1

Jean-Baptiste Guillaumin ID, Aymeric Nadjem ID, Léa Vigouroux ID, Ana Sibleyras, Mickaël Tanter, Jean-François Aubry ID and Béatrice Berthon* ID

Q2

Physics for Medicine Paris Institute, ESPCI Paris, PSL Research University, Inserm U1273, CNRS UMR 8063, Paris, France
* Author to whom any correspondence should be addressed.

E-mail: beatrice.walker@espci.fr

Keywords: multiparametric imaging, 3D ultrafast ultrasound, breast cancer

Abstract

**Objective.** Non-invasive tumor diagnosis and characterization is limited today by the cost and availability of state of the art imaging techniques. Thanks to recent developments, ultrasound (US) imaging can now provide quantitative volumetric maps of different tissue characteristics. This study applied the first fully concurrent 3D ultrasound imaging set-up including B-mode imaging, shear wave elastography (SWE), tissue structure imaging with backscatter tensor imaging (BTI), vascular mapping with ultrasensitive Doppler (uDoppler) and ultrasound localization microscopy (ULM) *in-vivo*. Subsequent analysis aimed to evaluate its benefits for non-invasive tumor diagnosis. **Approach.** A total of 26 PyMT-MMTV transgenic mice and 6 control mice were imaged weekly during tumor growth. First-order statistics and radiomic features were extracted from the quantitative maps obtained, and used to build predictive models differentiating healthy from cancerous mammary pads. Imaging features were also compared to histology obtained the last week of imaging. **Main results.** High quality co-registered quantitative maps were obtained, for which SWE speed, BTI tissue organization, ULM blood vessel count and uDoppler blood vessel density were correlated with histopathology. Significant changes in uDoppler sensitivity and BTI tissue structure were measured during tumor evolution. Predictive models inferring the cancerous state from the multiparametric imaging reached 99% accuracy, and focused mainly on radiomics measures of the BTI maps. **Significance.** This work indicates the relevance of a multiparametric characterization of lesions, and highlights the strong predictive power of BTI-derived parameters for differentiating tumors from healthy tissue, both before and after the tumor can be detected by palpation.

Q3

1. Introduction

Tumor development is a complex and heterogeneous process, involving a variety of physiological mechanisms. One of them is angiogenesis, the generation of a new vascular structure by the tumor to support its constant need for energy (Nagy *et al* 2009). Tumor vessels are often highly irregular in diameter, length and geometry (Nagy *et al* 2010). Tumors have also been shown to modify the structure of surrounding tissues by recruiting collagen fibers (Kaushik *et al* 2016), especially in the process of invading their microenvironment. This has consequences also on tissue elasticity, as it is well known that tumors and their surrounding tissues tend to feel more rigid at palpation than healthy tissue. Finally, tumor growth increases intratumor heterogeneity, in particular with the formation of necrotic cores (Heppner 1984). Measuring these parameters could help characterize the tumor tissue compared healthy tissue, but also stratify tumors into stages or subtypes, susceptible to respond differently to a given therapy.

The physiological parameters described above evolve during tumor growth, and can be affected by therapeutic treatments. For example, antiangiogenic therapies specifically target the irregular vascular trees created by the tumors to starve it (Kerbel 2008, Fox *et al* 2007). It is therefore crucial to be able to measure
and monitor these different tumor characteristics during tumor growth as well as during treatment course. This needs to be done non-invasively to be feasible on a regular basis.

Current state-of-the art imaging techniques in oncology remain difficult to apply repeatedly. Magnetic resonance imaging (MRI) is too costly to be applied in a longitudinal way, and is not applicable to patients with cardiac implantable electronic devices or metallic implants. Although it offers spatial resolution of the order of 1 mm and a range of different associated techniques, MRI temporal resolution remains limited (more than a second), and studies have shown that it lacks specificity for breast cancer (Radhakrishna et al 2018). Mammography, positron emission tomography (PET) and computerized tomography (CT) are ionizing imaging techniques, which are therefore not recommended for repeated use. PET is a highly sensitive technique for imaging the tumor burden, and is essential in identifying primary cancer location in the case of metastatic disease, but its spatial resolution is too low to inform on the tumor boundaries and close micro-environment. Mammography and CT, although providing higher spatial resolution than conventional ultrasound (of the order of 10 µm and 100 µm respectively), are limited to morphological and anatomical analysis of the tumor. Mammography remains the state-of-the art screening and diagnostic tool for breast cancer in Europe because of its high sensitivity and detection rate compared to other modalities. However, ultrasound imaging (US) is used routinely in parts of the world such as Asia, where its sensitivity is higher than mammography because of different breast density. US imaging has the advantage of being fully non-invasive, non-ionizing, portable and cost-effective. It has been shown to reach similar sensitivity as mammography in detecting breast cancer, but to lack specificity (Berg et al 2016). Its routine use in the clinic is limited today to 2D evaluation of the tumor size, echogenicity and morphology with anatomical B-mode imaging, generally in a qualitative way. The two dimensional aspect of US imaging has long represented one of its main limitations, as it leads to high operator dependence, but is now being overcome with 3D US imaging techniques (Guillaumin et al 2022, Provost et al 2014). Thanks to other recent developments, this technology can now also inform on a variety of tumor aspects. With the advent of ultrafast ultrasound (Tanter and Fink 2014), the temporal resolution of ultrasound imaging has largely increased to reach imaging rates of several kHz, bringing the resolution down to the millisecond range. This high frame rate imaging has opened the way to new techniques which image rapidly occurring phenomena that cannot be captured with other modalities, to provide quantitative information about the tissues. For example, tissue elasticity, which is largely modified during tumor growth due to the interaction with the extracellular matrix, can be measured with shear wave elastography (SWE) (Gennisson et al 2015), mapping the Young's modulus in an entire volume. Ultrasensitive power Doppler imaging (uDoppler) (Dizeux et al 2017) can map the tumor vasculature with high sensitivity in 3D, allowing measurements of blood volume and vessel diameter with a spatial resolution of 100 µm. This provides information highly relevant to evaluating how vascularized a tumor is, and its degree of angiogenesis. Fine vasculature can also be captured in 2D using a recently developed technique called ultrafast localization microscopy (ULM) (Errico et al 2015), providing access to measurements of the blood flow velocity profiles and vessel diameters with a precision of a few micrometers. 2D ULM cannot be used to measure vessel morphology and distribution inside the tumor volume, but quantifies local vascularization with a resolution much closer to microscopy than uDoppler, by leveraging the ultrafast frame rates. Finally, tissue organization, and in particular the disposition of collagen fibers in and around the tumor, can be measured with backscatter tensor imaging (BTI) (Papadacci et al 2014). These different techniques are all relevant to quantifying specific and complementary tumor aspects. US imaging with a combination of these different techniques could therefore be of interest for characterizing tumors. Yet, such a multiparametric US imaging framework does not exist in the clinic today. Although B-mode imaging and SWE are used in clinical routine for breast cancer, uDoppler imaging is not. BTI and ULM are recent techniques which have not yet made it to the clinic.

