AI-assisted identification of nonmelanoma skin cancer structures based on combined line-field confocal optical coherence tomography and confocal Raman microspectroscopy

Meriem Ayadh, Léna Waszczuk, Jonas Ogien, Grégoire Dauce, Luc Augis, Sana Tfaili, Ali Tfayli, Jean-Luc Perrot, Arnaud Dubois

<figure><img src="image_1.png" /></figure>

► To cite this version:

Meriem Ayadh, Léna Waszczuk, Jonas Ogien, Grégoire Dauce, Luc Augis, et al. AI-assisted identification of nonmelanoma skin cancer structures based on combined line-field confocal optical coherence tomography and confocal Raman microspectroscopy. Journal of Biomedical Optics, 2025, 30 (07), 10.1117/1.jbo.30.7.076008. [hal-05527375](https://hal HAL Id: hal-05527375)

https://iogs.hal.science/hal-05527375v1

Submitted on 25 Feb 2026

**HAL** is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire **HAL**, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Al-assisted identification of nonmelanoma skin
cancer structures based on combined line-field
confocal optical coherence tomography and
confocal Raman microspectroscopy

Meriem Ayadh<sup>a,∗</sup> Léna Waszczuk,<sup>b</sup> Jonas Ogien<sup>c</sup>,<sup>b</sup> Grégoire Dauce,<sup>b</sup>

Luc Augis,<sup>c</sup> Sana Tfaili,<sup>c</sup> Ali Tfayli,<sup>c</sup> Jean-Luc Perrot,<sup>c,a</sup> and Arnaud Dubois<sup>b,d</sup>

**ABSTRACT.** **Significance:** Morpho-chemical characterization of skin cancers provides valuable insights for early diagnosis, classification, and treatment response assessment.

**Aim:** We introduce a compact, noninvasive system combining high-resolution morphological imaging and chemical characterization of skin tissues. The system integrates line-field confocal optical coherence tomography for cellular-level imaging and confocal Raman microspectroscopy to analyze the chemical composition of specific targets identified within the morphological images.

**Approach:** We present results obtained from the system installed in a clinical set-
ting over the course of 1 year. More than 330 nonmelanoma skin cancer specimens
were imaged *ex vivo*, with different structures targeted for Raman microspectro-
scopy, resulting in over 1300 spectral acquisitions. To evaluate the system's ability
to accurately identify cancerous structures, an artificial intelligence model was
trained on the spectral data.

**Results:** The model demonstrated high classification performance, achieving an area under the ROC curve of 0.95 for basal cell carcinoma structures and 0.92 when including structures from both basal and squamous cell carcinomas.

**Conclusions:** Spectral attention scores derived from Raman data revealed key chemical differences among the various cancerous structures, offering deeper insights into their composition.

© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JBO.30.7.076008]

**Keywords:** nonmelanoma skin cancer; confocal Raman microspectroscopy; line-field confocal optical coherence tomography; artificial intelligence model; morpho-chemical characterization

Paper 250055GR received Feb. 25, 2025; revised May 8, 2025; accepted May 29, 2025; published Jul. 28, 2025.

<footnote><sup>a</sup>Dermatologie CHU Saint-Etienne, Laboratoire de Tribologie et Dynamique des Systèmes UMR CNRS 5513, Saint-Etienne, France</footnote>

<footnote><sup>b</sup>Damae Medical, Paris, France</footnote>

<footnote><sup>c</sup>Lip(sys)2, Chimie Analytique Pharmaceutique, Laboratoire de Chimie Analytique Pharmaceutique, Orsay, France</footnote>

<footnote><sup>d</sup>Université Paris-Saclay, Institut d'Optique Graduate School, CNRS, Laboratoire Charles Fabry, Palaiseau, France</footnote>

<footnote>*Address all correspondence to Meriem Ayadh, ayadhmeriem@gmail.com</footnote>
# 1 Introduction

Skin cancer is the most commonly occurring cancer in humans. It is also the cancer for which the number of cases has increased the most over the last decades. Almost all skin cancers can be divided into two categories: melanoma (10% of skin cancers) and carcinoma or non-melanoma skin cancers (NMSC) (90% of skin cancers). Melanoma arises from melanocytes, which are cells in the skin that produce pigment. NMSC arises from other skin cells called keratinocytes.¹,² NMSC can be divided into two categories: basal cell carcinoma (BCC), corresponding to cancerous basal keratinocytes growing into lobules within the dermis (75% of NMSC), and squamous cell carcinoma (SCC), corresponding to cancerous keratinocytes growing within the epidermis (20% of NMSC). More than 300,000 melanoma are diagnosed each year,³ and more than 10 million BCC and 3 million SCC are diagnosed each year,⁴ eventually leading to a higher mortality rate as compared to melanoma,⁵ even though melanoma is more prone to metastasis. In addition to causing negative health outcomes, skin cancer poses a significant economic burden. The factors that most impact prognosis remain early recognition and complete removal of the cancerous tissue before the onset of deep invasion or metastatic disease.⁶