Multiparametric US imaging approaches have already been applied in several research studies, demonstrating the complementarity of some of the US techniques described above. A small number of studies have specifically investigated the joint use of B-mode and SWE or strain imaging with vascular imaging (uDoppler or contrast-enhanced imaging) (Kapetas et al 2019, Liu et al 2019, Wildeboer et al 2020). The results of each of these studies indicated increased classification accuracy (between malignant and benign lesions) when combining the parameters rather than using single modalities. Similar results were obtained for predicting therapy response (Dobruch-Sobczak et al 2021). In the clinic, the complementarity of elasticity and anatomical imaging has already been demonstrated in breast cancer, showing that the addition of SWE to B-mode imaging increased the specificity of diagnosis (Berg et al 2012, Athanasiou et al 2010). These studies show that the different imaging techniques should be acquired concurrently so as to leverage the relationship between them as the tumor evolves. This requires being capable of acquiring them in a fully co-registered way. However, even in the field of research, B-mode, SWE, BTI and uDoppler have not yet been used concurrently for tumor imaging. This work applies for the first time a fully non-invasive multiparametric imaging protocol combining these different US techniques in a fully co-registered way. It
builds on previously published work combining some of these techniques, but without any vascular imaging, for the characterization of human breast masses (Guillaumin *et al* 2024).

In addition to concurrent imaging, evaluating the tissue rigidity, vascularization and structure requires volumetric imaging, as these parameters are inherently tri-dimensional. As an example, vessel tortuosity is often used as a marker of tumor angiogenesis, and can only be evaluated accurately in a volume. Clinical B-mode and SWE are generally 2D techniques, which have the added disadvantage of requiring operator expertise to identify and select the most relevant tumor views, and lead to large operator-dependence. In this work, we therefore propose a volumetric imaging framework to overcome these limitations.

Finally, tumors exhibit spatial heterogeneity, which tends to increase with tumor growth. The assessment of spatial texture of tissue anatomy, elasticity, fiber structure and vascularization could therefore be of interest for tumor monitoring. Clinical tumor characterization with US imaging currently still relies only on morphological aspects or average B-mode and SWE values. Quantitative B-mode analysis studies have been led to overcome this, and to leverage the complexity of information provided by the anatomical imaging. Several studies have used handcrafted features, including tumor morphology and texture to build a model predictive of malignancy (Antropova *et al* 2017, Wu *et al* 2021) or response to treatment (Yang *et al* 2014, Sadeghi-Naini *et al* 2017). Only a few studies have investigated more advanced SWE analysis than maximum or average values measured within 2D views of the tumor (Tian *et al* 2017, Farrokh *et al* 2019). In this work, we go one step further by fully exploiting 3D heterogeneity measurements, including radiomics features of tissue texture (Lambin *et al* 2012).

The objectives of this paper are to apply a multiparametric volumetric US imaging protocol to tumors *in-vivo*, and to evaluate the usefulness of this approach for tumor characterization, by extracting quantitative parameters describing the tumor and its micro-environment, and comparing those to tumor histology. We specifically combined B-mode imaging, SWE, tissue structure with BTI and vascular mapping with uDoppler and ULM for longitudinal imaging of a transgenic murine model of breast tumors.

## 2. Materials and methods

### 2.1. Animal experiments

#### 2.1.1. Tumor model

All animal experiment protocols were reviewed and approved by the relevant ethics committee (Comité d'éthique en matière d'expérimentation animale Paris Centre et Sud N° 59) under the name «Imagerie multiparamétrique du cancer du sein par ultrasons ultrarapides» and N° 30370 2021031215438668V3. A total of 26 FVB/N female mice were included (The Jackson Laboratory, Bar Harbor, Maine, USA), 20 of which corresponded to the transgenic strand MMTV-PyVT 634Mul/J (PyMT in this paper) and six were wild type animals (WT). The PyMT spontaneously grow mammary tumors (Guy *et al* 1992). Exhibiting many features of human breast cancer development (Lin *et al* 2003). The mice were 5 weeks of age on arrival (Week W1). Throughout the whole experiment, each animal was observed daily in terms of its behavior, aspect and weight, as part of the management of their well-being.

From week W3, the mice were imaged weekly with 3D B-mode, SWE, BTI and uDoppler until week W9 at the latest, and additionally with ULM on the day they were euthanized. From week W4, one mouse was sacrificed every week to sample tumor specimen for histology at different points in their development. The remaining mice were sacrificed at weeks W7, W8 and W9 as indicated on table 1 so as to obtain ULM acquisitions of larger tumors. The WT mice were euthanized at week W8 to keep enough control animals at each time point.

During daily observation, the location of the first tumor felt at palpation was noted, knowing that the tumor burden eventually takes up the whole mammary pad area of the animal. To evaluate both tumor characteristics and early tumor development, 14 of the PyMT mice were imaged at the location of the first palpable tumor as soon as it was observed, while 6 animals were imaged on the same mammary pad every week, and the apparition of a palpable tumor at that location noted.

#### 2.1.2. Animal preparation for imaging

The mouse was placed in a sealed box with 1.5% isoflurane (Isoflurin®, Axience, Pantin, France) mixed with 0.4 l min⁻¹ air and 0.2 l min⁻¹ O₂, until complete anesthesia. It was then positioned onto a heated plate on its back, with its head inside a fitted isoflurane mask. If necessary, hair was removed using a razor or depilatory cream (Netline, CCD, Paris, France) so as to avoid imaging artifacts. The ultrasound probe was placed inside a heated water bath slowly lowered onto the animal's chest, which was covered in ultrasound coupling gel. To monitor the anesthesia and animal's well-being, its respiratory and cardiac frequency were measured respectively via a pressure sensor placed in front of the animal's mouth, and ECG electrodes positioned on its chest. In addition, the animal's temperature was monitored using a rectal probe. The
<table><caption>**Table 1.** Summary of the imaging plan.</caption><thead><tr><td>Week</td><td>Imaging</td><td>Euthanasia</td></tr></thead><tbody><tr><td>W1</td><td>0</td><td>0</td></tr><tr><td>W2</td><td>0</td><td>0</td></tr><tr><td>W3</td><td>6 WT/20 PyMT (6)<sup>a</sup></td><td>0</td></tr><tr><td>W4</td><td>6 WT/20 PyMT (6)</td><td>0</td></tr><tr><td>W5</td><td>6 WT/20 PyMT (6)</td><td>1 PyMT</td></tr><tr><td>W6</td><td>6 WT/20 PyMT (6)</td><td>2 PyMT</td></tr><tr><td>W7</td><td>6 WT/20 PyMT</td><td>7 PyMT (3)</td></tr><tr><td>W8</td><td>6 WT/20 PyMT</td><td>6 WT/8 PyMT (3)</td></tr><tr><td>W9</td><td>2 PyMT</td><td>2 PyMT</td></tr></tbody></table>

Labchart system (ADInstruments, Dunedin, New-Zealand) was used to monitor these parameters and trigger ultrasound emissions during the low motion respiration period of the animal, and a few milliseconds after the R peak of the cardiac cycle. This means that plane-wave emission and reception blocks were acquired only in those moments of the cycles, leading to discontinuous acquisition, but ensuring minimal motion artefacts in the reconstructed data.