The standard diagnostic procedure is usually based on histological examination and grading of biopsied tissue. To aid early detection of malignant lesions and reduce unnecessary biopsies, noninvasive imaging modalities have been developed, including optical coherence tomography (OCT), reflectance confocal microscopy (RCM), line-field confocal optical coherence tomography (LC-OCT), and multiphoton microscopy (MPM). Conventional OCT provides vertical sections of skin with millimeter-scale fields of view, but it has limited resolution (a few micrometers) and cannot reveal information at the molecular level.⁷ RCM achieves near-histological resolution (~1 µm). However, its penetration depth is limited to ~250 µm, and the *en face* image orientation can make interpretation challenging compared with traditional vertically oriented histological sections.⁸ Moreover, RCM lacks the ability to provide molecular information. Combining OCT and RCM allows clinicians to leverage the high resolution of RCM and the deeper tissue penetration of OCT. Clinical studies have shown that integrating these complementary modalities enhances diagnostic accuracy.⁹,¹⁰

LC-OCT is an imaging method based on the principles of both OCT and RCM with line illumination and detection.¹¹ LC-OCT has the ability to generate horizontal and vertical section images in real time, as well as volume renderings, with a cellular resolution of ~1 µm, and a penetration depth of ~500 µm.¹²,¹³ LC-OCT further benefits from a lateral field of view and an imaging depth (1200 µm × 500 µm) that are superior to those of RCM but inferior to those of traditional OCT. LC-OCT has shown great potential for improving clinical diagnosis of all types of skin cancers, both NMSC and melanoma.¹⁴ More recently, LC-OCT has also been shown to have the ability to assist in evaluating preoperative surgical margins for BCC surgery.¹⁵,¹⁶ However, like RCM and traditional OCT, LC-OCT only provides morphological information about the tissue.

Histology combined with immunohistochemistry, a variant of histology that uses specific protein markers to stain tissues, offers crucial molecular insights for the diagnosis and classification of lesions, particularly for ambiguous lesions or early-stage pathologies with few structural changes. Molecular characterization can also provide information on the tumor environment, aiding in the prediction of the tumor progression and treatment response.¹⁷,¹⁸

MPM is an imaging modality that leverages nonlinear light-tissue interactions to visualize skin morphology and function at the molecular level. Unlike OCT, RCM, and LC-OCT, MPM provides intrinsic molecular contrast by detecting signals emitted at different wavelengths by various endogenous molecules upon excitation. This capability enables differentiation between tissues based on their molecular composition, offering a unique advantage in characterizing skin structure and pathology.¹⁹ MPM and RCM have been integrated into a commercially available handheld device. However, the system is limited to vertical cross-sectional imaging and has a relatively small field of view (400 µm × 300 µm).²⁰ A recently developed MPM-based imaging device enables tissue visualization at a macroscopic scale (ranging from millimeters to centimeters) with subcellular resolution (0.5 µm⁻¹). The device can perform second harmonic imaging and time-resolved fluorescence imaging, enabling the detection of endogenous fluorophores such as melanin, elastin fibers, collagen, and keratin.²¹ A key limitation of MPM,
however, is its relatively shallow penetration depth compared with interferometric techniques such as OCT and LC-OCT.

Although LC-OCT offers resolution comparable to RCM and MPM, its clinical value could be enhanced by the addition of molecular information to the three-dimensional morphological images. One approach is to couple LC-OCT with confocal Raman microspectroscopy (CRM), a technique that exploits the Raman effect by measuring the spectrum of inelastically scattered light to identify vibrational modes of chemical bonds, thus providing a molecular "fingerprint" of the tissue.<sup>22,23</sup> With its confocal setup, CRM enables point measurements in skin tissues, which has proved useful for guiding skin cancer surgery,<sup>24,25</sup> as well as for both *ex vivo*<sup>26-30</sup> and *in vivo* diagnosis.<sup>31-33</sup> A platform for colocalizing LC-OCT images with CRM measurements has been developed previously.<sup>22</sup> It has been used for characterizing tattoo composition in skin and inflammatory cells of adverse tattoo reaction,<sup>22</sup> but also on BCC,<sup>34</sup> for which differences in Raman spectra could be qualitatively observed between BCC lobules, epidermis, and dermis. This platform was limited to *ex vivo* imaging and needed precise calibration subject to drift over time, making its clinical integration complex. To solve this problem, integrating the two modalities in a single device would greatly simplify the co-localization of LC-OCT and CRM. Several devices have already been developed that integrate imaging with Raman spectroscopy,<sup>35</sup> including OCT with Raman spectroscopy,<sup>36,37</sup> OCT with CRM,<sup>38</sup> full-field OCT with CRM,<sup>39</sup> RCM with CRM,<sup>40,41</sup> RCM, MPM with CRM,<sup>42</sup> and autofluorescence microscopy with CRM.<sup>43</sup> Nevertheless, none of these devices provided 3D images compatible with *in vivo* use.

In this paper, we report on a device combining LC-OCT and CRM, compatible with both *in vivo* and *ex vivo* imaging. We present the first large-scale study of Raman spectroscopy guided by cellular-level 3D imaging on *ex vivo* specimens, using a compact probe under conditions compatible with *in vivo* use. The results summarize the imaging and molecular analysis of over 330 skin surgeries: more than 1300 Raman spectra from two types of nonmelanoma skin cancer acquired in 1 year.