## 2.2. Multiparametric acquisition

### 2.2.1. Equipment

A Vantage programmable ultrasound system (Verasonics, Kirkland, USA) was used for all image acquisitions to drive a 15,6 MHz (Vermon, Tour, France) linear probe with 0.11 mm element spacing and an elevation focal distance of 8 mm. The probe was fixed to a set of three linear motors (VT 80, Physik Instrumente, Karlsruhe, Germany) and one rotation motor (DT 80, Physik Instrumente, Karlsruhe, Germany) via a custom 3D printed plastic probe holder, so that it could be moved automatically in vertically, laterally and in rotation along its axis. Real-time B-mode acquisition was used for accurate positioning over the target mammary, previously visually identified. SWE, uDoppler and BTI imaging were then performed sequentially. If this acquisition was not to be the last for this mouse, the isoflurane administration was stopped. The mouse woke up and was left a few minutes on the heated plate to recover, before being placed back in its cage. If this acquisition corresponded to the last one for this animal (as per table 1), ULM imaging was performed with adjusted levels of isoflurane, after which the animal was euthanized while still asleep with 200 µl pentobarbital (Euthasol®, Dechra, Northwich, UK) injected intraperitoneally. The tumor tissue was then sampled for histology and immersed in 4% Formaldehyde with a pH of 7 (MM France—Microm, Brignais, France) before being embedded in paraffin and cut into 3 µm slabs every 100 µm across the whole sample. The slabs were then scanned and saved using a NanoZoomer S360MD (Hamamatsu, Japan).

### 2.2.2. SWE

Shear waves were generated along the width of the transducer array (X direction), by focusing at 5 different depths ranging between 6 and 16 mm during 6.5 µs at a frequency of 13 MHz and 25 V. Ultrafast imaging was then used to build a 2D map of the shear wave speed from 25 images generated by combining three plane waves emitted with different orientations (−3°, 0° and 3°), at a frame rate of 8333 Hz. The presence of shear waves was verified visually during the postprocessing. These 2D maps of the shear wave speed were acquired for 49 plane locations every 0.25 mm along the tumor, for three different scanning directions (0°, 45°, 90°). The final SWE volumes were reconstructed by averaging these three scans, so as to obtain isotropic resolution and pixel size (Demené et al 2016).

### 2.2.3. Ultrasensitive Doppler imaging

Similarly as for SWE, the uDoppler 3D volume was reconstructed from 49 2D planes with 0.25 mm spacing acquired in three different orientations (0°, 45°, 90°). For each plane, 200 images were acquired at a frame rate of 1000 Hz, each image being reconstructed from a combination of 11 tilted plane waves (angles ranging from −5° to +5° with a 1° step).

The blood flow data was obtained by applying singular value decomposition to the 200 image blocks and cutting off the 5 first eigenvectors, as explained in Demené et al (2015). The acquisitions corresponding to the different orientations were then averaged.

<footnote><sup>a</sup> Nb of PyMT animals imaged on the same mammary pad each week, regardless of the first tumor location.</footnote>
### 2.2.4. BTI

BTI was performed using the tomographic approach presented in Guillaumin et al (2022). The imaging volume was a 14 mm cylinder of 15 mm depth. The probe rotated around the Z axis at a step of 10° chosen as a compromise between acquisition time and image quality (Guillaumin et al 2022). The local BTI coherence for each probe angle was evaluated by averaging coherence values plane by plane within a cylinder of 0.55 mm diameter (in the XY plane) and 2 mm depth (Z axis). Three quantitative US maps were extracted from this imaging:

* BTI Co: map of the local tissue coherence, quantifying the local tissue organization level

* BTI FA: map of the Fractional Anisotropy, quantifying the local anisotropy of the tissue backscattering coefficient

* BTI angle: map of the local tissue orientation angle in degrees.

### 2.2.5. Ultrafast localization microscopy

Real-time uDoppler was used to select a highly vascularized view of the tumor by rotating the probe around its imaging axis. A volume of 120 ± 20 µl of commercial microbubbles (SonoVue®, Bracco, Milan, Italy) was injected via the animal's tail-vein, while carefully monitoring its breathing and heartbeat. A total of 150 blocks of 250 images were acquired at a frame rate of 2778 Hz. Coherent compounding was obtained with nine plane waves (tilted between -4° and +4° with a 1° step). Microbubble localization and tracking was performed as described in Errico et al (2015).

## 2.3. Image analysis

### 2.3.1. ROI segmentation

Tumor segmentation was done semi-automatically using the ITK-snap Active Contours Segmentation mode (Yushkevich et al 2006), with Edge Attraction pre-segmentation (edge transformation exponent: 4, Gaussian blurring scale:1, edge transformation contrast: 0.1). The segmentation step was carried out with an edge attraction force of 5, and an expansion force of 0.3, and a smoothing force at 0 until all the foci were merged, and 1 afterwards, until the contour stopped varying. The only user input was the selection of seed points inside the tumor, and protocol parameters were chosen empirically to provide similar results for different choices of seed points, so as to minimize potential operator bias.

This segmentation, named 'tumor', was also used to produce other contours, corresponding to different zones of the tumor or its periphery:

* The 'center' was obtained via a 0.75 homothety of the original tumor contour with respect to its barycenter.

* The 'peritumoral' zone contour corresponded to a dilation of the original tumor contour by a 2 mm margin in all directions.

The healthy mammary pads for WT mice, or for PYMT mice before tumor apparition, were delineated manually with ITK-snap tool, and named 'control'.

### 2.3.2. Feature extraction

A selection of 43 first order statistics and radiomics features, were extracted for each of B-mode, SWE, BTI Co, BTI FA and BTI angle, inside each of 'tumor', 'center' and 'peritumoral' contours defined above. The tumor volume (in mm³) was measured for B-mode only. Radiomics features were calculated following the methodology described in the Image Standardization Biomarker Initiative (Zwanenburg et al 2020). In addition, 16 vascular features were extracted for each contour, as described in the next paragraph. For each feature, the difference between the average feature in the tumor and peritumoral zone, as well as between the peritumoral zone and center were also calculated. As a result, each feature took five different values for each US map, corresponding to the tumor, center, peritumoral, peritumoral-tumor and peritumoral-center respectively. This resulted in a total of 1156 features taken into account at the start of the analysis.

### 2.3.3. Vascular feature extraction

The uDoppler image was filtered using the 3D vesselness filter (Frangi et al 1998, Jerman et al 2016), with a parameter $\tau$ of 0.9 and a scale array of [0.5,2,3]. The filtered image was then binarized using the corresponding Matlab (The MathWorks, Inc., Natick, Massachusetts, United States of America) function `imbinarize` with a fixed threshold t1. The binarized image was segmented to extract one of the contour zones described above. For each one, it was then used to measure the global vascular volume, as well as the vascular Density defined as the ratio between the number of voxels in the vascular tree and the total number of voxels in the volume.
To extract more vascular parameters, the vessel skeleton was extracted using the bwskel Matlab function, from which each vessel was defined as a cluster of connected voxels, after removing branch points. Vessel length was measured as the sum of distances between consecutive vessel voxels. This allowed for measuring the number of vessels, the number of vessels as a fraction of the total skeleton length, the total length of vessels, the total length of vessels as a fraction of the volume, the mean and maximum length, as well as the variance, skewness and kurtosis of vessel lengths. Tortuosity was measured for each vessel using the methodology in Bullitt et al (2003), as well as using the sum of angles metric (Hart et al 1997).