As Raman signature illustrates differences in chemical bonds, and as the latter are quite similar across all the molecules contained in biological tissues, only subtle and weak modifications are recorded. These changes in Raman signature need data processing to be identified,<sup>44,45</sup> and they do not give direct readable information to the practitioner. Several approaches have been investigated to exploit the chemical information contained in Raman spectra, including using spectra of known components to analyze their contributions to a Raman spectrum and identify its composition,<sup>46,47</sup> or classifying Raman spectra by assigning them to pre-identified categories, using metrics of specific Raman peaks identified as relevant chemical bonds,<sup>43,48</sup> or using machine learning (ML) or deep learning (DL) techniques.<sup>49,50</sup> In the context of skin cancer, DL and ML were used to differentiate Raman spectra of normal versus tumor cells<sup>39,51</sup> and to identify the chemical fingerprint for melanoma versus pigmented nevus classification using explanatory modeling.<sup>52</sup> In clinical settings, DL was applied to data obtained from a portable Raman and autofluorescence system used on *in vivo* patients to perform binary classification of malignant versus benign skin lesions based on convolutional neural networks (CNN),<sup>53</sup> showing good performance, i.e., a receiver operating characteristic area under the curve (AUC) of 0.96 for differentiation of malignant versus benign tumors as well as melanomas versus pigmented tumors and melanomas versus seborrheic keratosis. Most ML and DL models require large datasets for effective training. To overcome data limitation, DL precisely generative adversarial networks have been used to generate synthetic samples for data augmentation, which has been shown to improve the performance of both DL and ML models.<sup>53,54</sup>

In the case of skin cancer, it is difficult to apply DL and ML to classify specific, in-depth structures of cancerous lesions if the CRM system is not assisted by 3D image guidance to identify structures in which CRM acquisitions are performed, which is necessary to establish the training dataset. Here, we present a DL model that enables skin cancer structures to be differentiated at the cellular level, directly from freshly excised unprocessed skin tissue. We show that it is possible to accurately classify Raman spectra based on the areas from which they were acquired under LC-OCT image guidance. The DL algorithm achieves an AUC of 0.95 in differentiating for histology-proven BCC lesions between healthy epidermis (EPI-H), epidermis above BCC lobules, healthy dermis (DER-H), and BCC lobules. This high AUC indicates that
molecular information can accurately and noninvasively be probed for different targets within skin cancer specimens.

In addition, we propose a method to identify the spectral bands responsible for the distinctions made by the DL model. This approach generates heatmaps that weigh the impact of each wavenumber on differentiation, resulting in attention scores. During biochemical attribution, high attention scores align with the known specific characteristics of each tissue type.

# 2 Materials and Methods

## 2.1 Instrumentation

The device, integrated into a handheld probe (Fig. 1), comprises three coupled systems: an LC-OCT imaging system, a confocal Raman microspectrometer, and a color surface imaging system. The three systems share the same microscope objective to collect light from the object, thus being intrinsically co-located. LC-OCT and microscopy images are displayed in real time. A Raman spectrum can be acquired at a given point of interest within the field of view of the images.

<figure><img src="image_3.png" /><figcaption>**Fig. 1** Diagram of the coupled LC-OCT/CRM device and its associated sample holder. BS1: Broadband cube beamsplitter (50R:50T), BS2: Broadband plate beamsplitter (20R:80T), F1: Bandpass filter at 785 nm, F2: Longpass filter at 785 nm, F3: Shortpass filter at 600 nm, DBS1: Longpass dichroic beamsplitter at 790 nm, DBS2: Longpass dichroic beamsplitter at 770 nm, OF1: Single mode optical fiber, OF2: Multimode optical fiber. L1: Cylindrical lens, L2, L3: Achromatic doublets, L4 + L5: Afocal optical system, L6: Micro-lens with autofocus, GM: Galvanometer.</figcaption></figure>
The LC-OCT imaging system is based on a Linnik-type interferometer with two identical microscope objectives (Olympus, UMPLFLN 20XW, Shinjuku City, Tokyo, Japan). A super-continuum laser (NKT photonics, Whitelase micro, Birkerød, Hovedstaden, Denmark) is used as a broadband light source at a detected central wavelength of about $\lambda_0 = 800$ nm. The beam at the output of a single mode optical fiber (OF1, Thorlabs PM780-HP, Newton, New Jersey, United States) is collimated by a reflective collimator based on a 90 deg off-axis parabolic silver mirror (Thorlabs RCR50A-P01). A cylindrical lens (L1, Edmund 34-648) is employed to generate a line of light focused on the focal planes of the microscope objectives. This line is imaged on the sensor of a line-scan camera (Teledyne e2v, Octoplus, 2048 pixels along the x direction) using a 150-mm focal length achromatic doublet (L2, Thorlabs AC254-150-B). The broadband beam-splitter of the interferometer (BS1, Thorlabs BS017) and the microscope objective in the object arm are mounted on a piezoelectric lead zirconate titanate-driven (PZT) linear stage (Physik Instrument, P625.1CD, Auburn, Massachusetts, United States) for depth (z direction) scanning, whereas a 20% reflectivity mirror (BS2, Edmund Optics 68-370) mounted on a galvanometer (GM, Thorlabs QS7) is placed in the object arm for lateral (y direction) scanning. The focal plane of the microscope objective in the reference arm coincides with the outer side of a 500 µm-thick calcium fluoride (CaF₂) window, thus constituting a low reflectivity (~3%) reference surface. The window is mounted on a PZT chip (PI PICMA PD080) that oscillates to generate a phase modulation as required to acquire the LC-OCT horizontal section images.⁵⁵ The microscope objectives are immersed in silicone oil whose refractive index ($n = 1.4$) is close to that of skin tissues. The LC-OCT imaging system provides vertical and horizontal section images in real time at a rate of 3 Hz, covering a field of view of 1.0 mm × 0.4 mm ($x \times z$) in the vertical imaging mode, and 1.0 mm × 0.5 mm ($x \times y$) in the horizontal imaging mode. The acquisition speed is reduced by a factor of ~3 compared with the conventional LC-OCT setup due to the use of a 20% reflectivity mirror in the LC-OCT path, which limits the amount of light collected from the tissue. With the laser power already set to its maximum, the camera's integration time is increased to maximize light collection, further limiting acquisition speed. Compared with conventional LC-OCT, the signal-to-noise ratio is only slightly lower and inferior, three-dimensional images can be acquired, with the same quasi-isotropic resolution of ~1 µm, but over a field of view reduced to 1.0 mm × 0.5 mm × 0.25 mm ($x \times y \times z$).

The Raman microspectrometer is an in-house developed system, using a continuous-wave (CW) laser emitting at the wavelength of 785 nm (Sacher Lasertechnik, Micron Cheetah, Marburg, Germany). The beam at the output of a single-mode optical fiber (OF1, Thorlabs PM780 HP) is collimated using an off-axis parabolic mirror (Thorlabs RCR50A-P01). A sharp band-pass filter at 785 nm (F1, Semrock LL01-785-12.5) removes parasitic wavelengths. The beam is focused on the sample using the same microscope objective as the one employed in the LC-OCT imaging modality. The Raman signal is collected by the same objective. For both excitation and collection, the beam also goes through the beamsplitter BS2, having a transmission of 80%. After collection, the beam is separated from the excitation beam using a long-pass dichroic beamsplitter at 790 nm (DBS1, Chroma RT785RDC). A long-pass edge filter at 785 nm (F2, Semrock LP02-785RU-25) avoids the detection of residual excitation. The Raman signal is focused into a multimode optical fiber (OF2, Thorlabs FG050LGA) using an achromatic doublet (L3, Thorlabs AC127-075-B). The optical fiber both guides the Raman signal to a spectrometer and serves as a confocal aperture. The Raman signal is thus acquired at a point of interest with an estimated resolution of ~1 µm × 1 µm × 30 µm ($x \times y \times z$). The size of this probed volume results from a compromise between the intensity and spatial resolution of the Raman signal. The spectrometer (Ocean Insight, QE Pro-Raman+, Largo, Florida, United States) has a spectral resolution of 11 cm⁻¹. The optical power delivered to the sample is 50 mW. Raman spectra are recorded in a range from 0 to 3000 cm⁻¹, with an integration time of 20 s. Raman spectra used in the study consist of the mean spectra of three successive acquisitions. A previous study showed that an acquisition time of 1 min provided a good signal-to-noise ratio (SNR) enabling relevant chemical identification of structures in the skin.²² The LC-OCT laser is turned off during Raman acquisition not to generate parasite light collected by the fiber leading to the spectrometer. Each time the sample is changed, and a new series of Raman acquisitions is performed; a first Raman acquisition with the Raman laser switched off is performed to get a spectrum used for background correction of subsequent Raman spectra.
A color surface imaging system is integrated into the system to provide color images of the skin surface. A ring of five white light-emitting diodes (LEDs) is placed around the microscope objective in the object arm of the LC-OCT system to illuminate the skin. Light reflected from the skin surface is collected by the microscope objective, goes through the 80% transmission beamsplitter BS2 as the Raman beam, and is separated from it by a long-pass dichroic mirror reflecting the wavelength below 770 nm. An afocal optical system (L4 + L5) projects the surface image on the area sensor of a color camera equipped with an autofocus micro-lens (IDS Imaging Development Systems, uEye XS, Obersulm, Germany). A short-pass filter at 600 nm (F3, 84-697 Edmund) eliminates most of the stray light from the supercontinuum laser, allowing the surface images and the LC-OCT images to be acquired simultaneously. The surface image has a spatial resolution of 5 µm and covers a field of view of 2.5 mm in diameter. The LEDs are turned off during Raman acquisition.

Custom software displays the images and allows for Raman acquisitions. Raman spectra are acquired at a fixed point (x, y) in a horizontal LC-OCT image. A graphic marker (cross) is displayed on the image to indicate the position of the point where a Raman acquisition can be performed. The depth (z) of Raman acquisition is the same as the depth at which the horizontal LC-OCT image is acquired, controlled by the PZT stage. Similarly, graphic markers are displayed on the surface image to indicate both the region imaged by LC-OCT and the point targeted by the Raman modality. To obtain a Raman spectrum from a given structure, for example, a structure identified within a 3D volume acquisition, the structure must be brought by the user at the position of the point indicated by the graphic marker, and the depth must be adjusted by the user to visualize the structure while imaging in the LC-OCT horizontal mode.