Local vessel diameter was further measured at each skeleton point as the average of the five smallest distances between this point and voxels outside the binarized vascular tree. From this collection of diameter values, the overall mean and maximum were measured, as well as the variance, skewness and kurtosis of diameter values along a single vessel, averaged over vessels.

A similar methodology was used for 2D ULM images, with a single custom binarization threshold, and a morphological opening operation performed before skeletonization to avoid extra vessel artefacts.

### 2.3.4. Feature selection

#### Robustness to image processing parameters

In order to keep only the most robust features, we first evaluated the sensitivity to segmentation differences for B-mode, BTI and SWE features. This was done by manually altering the local shape of the automatic tumor contour, keeping the volume constant. Three new segmentations were created with this method for each of four different tumors of different sizes, corresponding to different individuals. Each contour was then automatically modified using the *imdilate* and *imerode* functions in Matlab, with a 3-dimensional structuring element of 1 or 2 pixels in radius. This generated 12 slightly different segmentations for each of the four tumors selected. For each modality, feature and tumor, we measured the interclass correlation coefficient as follows:

$$ICC = \frac{V_{\text{tumors}}}{V_{\text{tumors}} + V_{\text{contours}}}$$

where $V_{\text{tumors}}$ is the variance of values obtained across tumors and $V_{\text{contours}}$ the variance of values obtained across different segmentation contours for a given tumor. An ICC close to 1 therefore corresponds to a feature robust to segmentation differences. Only features reaching ICC values above 0.8 were kept for the analysis. In the case of vascular imaging, a similar methodology was used by varying for the same 4 tumors parameter t1 between 20 and 30, and t2 between 0, 50 and 100.

#### Predictive power

The remaining features were evaluated in terms of their predictive power for discriminating healthy mammary pads from mammary tumors. This was done by normalizing the feature values by their maximum value across the dataset, and calculating the area under the receiver operating curve (AUC) for the classification of values into control/tumor, using the pROC library in R software (R Core Team 2022). Only features with an AUC above 0.8 were kept in the analysis. This was done independently for each tumor zone (i.e. tumor, center, peritumoral, peritumoral—tumor and peritumoral—center), so that a given feature could be included in the analysis for some zones only.

#### Correlation between redundant features

Features were then further selected by keeping only a non-redundant sub-selection. This was done by measuring the Pearson correlation coefficient and associated *p*-value between all the remaining individual features. Features were considered redundant when the coefficient *r* was above 0.8 and the *p*-value below 0.05. Among redundant features, the one with the highest AUC was kept.

### 2.3.5. Histology analysis

Tumors areas were manually outlined with the help of expert pathologists on each of the histology slices obtained for each extracted tumor. The proportion in the tumor area of tumor cells, liquid, adipocytes and fibers in the tumor was obtained by selecting appropriate HSV thresholds to extract pixels of the color corresponding to each category. The tumor outline, and segmentation maps obtained for each category, as well as the corresponding measured proportions are illustrated on an example on figure 1. Each tumor was scored according to the presence (score of 1) or absence (score of 0) of a bundle of fibers enveloping the tumor area, to be compared to BTI imaging parameters. In addition, blood vessels were manually counted on each slice. From these measurements, the average number of vessels per slice was extracted to be compared to ULM vessel count. Only vessels with a measured diameter above 5 µm were taken into account to fit the imaging resolution limit. The total blood volume inside the tumor was estimated on histology by manually measuring blood vessel diameters on each slab and estimating the corresponding volume knowing the slab
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Illustration of the analysis process for histology slices. Black arrows on (c) indicate a bundle of collagen fibers surrounding the tumor area. This tumor was scored as 1 in terms of « enveloping fiber structure ». Panels (d), (e), (f) and (g) show the segmentation maps obtained respectively for fibers in the whole image, and for tumor cells, for adipocytes, and liquid secretions in the tumor area.</figcaption></figure>

thickness. Parameters obtained on histology and US imaging were normalized by the maximum across
tumors to compare relative rather than absolute values, which were expected to differ because of inherent
differences in the data.

2.3.6. Predictive models

Parameter selection scenarios

The correlation step described above to select predictive features (cf.2.3.4)) was done individually among all zone contours (tumor, center, peritumoral etc), but also among full tumor contours only, to evaluate the case when only the full tumor contours are available (e.g. very small lesions). As parameters evaluated within one contour were often correlated to the same parameter evaluated in a different contour, this led to two different evaluation scenarios corresponding to a different selection of features:

* Scenario 1: all tumor contours and differences used as model input parameters

* Scenario 2: only features measured inside the contour 'tumor' used as model input parameters

Model training

For each scenario, all features in the final selection were used as input in the extreme gradient boosting decision tree algorithm (XGBoost package) in the R software, to perform classification between tumors and healthy mammary pads. 5-fold cross-validation was run by randomly selecting for each fold 10 tumors and 10 healthy mammary pads for testing, and training the model on the remaining data. The model was trained until the error rate did not change for 5 successive iterations. The reproducibility of the training was also verified by training several instances of each model with the same dataset, to check the stability of the training and input parameter selection.

Model evaluation

Results obtained on the testing data are reported averaged over the 5 folds. Because the decision tree
algorithm identifies the best possible split at each step of the optimization, some of the input parameters may
be unused in the final model. The parameters included in the final models were extracted as the ones most
useful for the classification problem.
The models for Scenarios 1 and 2 were then further applied to the animals imaged on the same mammary pad every week, and used to determine the status (tumor or healthy) of the mammary pads imaged before apparition of a palpable tumor.

# 3. Results

## 3.1. Animal experiments

Each PyMT animal developed at least one palpable tumor which was imaged using our multiparametric protocol across several weeks. Tumor sizes ranged from 1.34 mm³ to 425 mm³.

## 3.2. Multiparametric acquisition

### 3.2.1. Imaging results

Images with visible contrast were obtained for all of the tumors, with the animal recovering fully from the anesthetics after the imaging session. An example of co-registered volumes obtained for one tumor is provided on figure 2, showing 3D views of the fiber structure in the peritumoral area, the elasticity inside the tumor volume and the blood vessels obtained with uDoppler in the tumor and peritumoral areas. In this particular case, high SWE speed was obtained inside the tumor (+34% above the average across tumors), corresponding to high rigidity, whereas Co, FA and vessel density values inside the tumor were close to the average values across tumors. This agrees well with the large proportion of tumor cells visible on a representative histology slice, with little other elements (adipocytes, blood vessels, fibers) present inside the tumor area.

### 3.2.2. Validation with histology

Figure 3(a) shows the significant Pearson correlation ($p < 0.05$) found between the average rigidity from SWE in the full tumor volume and the average proportion of adipose cells across histology slices. Similar correlation was measured with the proportion of tumor cells in the histological tumor area ($R^2 = 0.55, p = 0.016$). The number of vessels identified on ULM data and the average number of vessels per slice counted on histology data were correlated with $R^2 = 0.51$, and $p = 0.027$ (cf figure 3(b)). The difference in average BTI FA in the peritumoral area was significant between tumors scored as 1 and 0 for the presence of an enveloping fiber structure on histology slices, according to the Wilcoxon signed rank test (cf figure 3(c)). Finally, significant correlation was obtained between blood volume values measured on uDoppler volumes and histology, with $R^2 = 0.68$, and $p = 0.0014$ (cf figure 3(d)).

### 3.2.3. Quantitative monitoring of tumor development

US-derived quantitative parameters were compared across three groups of tumors of different sizes, corresponding to tumor sizes under 15 mm³ (Small, $N = 21$), above 30 mm³ (Large, $N = 20$), and between 15 mm³ and 30 mm³ (Medium, $N = 32$) to compare tumors at different development stages. Figure 4(a) shows lower mean B-mode gray level values for larger tumors compared to the smaller and medium tumors. This difference was significant in the tumor and center zones. There was an increase of SWE speed with tumor growth, yielding significantly higher mean values for large masses compared to small and medium masses, especially in the tumor and center zones (cf figure 4(b)). Mean tissue anisotropy (BTI FA) was significantly higher in larger masses in the tumor and center zone, and was significantly larger in the peritumoral zone compared to the tumor and center zones, for small, medium and large masses (cf figure 4(c)). The mean BTI Co, quantifying the degree of tissue organization, decreased with tumor growth, with significantly lower values reached for large masses compared to medium and small masses, especially in the tumor and center zones (cf figure 4(d)). The peritumoral zones also yielded significantly lower values than the tumor and center zones. Finally, uDoppler density was significantly lower in peritumoral zone contours compared to the tumor and its center. There was also a significant decrease of the vascular density with tumor growth, particularly when measured in the full tumor contour (cf figure 4(e)).

### 3.2.4. Predictive models

From the 1155 features obtained across all US maps and contours, 180 features with ICC below 0.8 were kept in the analysis. From these, 101 features with AUC above 0.8 were kept. Finally, for Scenario 1, after selecting non-redundant features, 28 were kept. These included 3 for B-mode, 1 for SWE, 7 for BTI Co, 10 for BTI FA, 5 for BTI angle and 2 for uDoppler, as given in table 2. For Scenario 2, 16 were kept, including 4 for B-mode, 0 for SWE, 1 for BTI Co, 7 for BTI FA, 4 for BTI angle and 0 for uDoppler, as given in table 3.

The model obtained for Scenario 1, considering all contours, achieved an AUC of $99 \pm 2\%$ across 5 realizations. A single parameter was included alone in 4 out of 5 realizations, and yielded the highest overall gain to the model (cf figure 5(a)): Large Zone, High Gray level Emphasis (LZHGE) of the GLSZM applied to
<figure><img src="image_5.png" /></figure>

the angle map measured in the peritumoral contour volume. A model run only with this parameter achieved 100% accuracy on average across 5 realizations. The 5th model realization, included also the LZHGE, but applied to FA maps of the peritumoral area, with a gain of 99.6%. Only parameters evaluated on the peritumoral contour volumes were included in the final model.

The model run for Scenario 2 achieved an AUC of 99 ± 2% across 5 realizations. The most important feature for this model was the LZHGE parameter applied to the map of tissue angles, and the LZE applied to the B-mode intensity map (cf figure 5(c)). Both parameters were significantly higher for tumors (t-test $p < 0.0001$), as shown on figure 5(b), indicating larger homogeneous areas of similar angular orientation or echogenicity. When evaluated on handcrafted gray level maps of different spatial organization for the same gray level distribution, LZHGE reached higher values for organized structures, such as the one illustrated on figure 5(d). Its value also increased when increasing the number or size of high intensity focal regions, all other pixels remaining the same. Other B-mode information, and information extracted from BTI FA and angle maps was also used by the model. The same model run using only the two prominent features reached 98 ± 3% accuracy.

No uDoppler or SWE parameters were included in any of the models obtained.

Both models were applied to the classification of 6 mammary pads, which had been imaged weekly before the apparition of a palpable tumor, leading to 17 imaged volumes. The models for Scenario 1 and Scenario 2 respectively classified 94% (all volumes but one) and 100% of these volumes as tumors. Further analysis of the temporal evolution of the parameters included in the model showed that GLSZM_LZHGE_FA, Kurtosis_FA, Mean_FA, GLSZM_LZHGE_Angle, GLCM_Correlation_Bmode and GLSZM_LZE_Bmode significantly increased with the apparition of a palpable tumor, while GLCM_SumAverage_FA, GLSZM_LGZE_FA, GLRLM_SRLGE_FA, GLRLM_LRLGE_FA and GLSZM_SZE_Angle significantly decreased, according to a Wilcoxon signed-rank test between values obtained in the volume before and after apparition of a palpable tumor, with a $p$-value of 0.05 considered significant.
<figure><img src="image_6.png" /><figcaption>Figure 3. Comparison of ultrasound imaging-extracted features with parameters measured on histology.</figcaption></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>Figure 4. Evolution with size of average quantitative imaging values in different zone contours compared to control pads. Statistics were obtained for comparing groups with a t-test when the distribution of values was normal, and a Wilcoxon signed rank test otherwise, with *: p < 0.05, ** p < 0.01, ***: p < 0.001.</figcaption></figure>
<table><caption>**Table 2.** Features selected as input parameters to the predictive model for scenario 1, when considering all possible zone contours.</caption><thead><tr><th>Modality</th><th>Type</th><th>Name</th><th>Contour</th></tr></thead><tbody><tr><td rowspan="3">B-mode</td><td>First order statistics</td><td>Skewness</td><td>Tumor</td></tr><tr><td>GLRLM</td><td>SRLGE</td><td>Tumor</td></tr><tr><td>GLCM</td><td>Correlation</td><td>Center</td></tr><tr><td>SWE</td><td>GLRLM</td><td>LRHGE</td><td>Center</td></tr><tr><td rowspan="5">BTI Co</td><td>First order statistics</td><td>Kurtosis</td><td>Peritumoral</td></tr><tr><td>GLCM</td><td>Variance</td><td>Peritumoral</td></tr><tr><td></td><td>Variance</td><td>Peritumoral-center</td></tr><tr><td>GLSZM</td><td>Autocorrelation</td><td>Peritumoral</td></tr><tr><td>NGTDM</td><td>SZE</td><td>Center</td></tr><tr><td></td><td>ZSV</td><td>Peritumoral-center</td></tr><tr><td></td><td>Complexity</td><td>Peritumoral</td></tr><tr><td rowspan="6">BTI FA</td><td>First order statistics</td><td>Kurtosis</td><td>Tumor</td></tr><tr><td></td><td>Mean</td><td>Peritumoral-center</td></tr><tr><td>GLSZM</td><td>LGZE</td><td>Tumor</td></tr><tr><td></td><td>LZHGE</td><td>Peritumoral</td></tr><tr><td></td><td>ZSN</td><td>Peritumoral-center</td></tr><tr><td>GLRLM</td><td>SRLGE</td><td>Peritumoral</td></tr><tr><td></td><td>LRLGE</td><td>Peritumoral</td></tr><tr><td></td><td>NGTDM</td><td>Contrast</td><td>Peritumoral</td></tr><tr><td></td><td></td><td>Complexity</td><td>Peritumoral-center</td></tr><tr><td></td><td></td><td>Strength</td><td>Peritumoral-center</td></tr><tr><td rowspan="4">BTI angle</td><td>GLCM</td><td>Homogeneity</td><td>Peritumoral-tumor</td></tr><tr><td></td><td>Contrast</td><td>Peritumoral-tumor</td></tr><tr><td>GLSZM</td><td>LZHGE</td><td>Peritumoral</td></tr><tr><td></td><td>SZE</td><td>Peritumoral</td></tr><tr><td></td><td></td><td>SZE</td><td>Center</td></tr><tr><td rowspan="2">uDoppler</td><td>GLCM</td><td>Length of vessels</td><td>Center</td></tr><tr><td>Vascular</td><td>Diameter mean</td><td>Peritumoral—tumor</td></tr></tbody></table>