The probe is integrated into a module equipped with a motorized two-axis scanning stage (EK 75 × 50 Pilot, Märzhäuser Wetzlar), allowing easy positioning of the sample placed on a sample holder attached to the motor, including a manual translation stage (SM1ZM, Thorlabs) for height adjustments (Fig. 1). The top of the sample holder is covered with a 500 µm-thick plate of Raman-grade CaF₂ (Crystran Ltd, Poole, United Kingdom). Raman-grade CaF₂ was chosen because its Raman signature consists of a single Raman peak at 321 cm⁻¹, minimizing the background Raman signal superimposed on the Raman spectra of skin.²² The sample is positioned under the CaF₂ window using paraffin oil for optical contact and pressed onto it by means of a second plate held to the first one using magnets. Biopsy foam pads are positioned under the sample to avoid altering it. Silicone oil is then positioned between the CaF₂ plate and the microscope objective. Note that the probe can be easily modified for *in vivo* acquisition by simply adding a tank filled with silicone oil with a CaF₂ window in contact with the skin.

## 2.2 Protocol

Measurements were carried out *ex vivo* on fresh surgical specimens taken from patients at the dermatology department of Saint-Etienne University Hospital, France, between May 2023 and August 2024. The study was conducted with the informed consent of patients and with the approval of the Research Ethics Committee (IRBN522023/CHUSTE).

Each surgical specimen was analyzed immediately after excision and after being cleaned of blood using physiological fluid. The specimen was placed in the sample holder positioned under the LC-OCT Raman probe (Fig. 1). Optical contact between the CaF₂ window and the skin sample was achieved using a very thin layer of paraffin oil. The sample was oriented with its surface facing the CaF₂ window. A plate was added under the sample, and its position was adjusted using a retaining magnet to gently press the sample against the upper window, thereby preventing any sample displacement during experiments. Silicone oil was added to the top of the sample holder as an immersion medium. Silicone oil has a refractive index of 1.4, close to the refractive index of skin, which ensures that the coherence plane and the focal plane of the objective are superimposed during depth scanning, thus maintaining optimum resolution and signal at all depths. Regarding CRM acquisition, silicone oil is far enough from the focal volume of the microscope objective not to parasite the Raman signal, and the layer of paraffin oil on the sample is so thin that it does not affect either the Raman signal. For every specimen, before applying the imaging protocol, a CRM acquisition was performed with the CRM laser turned off for background subtraction in Raman spectra.
<table><caption>**Table 1** Number of specimens and spectra analyzed for each lesion type and target.</caption><thead><tr><th>Lesion</th><th>Number of lesions</th><th>Target</th><th>Number of spectra</th></tr></thead><tbody><tr><td rowspan="4">BCC</td><td rowspan="4">247</td><td>EPI-T</td><td>253</td></tr><tr><td>LOB-T</td><td>364</td></tr><tr><td>EPI-H</td><td>236</td></tr><tr><td>DER-H</td><td>216</td></tr><tr><td rowspan="2">SCC</td><td rowspan="2">85</td><td>TUM-T</td><td>219</td></tr><tr><td>EPI-H</td><td>94</td></tr><tr><td>Total lesions</td><td>332</td><td>Total spectra</td><td>1382</td></tr></tbody></table>

For each specimen, a 3D LC-OCT image was acquired in a region presenting tumor criteria. Raman spectra were then acquired at several points of interest (POI) in this region. The targeted POI depends on the type of lesion. For BCC, the targets are the epidermis above the tumoral epidermis (EPI-T) and the BCC tumor lobules (LOB-T). For SCC, the target is the tumor (TUM-T), i.e., the cancerous keratinocytes within the epidermis. Raman spectra were also acquired remotely in healthy regions of the epidermis (EPI-H) and dermis (DER-H). The healthy part corresponds to the margins of the surgical specimen. Six spectra are acquired for each surgical specimen. The specimens were then sent for histopathological analysis for confirmation of the cancer type. For histopathologically confirmed lesions, a *posteriori* verification of the POI probed with CRM within the 3D LC-OCT images was performed by an experienced dermatologist to confirm that the Raman spectra come from POI fully included in the associated target.

The data have been acquired from lesions of 332 patients, including 213 males and 119 females with a median age of 77 years old (range: 30 to 96) and 81 years old (range: 44 to 99), respectively. Of the 332 lesions, 247 cases were BCC and 85 SCCs. All these lesions were clinically confirmed by the experienced dermatologists and by histopathology. On 247 BCC surgical specimens, a total of 1069 Raman spectra were selected: 364 LOB-T, 253 EPI-T spectra, 236 EPI-H spectra in the *in sano* zone and 216 DER-H spectra in the *in sano* zone. On 85 SCC surgical specimens, a total of 313 Raman spectra were selected: 219 TUM-T spectra and 94 EPI-H spectra in the *in sano* zone. Table 1 summarizes the number of specimens and spectra analyzed for each lesion type and target. Figure 2 presents examples of 3D LC-OCT images with targets for CRM acquisition, for the two types of lesions analyzed, i.e., BCC and SCC.

## 2.3 Raman Spectra Processing

To extract the most relevant Raman features from the raw data and minimize the impact of measurement artifacts, a preprocessing procedure was optimized. After CRM acquisition, all spectra were divided into three spectral ranges (800 to 1350, 1350 to 1750, and 2800 to 3000 cm⁻¹), and the following preprocessing steps were applied:

1. extension of the range by 50 cm⁻¹ at both ends to avoid edge effects

2. baseline correction using polynomial fitting of order 5 for the first two ranges and 1 for the last range with asymmetric quadratic function with tolerance 0.01 as cost function⁵⁶

3. removal of the extended margins added in step 1

4. standard normal variate (SNV) normalization

5. application of Savitzky-Golay filter (second order, window size 13 points).