<table><caption>**Table 3.** Features selected as input parameters to the predictive model for scenario 2, when considering tumor zone contours only.</caption><thead><tr><th>Modality</th><th>Type</th><th>Name</th></tr></thead><tbody><tr><td rowspan="4">B-mode</td><td>First order statistics</td><td>Skewness</td></tr><tr><td>GLCM</td><td>Correlation</td></tr><tr><td>GLSZM</td><td>LZE</td></tr><tr><td>GLRLM</td><td>SRLGE</td></tr><tr><td>SWE</td><td>GLCM</td><td>Autocorrelation</td></tr><tr><td rowspan="5">BTI Co</td><td>First order statistics</td><td>Mean</td></tr><tr><td></td><td>Kurtosis</td></tr><tr><td>GLCM</td><td>SumAverage</td></tr><tr><td>GLSZM</td><td>LGZE</td></tr><tr><td>GLRLM</td><td>LZHGE</td></tr><tr><td></td><td>SRLGE</td></tr><tr><td></td><td>LRLGE</td></tr><tr><td rowspan="4">BTI angle</td><td>GLCM</td><td>Homogeneity</td></tr><tr><td>GLSZM</td><td>LZHGE</td></tr><tr><td></td><td>SZE</td></tr><tr><td>GLRLM</td><td>RP</td></tr></tbody></table>

## 4. Discussion

In this study, B-mode, SWE, uDoppler, and BTI were acquired for the first time *in-vivo*, in a fully co-registered framework, providing images with high contrast for the different quantitative US maps obtained, as illustrated on figure 2. This was achieved for all animals on a weekly basis with a single device,
<figure><img src="image_9.png" /></figure>

and in 1 h of acquisition in total, partly because of the discontinuous acquisition due to respiratory and cardiac gating to avoid motion artefacts. Combining the different imaging techniques into a single interleaved tomographic acquisition protocol could potentially reduce the total acquisition time, but the main limiting factor was the gating in our case. Although this acquisition time would be too long for clinical purposes, due to the tomographic approach used here, future work using US transducers adapted to volumetric imaging will help achieve imaging times of a few minutes. By using such transducers capable of capturing a whole volume in one or two emission/reception blocks, the constraints both of scanning the area of interest and of gating the acquisition would be dropped. Data post-processing may still require additional time, especially for BTI, but this would not hamper the clinical feasibility since our approach focuses on providing quantitative biomarkers, rather than a real-time visualization tool. With such technological advances, our non-invasive multiparametric 3D approach could potentially be used in the clinic, in a repeated way when monitoring is required.

The different parametric US maps obtained informed on several quantitative aspects of the microscopic tumor structure measurable on histology. It should be noted that the histology-derived parameters represented part of the tumor only, as slabs were not extracted continuously, but every 100 µm. These still spanned the whole tumor volume, but this discontinuous sampling could explain the relatively low $R^2$ values obtained in some cases. It should also be noted that the resolution of the histopathology, obtained by a microscope, and of most of the US techniques used here (all except ULM) is different by a few orders of magnitude, and that some microscopic aspects of the tumor can only be captured indirectly by the imaging.

The significant inverse correlation between the SWE rigidity and the proportion of adipose tissue in the tumor volume is in line with the lower elastic modulus reported for fat tissue in breast compared to other tissue types (Demené et al 2015). The proportion of tumor cells, which was itself correlated to the percentage of adipose cells in our data.

The BTI FA, which quantifies the degree of anisotropy of the tissue structure, was significantly larger in the peritumoral zone of tumors visually scored as exhibiting an enveloping collagen fiber structure on histology. The correlation between FA and fiber structure around the tumor has already been demonstrated in a previous study on human breast masses (Guillaumin et al 2024), confirming that the BTI is sensitive to collagen fibers. The comparison with histology was limited here by the relatively large peritumoral contour, despite a sparse fiber structure on histology. PyMT tumors are known to be low in collagen compared to other tumor models, and a different model may have been more appropriate for the specific validation of
BTI biomarkers within the tumors. However, as illustrated on figure 1, the fiber bundles were clearly visible on the histology slabs, making the scoring unambiguous.

The relative number of blood vessels counted on the histology slices matched the relative number identified on ULM images. This comparison reaches its limits for very small vessels, both because of the manual counting on histology images, and of the resolution of ULM, which is why the smallest vessels were excluded from the analysis. In addition, the present data compares average vessels per slice for histology to a single slice imaged with ULM, manually selected to contain as many vessels as possible on uDoppler imaging. This may explain the dispersion of data values, and the tendency of ULM to report more vessels here.

Finally, relative blood volume measurements were significantly correlated between 3D uDoppler and histology data, despite a large variance across tumors. The blood volume estimation also assumed continuous sampling for the histology data, which is why relative values are compared again in this case. This could explain the higher relative blood volume values obtained for uDoppler.

The different imaging techniques provided informative quantitative data on tumor evolution, indicating significant changes during tumor growth (cf figure 3). In particular, SWE speed and BTI FA measurements similarly showed a significant increase with tumor growth, conversely to a decrease in BTI Co. This is consistent with the recruitment of collagen fibers driving the metastatic process. PyMT tumors are known to exhibit similar evolution to human breast cancer, and metastasize for this strand of mice at approximately 2 months. In this study, the mice were euthanized after 8 weeks to avoid advanced metastatic cancer for ethical reasons, but the larger tumors in this study are probably close to this stage. Lower vessel density in larger tumors is consistent with the dysfunctional tumor vasculature, often failing to span the whole of large tumors. Lower B-mode mean values for large tumors could be due to the apparition of necrotic or liquid pools with tumor growth, which were visible on histology (cf figure 1(c)).

This biologically relevant and complementary information led to high accuracy of the models using them to differentiate cancerous and healthy mammary pads. Models built for Scenario 1 (only full tumor contours) and Scenario 2 (all zone contours) both reached 99% accuracy, demonstrating that our imaging can be sufficient to identify cancerous mammary pads. Interestingly, the final models used only a few parameters in the analysis, comprising BTI angle and B-mode textural information. No average features were kept as predictors, which confirms the need to explore image texture beyond mean values to extract useful information.

Among BTI-derived parameters, the tissue orientation (angle) in the peritumoral area, and more specifically the LZHGE of this tissue angle map presented the largest gain in predictive power for both Scenario 1 and 2, and yielded a very high AUC on its own. This parameter is particularly high in an ROI with a large proportion of large regions of similar tissue orientation as illustrated on figure 4(b). It was also included in Scenario 1 when applied to BTI FA in the peritumoral area. This could indicate sensitivity to large fiber bundles oriented in specific directions around the tumor. The predictive B-mode information corresponds mainly to the LZE, indicating the presence of large homogeneous areas, probably of low to medium intensity. This is consistent with results from figure 3(a).