After applying this preprocessing method to the three ranges separately, the data were concatenated to form the final input data to the models described below. The average spectra for all of the different targets are shown in Fig. S1 in the Supplementary Material. Shaded areas indicate the standard deviation of all spectra. The average spectrum of all targets combined is also shown on each graph, to reveal deviations in the spectrum of a given target and highlight its particularities.
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Example of 3D LC-OCT images of different CRM targets: (a) for basal cell carcinoma (BCC) at the BCC tumor lobule (LOB-T), and (b) for squamous cell carcinoma (SCC) at the tumor (TUM-T). The point probed by CRM is represented by the red cross on the images. The circular images to the left of the 3D LC-OCT images represent the surface image of the surgical specimens.</figcaption></figure>

## 2.4 Artificial Intelligence Models and Spectral Attention Score

Artificial intelligence (AI) models have been developed to classify the different targets analyzed by CRM. The performance of standard ML methods was compared with a simple 1d-CNN. The ML models evaluated were: XGBoost⁵⁷ with 100 estimators and a maximum depth of 5; k-Nearest Neighbors (k-NN) with 10 neighbors and Euclidean distance; multilayer perceptron (MLP) with hidden layers of sizes 32 and 16; and support vector machine (SVM) with a radial basis function (RBF) kernel. The CNN consists of three convolution layers (kernel sizes: 7, 3, and 3; strides: 2; features: 16, 32, and 64), each followed by group normalization⁵⁸ with four groups and rectified linear unit (ReLU) activation. The features are then flattened and passed through three fully connected layers (output features: 256, 64, and 5) with batch normalization⁵⁹ and ReLU activation.

Following standard practice, a fivefold cross-validation scheme was employed. The folds were stratified by labels, with patients grouped to ensure no individual appeared in both training and validation sets. Two types of multiclass models were developed, with four classes for BCC targets and five for BCC and SCC targets, as described in Sec. 2.2.
<figure><img src="image_5.png" /><figcaption>Fig. 3 Visual flowchart summarizing the protocol and its outputs.</figcaption></figure>

To identify key features in the input spectra influencing class attribution, we used the AI model to generate spectral attention scores from Raman spectra. These scores highlight spectral regions relevant for classifying targets. Raman band shifts or intensity changes, linked to protein and lipid content, can indicate metabolic changes and potential skin cancer biomarkers.⁶⁰ We applied an occlusion method to refine this process, replacing input regions with zeros and measuring output variation.⁶¹ Regions causing the greatest variation were deemed most important. This was performed using a sliding window of size 13 and stride 5. The absolute variations were normalized to a 0 to 1 scale using min-max normalization.

Note that this was computed for all out-of-fold samples for all folds and then averaged to yield robust estimates of the attention scores. To quantitatively assess consistency across folds, we computed the pair-wise cosine similarity between the average attention score vectors for all pairs of folds, for each target class. We then averaged these values to obtain a similarity score for each class. The values that we obtained are shown in Table S1 in the Supplementary Material. These high values testify to the consistency of the attention scores considered.

The flowchart in Fig. 3 summarizes the experimental workflow from data acquisition to interpretation of results.

# 3 Results

## 3.1 AI Performance

We show in Table S2 in the Supplementary Material the performance (AUC) of the different AI methods described in Sec. 2. AUC values for multiclass models correspond to the average of the class-wise receiver operating characteristic AUC values from binary “one-vs-rest” classification tasks. The best-performing method, the 1d-CNN model, was selected for further analysis.

For more details about 1d-CNN models performance, Fig. S3 in the Supplementary Material presents confusion matrices for each one-versus-rest task, and Table S3 in the Supplementary Material shows key performance metrics (AUC, recall, sensitivity, specificity, accuracy, and precision) computed with 1d-CNN models. Confusion matrices for multiclass setups are built using the maximum score's class and built using a threshold of 0.5 for binary setups.

For BCC targets, we obtain an AUC of 0.95. The associated confusion matrix is given in Fig. 4(a). The confusion matrix shows the performance of the model, indicating for a given target what the model predicted, both in absolute value (number of spectra) and as a proportion of all spectra for the given target. For example, 91.2% of the LOB-T spectra (332 spectra) were correctly predicted as LOB-T, whereas 8.8% (32 spectra) were incorrectly labeled, 4 being labeled as EPI-T, 11 as EPI-H, and 17 as DER-H. Note that the EPI-H and DER-H are limited to acquisitions for BCC lesions. The model best performs for discrimination of BCC lobules and DER-H. Only 13.4% (29) of DER-H samples were misclassified as LOB-T. This confusion can be due to
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Confusion matrix associated with the 1d-CNN models developed for (a) BCC targets and (b) BCC and SCC targets. Normalization is performed row-wise. Each cell shows the raw count along with the proportion of samples predicted as that class among all samples with the given ground truth class (in parentheses).</figcaption></figure>

close spectral signatures, linked to similar biochemical components (lipids, proteins such as collagen: amide I at ~1650 cm⁻¹, proline/hydroxyproline at ~850 to 950 cm⁻¹) or spatial overlap between dermis and LOB-T.⁶²,⁶³ This confusion can also be explained by the presence of identical deoxyribonucleic acid/ribonucleic acid (DNA/RNA) despite different organizations or by partial overlap of the analyzed areas. Regarding the epidermis, 26.1% of the spectra of the epidermis in the tumor area were predicted as EPI-H, and conversely, 13.1% of the spectra of EPI-H were predicted as epidermis in the tumor area. Nevertheless, 69.2% (resp. 79.7%) of spectra of epidermis in the tumor area (resp. EPI-H) were correctly identified by the model. It is important to note that EPI-H exhibits the same morphological features as the epidermis in the tumor area, showing an interest in chemical characterization using CRM.

For BCC and SCC targets, we obtain an AUC of 0.92. The associated confusion matrix is shown in Fig. 4(b). The introduction of the SCC targets (including TUM-T and EPI-H) marginally affected the performance of the model in identifying BCC lobules (87.6% of correct predictions) and DER-H (81.9% of correct predictions) compared with the model developed for BCC alone. For this model, EPI-H targets from SCC were merged with EPI-H targets from BCC in a single EPI-H class. It is interesting to note that the performance for identification of EPI-H was slightly lower in this model (76.1% versus 79.7% in the model developed for BCC), whereas it was degraded for the epidermis in the BCC area, which becomes more prone to confusion with cancerous SCC epidermis (17.4% of the EPI-T spectra become predicted as TUM-T). Conversely, TUM-T is prone to confusion with EPI-T (25.6% of TUM-T spectra are predicted as EPI-T). TUM-T and particularly EPI-T are prone to confusion with EPI-H in this model. As observed for BCC, distinguishing between the different types of epidermis (healthy, SCC, and in the area of BCC) is confirmed as not being straightforward, despite the decent performance of the model. Of the SCC tumor (TUM-T) samples, 14.2% (31) were misclassified as EPI-H.⁶⁴ Well-differentiated SCCs produce keratins and proteins similar to EPI-H (amides I/III ~1650, ~1240 cm⁻¹),⁶⁵ making their Raman signatures virtually identical and complicating their distinction. This biochemical and spectral similarity between healthy and tumoral keratinocytes explains the difficulties of diagnosis by Raman spectroscopy, particularly for low-grade SCCs, which retain an architecture close to that of healthy tissue.⁶⁴

## 3.2 Biochemical Analysis

The chemical assignments of the Raman bands can indicate the presence of specific biomolecules that serve as potential biomarkers for skin cancer. Shifts in band positions or changes in intensities can be associated with protein and lipid content, reflecting underlying metabolic changes.⁶⁰ By analyzing the average Raman spectra from the different lesion types and targets, combined with the spectral attention scores generated from the AI model, distinct spectral regions and chemical compositions that differentiate healthy from cancerous tissues were identified. Figure 5 also displays the average spectrum of all the targets combined (orange dashed curve), to locate
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Heatmaps of attention scores for Raman spectra of: the BCC tumor lobules (label LOB-T), the epidermis above BCC tumor (label EPI-T), the healthy epidermis (label EPI-H), the healthy dermis (label DER-H), and the tumor of the SCC (label TUM-T). Annotations A to G (in red) highlight areas of interest. The orange dashed curve represents the mean spectrum of all the targets combined.</figcaption></figure>

deviations in the spectrum of a given target, pointing out the particularities of that spectrum. Shaded areas indicate the standard deviation of all spectra to show the measured consistency across multiple patients and measurements. The assignment of bands was conducted based on an extensive literature review. The vibrational groups and modes were associated with specific skin components, as detailed in Table 2. An exhaustive listing of the correspondence between all the group and vibrational modes and their associated biochemical assignments in the skin, obtained from the literature review, is included in Table S4 in the Supplementary Material.

Regarding BCC lobules (LOB-T), the upper part of the band centered around $1092 \text{ cm}^{-1}$ (annotated A Fig. 5), corresponding to the symmetric stretching of $PO_2$ from DNA, appears essential for classification. The $1025$ to $1165 \text{ cm}^{-1}$ region, a spectral region of interest for DNA,$^{47,71}$ was extracted from the average spectra of each target, deconvoluted and integrated, as shown in Figs. S2(A)–S2(F) in the Supplementary Material. Deconvolution was performed using Orange Data Mining software version 3.34.0, with the Spectroscopy 0.6.9 Add-on. Gaussian fitting components were applied for deconvolution to minimize the reduced chi-square value.
<table><caption>**Table 2** Biochemical assignments of the average spectrum based on literature. $\nu$, stretching; $\nu_s$, symmetric stretching; $\nu_{as}$, antisymmetric stretching; $\delta$, bending; $\tau$, twisting.</caption><thead><tr><th>Raman shift (cm<sup>-1</sup>)</th><th>Discriminations</th><th>Group and vibrational mode</th><th>Biochemical assignments</th><th>References</th></tr></thead><tbody><tr><td>821</td><td>SCC epidermis (B)</td><td>ν(C–C)</td><td>Proline and hydroxyproline (collagen)</td><td>66, 67</td></tr><tr><td>1001</td><td>SCC epidermis (C)</td><td>ν<sub>s</sub>(C–C)<br>Ring breathing</td><td>Phenylalanine (collagen)</td><td>66, 68–70</td></tr><tr><td>1092 to 1109</td><td>BCC lobules (A)</td><td>ν<sub>s</sub>(O–P–O)</td><td>DNA backbones (nucleus)</td><td>47, 71</td></tr><tr><td>1450</td><td>SCC epidermis (E)</td><td>δ<sub>scissoring</sub> (CH)</td><td>Proteins (collagen), lipids</td><td>48, 49, 72–75</td></tr><tr><td>1573</td><td>SCC epidermis (F)</td><td>δ(N–H), ν(C–N)</td><td>Nucleic acids DNA</td><td>25, 66, 76, 77</td></tr><tr><td>2930</td><td>SCC epidermis (G)</td><td>ν<sub>s</sub>(CH<sub>3</sub>)</td><td>Mostly keratin</td><td>78, 79</td></tr></tbody></table>