No uDoppler parameters were kept as predictors, although several were found capable of differentiating tumors from healthy mammary pads with an AUC above 0.8. Only uncorrelated vascular parameters were kept as model inputs, but were not used by the models. Their exclusion may be due to sufficient predictive power of the BTI-derived information. AUC values and data from figures 3 and 4 still show that vascular information is informative and complementary to the BTI information. Future work will explore the potential of ULM more in depth, as it was limited to 2D slices imaged on the last imaging session here.

The absence of SWE information from the final models, and the presence of only one parameter with an AUC above 0.8 in the final parameter pool is surprising given the literature. This could be due to the small fiber content of the PyMT mice compared to human breast, and to a certain degree of correlation with the most predictive BTI-derived parameters, used preferentially by the models.

The tumor volume did not reach an AUC above 0.8, i.e. was not sufficient on its own to separate tumors from healthy mammary pads and was therefore not selected as a model input. In addition, none of the features included as model inputs were correlated to the volume. This is an important point to stress, as the healthy mammary pads were relatively small (maximum of 2 mm³) compared to the tumors (maximum of 425 mm³), although there was some overlap in size. The choice of comparing the tumor ROIs to small delineated regions as opposed to arbitrary regions of more varied sizes was made to ensure that the healthy mammary pad ROIs were physiologically relevant, and did not encompass other surrounding organs. The models described here were also run and tested using only the smallest tumor ROIs (N = 33, up to 3 mm³), which returned identical results in terms of the important parameters chosen, with slightly lower accuracy. These different points confirm that the classification was not done on the basis of the tumor volume.

It is also interesting to note that only features evaluated in the peritumoral contour were included as predictors in the model of Scenario 1. Peritumoral contour features had higher AUC values than their
counterparts in other zone contours. This result is likely to depend on the definition of the peritumoral zone. In this work, a region of 2 mm around the tumor was chosen to encompass enough tissue to be informative without reaching too far beyond the tumor boundaries. This was important as we imaged mammary pads positioned in different regions of the animal, with a variety in surrounding tissues. However, the peritumoral zone could potentially be decreased. Indeed, the most important model features remained the same when considering the tumor volume only (Scenario 2), which could indicate that the relevant changes occurred at the tumor boundary. This scenario avoids the arbitrary definitions of the peritumoral and center zone. Fiber bundles surrounding the tumor were indeed visible on histology slices, as shown on figure 1. This work confirms previous findings on *ex-vivo* human breast masses, which identified BTI-derived parameters at the tumor boundary as relevant for tumor characterization (Guillaumin *et al* 2024).

The models were also evaluated on mammary pads imaged before the tumor was palpable, classifying all of them as tumors. The tumor class was chosen despite the relatively small size of these volumes. Such a model, trained on appropriate clinical data, could be used in the future in similar settings for screening applications. It should be noted that the problem studied here was the classification of whole mammary pads into cancerous or non-cancerous ones, rather than the identification of an abnormal region within the breast or differentiation between a benign and a cancerous mass.

The results presented here may be affected by the choice of segmentation, i.e. semi-automatic segmentation performed by one operator only. The effect of the operator-dependence in the segmentation, however, was minimized by choosing only parameters little influenced by the exact outline, as explained in the methods. Although this work shows promise for non-invasive breast cancer characterization, the evaluation of our approach on a single murine model makes it difficult to generalize on how well it could perform on other types of tumors. The PyMT model was chosen as the most relevant for human breast cancer, both because it spontaneously grows tumors, allowing the study of the tumor interaction with its microenvironment, and because its evolution most resembles human metastatic breast cancer (Guy et al 1992). The main disadvantage of this model is its low fiber content, which may have led to some redundant information captured by BTI and SWE in this case. Indeed, in our previous work on *ex-vivo* human breast masses, we showed that BTI was related to the collagen fiber structure, and that it provided complementary information to SWE (Guillaumin et al 2024). More generalizable information could be extracted from a larger dataset of more diverse tumor models to better account for the heterogeneity in human breast cancer. This study should therefore be considered as an *in-vivo* proof of concept of the feasibility, biopathological relevance and usefulness for diagnostics of the multiparametric volumetric US approach, and our future work will focus on developing this approach for the clinic.

## 5. Conclusion

This work has applied for the first time fully concurrent B-mode, SWE, uDoppler, BTI and ULM in 3D *in-vivo* using a unique custom-built device. The analysis of the quantitative markers extracted from the different imaging volumes obtained has highlighted correlation between histology-derived and imaging-derived markers. This markers used in an ensemble learning model were shown to be useful in identifying tumor tissue, particularly BTI-derived markers quantifying heterogeneity. Further development is necessary to evaluate the benefits of this approach in a clinical setting, in particular for breast cancer diagnosis and screening.

## Data availability statement

The data cannot be made publicly available upon publication because the cost of preparing, depositing and hosting the data would be prohibitive within the terms of this research project. The data that support the findings of this study are available upon reasonable request from the authors.

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## Acknowledgments

The authors would like to thank collaborators at Institute Physics for Medicine, in particular Alexandre Dizeux, Lauriane Delay, Sophie Pezet, Nathalie Ialy-Radio, Philippe Mateo and Lantonirina Abdou-Agige who assisted in the animal experiments, as well as the pathology team at Institut Curie, Paris, including Martial Caly and Andre Nicolas for their training on histology preparation.

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## Ethical statement

All animal experiment protocols were reviewed and approved by the relevant ethics committee (Comité d'éthique en matière d'expérimentation animale Paris Centre et Sud) under the name «Imagerie multiparamétrique du cancer du sein par ultrasons ultrarapides», N° 30370 2021031215438668V3. All experiments followed the European guidelines of the Directive 2010/63/EU.

## ORCID iDs

Jean-Baptiste Guillaumin https://orcid.org/0000-0002-8713-9800

Aymeric Nadjem https://orcid.org/0009-0002-6154-6376

Léa Vigouroux https://orcid.org/0009-0003-9243-055X

Jean-François Aubry https://orcid.org/0000-0003-2644-3945

Béatrice Berthon https://orcid.org/0000-0002-2984-2354

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## References

Antropova N, Huynh B Q and Giger M L 2017 A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets *Med. Phys.* 44 5162–71

Athanasiou A, Tardivon A, Tanter M, Sigal-Zafrani B, Bercoff J, Deffieux T, Gennisson J-L, Fink M and Neuenschwander S 2010 Breast lesions: quantitative elastography with supersonic shear imaging—preliminary results *Radiology* 256 297–303

Berg W A, Bandos A I, Mendelson E B, Lehrer D, Jong R A and Pisano E D 2016 Ultrasound as the primary screening test for breast cancer: analysis from ACRIN 6666' *J. Natl Cancer Inst.* 108 djv367

Berg W A et al 2012 Shear-wave elastography improves the specificity of breast US: the BE1 multinational study of 393 masses *Radiology* 262 435–49

Bullitt E, Gerig G, Pizer S M, Lin W and Aylward S R 2003 Measuring tortuosity of the intracerebral vasculature from MRA images *IEEE Trans. Med. Imaging* 22 1163–71

Demené C et al 2015 Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases Doppler and fUltrasound sensitivity *IEEE Trans. Med. Imaging* 34 2271–85

Demené C, Tiran E, Sieu L-A, Bergel A, Gennisson J L, Pernot M, Deffieux T, Cohen I and Tanter M 2016 4D microvascular imaging based on ultrafast Doppler tomography *NeuroImage* 127 472–83

Dizeux A, Payen T, Le Guillou-Buffello D, Comperat E, Gennisson J-L, Tanter M, Oelze M and Bridal S L 2017 *In vivo* multiparametric ultrasound imaging of structural and functional tumor modifications during therapy *Ultrasound Med. Biol.* 43 2000–12

Dobruch-Sobczak K, Piotrzkowska-Wróblewska H, Klimonda Z, Karwat P, Roszkowska-Purska K, Clauser P, Baltzer P A T and Litniewski J 2021 Multiparametric ultrasound examination for response assessment in breast cancer patients undergoing neoadjuvant therapy *Sci. Rep.* 11 2501

Errico C, Pierre J, Pezet S, Desailly Y, Lenkei Z, Couture O and Tanter M 2015 Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging *Nature* 527 499–502

Farrokh A, Maass N, Treu L, Heilmann T and Schäfer F K 2019 Accuracy of tumor size measurement: comparison of B-mode ultrasound, strain elastography, and 2D and 3D shear wave elastography with histopathological lesion size *Acta Radiol.* 60 451–8

Fox S B, Generali D G and Harris A L 2007 Breast tumour angiogenesis *Breast Cancer Res.* 9 216

Frangi A F, Niessen W J, Vincken K L and Viergever M A 1998 Multiscale vessel enhancement filtering *MICCAI'98* (Springer-Verlag) pp 130–7

Gennisson J-L, Provost J, Deffieux T, Papadacci C, Imbault M, Pernot M and Tanter M 2015 4-D ultrafast shear-wave imaging *IEEE Trans. Ultrason. Ferroelectr. Freq. Control* 62 1059–65

Guillaumin J-B et al 2022 Proof of concept of 3-D backscatter tensor imaging tomography for non-invasive assessment of human breast cancer collagen organization *Ultrasound Med. Biol.* 23

Q8

Guillaumin J-B, Djerroudi L, Aubry J-F, Tardivon A, Dizeux A, Tanter M, Vincent-Salomon A and Berthon B 2024 Biopathologic characterization and grade assessment of breast cancer with 3-D multiparametric ultrasound combining shear wave elastography and backscatter tensor imaging *Ultrasound Med. Biol.* 50 474–83

Guy C T, Cardiff R D and Muller W J 1992 Induction of mammary tumors by expression of polyomavirus middle T oncogene : a transgenic mouse model for metastatic disease 12 954–61

Q9
Q10

Hart W E, Cote B and Nelson M R 1997 Automated measurement of retinal vascular tortuosity *Proc. AMIA Annual Fall Symp.* pp 459–63

Heppner G H 1984 Tumor heterogeneity *Cancer Res.* 44 2259–65

Jerman T, Pernus F, Likar B and Spiclin Z 2016 Enhancement of vascular structures in 3D and 2D angiographic images *IEEE Trans. Med. Imaging* 35 2107–18

Kapetas P, Clauser P, Woitek R, Wengert G J, Lazar M, Pinker K, Helbich T H and Baltzer P A T 2019 Quantitative multiparametric breast ultrasound: application of contrast-enhanced ultrasound and elastography leads to an improved differentiation of benign and malignant lesions *Investigative Radiol.* 54 257–64

Kaushik S, Pickup M W and Weaver V M 2016 From transformation to metastasis: deconstructing the extracellular matrix in breast cancer *Cancer Metastasis Rev.* 35 655–67

Kerbel R S 2008 Tumor angiogenesis *New Engl. J. Med.* 358 2039–49

Lambin P et al 2012 Radiomics: extracting more information from medical images using advanced feature analysis *Eur. J. Cancer* 48 441–6

Lin E Y, Jones J G, Li P, Zhu L, Whitney K D, Muller W J and Pollard J W 2003 Progression to malignancy in the polyoma middle T oncoprotein mouse breast cancer model provides a reliable model for human diseases *Am. J. Pathol.* 163 2113–26

Yang X, Torres M, Curran W J and Liu T 2014 Ultrasound texture features as potential early imaging biomarkers for normal-tissue toxicity in breast cancer radiation therapy *Int. J. Radiat. Oncol. Biol. Phys.* 90 S24–S3

Liu G, Zhang M-K, He Y, Liu Y, Li X-R and Wang Z-L 2019 BI-RADS 4 breast lesions: could multi-mode ultrasound be helpful for their diagnosis? *Gland Surg.* 8 258–70
Nagy J A, Chang S H, Dvorak A M and Dvorak H F 2009 Why are tumour blood vessels abnormal and why is it important to know? *Br. J. Cancer* **100** 865–9

Nagy J A, Chang S H, Shih S C, Dvorak A M and Dvorak H F 2010 Heterogeneity of the tumor vasculature *Semin. Thromb. Hemost.* **36** 321–31

Papadacci C, Tanter M, Pernot M and Fink M 2014 Ultrasound backscatter tensor imaging (BTI): analysis of the spatial coherence of ultrasonic speckle in anisotropic soft tissues *IEEE Trans. Ultrason. Ferroelectr. Freq. Control* **61** 986–96

Provost J, Papadacci C, Arango J E, Imbault M, Fink M, Gennisson J-L, Tanter M and Pernot M 2014 3D ultrafast ultrasound imaging in vivo *Phys. Med. Biol.* **59** L1–3

R Core Team 2022 R: a language and environment for statistical computing (R Foundation for Statistical Computing) (available at: www.R-project.org/)

Radhakrishna S, Agarwal S, Parikh P M, Kaur K, Panwar S, Sharma S, Dey A, Saxena K K, Chandra M and Sud S 2018 Role of magnetic resonance imaging in breast cancer management *South Asian J. Cancer* **07** 069–71

Sadeghi-Naini A, Suraweera H, Tran W T, Hadizad F, Bruni G, Rastegar R F, Curpen B and Czarnota G J 2017 Breast-lesion characterization using textural features of quantitative ultrasound parametric maps *Sci. Rep.* **7** 13638

Tanter M and Fink M 2014 Ultrafast imaging in biomedical ultrasound *IEEE Trans. Ultrason. Ferroelectr. Freq. Control* **61** 102–19

Yushkevich P A, Piven J, Hazlett H C, Smith R G, Ho S, Gee J C and Gerig G 2006 User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability *NeuroImage* **31** 1116–28

Tian J, Liu Q, Wang X, Xing P, Yang Z and Wu C 2017 Application of 3D and 2D quantitative shear wave elastography (SWE) to differentiate between benign and malignant breast masses *Sci. Rep.* **7** 41216

Zwanenburg A *et al* 2020 Image biomarker standardisation initiative *Radiology* **295** 328–38

Wildeboer R R, Mannaerts C K, van Sloun R J G, Budäus L, Tilki D, Wijkstra H, Salomon G and Mischi M 2020 Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics *Eur. Radiol.* **30** 806–15

Wu L, Zhao Y, Lin P, Qin H, Liu Y, Wan D, Li X, He Y and Yang H 2021 Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ *BMC Med. Imaging* **21** 84