Notably, a component centered at 1089 cm⁻¹ showed significant variability, with the integration value (AUC<sub>1089</sub>) 70% higher than that of EPI-H. This finding aligns with the increased DNA content associated with the high proliferative activity observed in BCC cells.<sup>68,80</sup> In addition, the BCC lobules category exhibited a small component at 1092 cm⁻¹ that was absent in the other categories; however, the corresponding vibrational group was not identified.

Regarding SCC tumor (the TUM-T category), six discriminant regions were identified centered at 821, 1012, 1220, 1446, 1580, and 2931 cm⁻¹, labeled from B to G (Fig. 5), respectively. Most of these regions (Fig. 5 B, C, E, and G) are primarily linked to proteins, particularly collagen and keratin. These peaks exhibit higher-than-average intensity, suggesting an over-expression of proteins. The high wavenumber region around 2931 cm⁻¹ (Fig. 5 G) was further deconvoluted into five separate components. The specific component corresponding to the highest attention score is related to the symmetric stretching of CH₃ groups, which is linked to keratin.<sup>78</sup> The integration value AUC<sub>2931</sub> for SCC epidermis was 44% higher than that of EPI-H, aligning with previous findings that indicate SCC contains higher levels of keratin, ceramides, and water compared with healthy skin.<sup>68,80</sup> This keratin attribution is further supported by the lower AUC<sub>2931</sub> values observed in BCC lobules and DER-H, both of which are less dense in keratinocytes.<sup>79</sup>

Lipid content is a key indicator of differentiation, varying according to the specific skin layer and overall skin homeostasis. Lipid content was estimated by comparing the sum of areas of two lipid-attributed components centered at 2855 cm⁻¹ (width: 30 cm⁻¹) and 2880 cm⁻¹ (width: 20 cm⁻¹) (AUC<sub>2840–2870</sub> and AUC<sub>2870–2890</sub>) to those attributed to keratin, centered at 2930 and 2955 cm⁻¹ (both with a width of 20 cm⁻¹) (AUC<sub>2920–2940</sub> and AUC<sub>2945–2965</sub>) according to the formula: Lipid content = $\frac{AUC_{2840–2870} + AUC_{2870–2890}}{AUC_{2920–2940} + AUC_{2945–2965}}$, as described by Li et al.<sup>69</sup>

The results, illustrated in Fig. S2(G) in the Supplementary Material, indicate higher lipid concentrations in the epidermis (healthy, SCC, or BCC) and lower concentrations in BCC lobules and DER-H. These estimations are also consistent with the expected lipid distribution across the different skin layers.

The spectral attention scores generated based on the AI model highlighted specific spectral areas that were closely aligned with the compositional differences among tissue categories, indicating a nonrandom and targeted correlation.

## 4 Discussion and Conclusion

We have presented an innovative device combining LC-OCT and CRM to characterize nonmelanoma skin cancers. The device, integrating three channels to provide LC-OCT imaging, CRM, and color surface imaging, is compatible with both *in vivo* and *ex vivo* imaging while retaining the overall performance of the LC-OCT and CRM modalities. A specific *ex vivo* module with a sample holder adapted for both LC-OCT and CRM has been introduced. In this first large-scale
ex vivo study of 3D image-guided Raman spectroscopy at the microscopic scale, we used a compact probe on fresh, unprocessed ex vivo skin tissues. Although the device is compatible with in vivo imaging, the current relatively long acquisition time for Raman spectra (1 min) makes it difficult to carry out in vivo studies. A future study could compare the performance of the AI model with reduced acquisition times, collecting more BCC spectra.

Measurements were carried out on fresh surgical specimens of different types of skin cancer taken from 332 patients at the dermatology department of Saint-Etienne University Hospital, France. The processing and analysis of a large number of Raman spectra (1382 Raman spectra acquired over 1 year) required the development of processing tools capable of retaining the most relevant information from the raw data. For this reason, we have introduced a DL algorithm to differentiate skin cancer structures at the cellular level. Our results show that it is possible to accurately classify Raman spectra according to the areas from which they were acquired under LC-OCT image guidance. We also used a method to identify the spectral bands responsible for the distinctions made by the AI model. This approach can generate heat maps showing the impact of each wavenumber on differentiation based on attention scores. During biochemical assignment, high attention scores align with the known specific characteristics of each tissue type.

To validate our model, an initial analysis was carried out on the Raman spectra of the BCC targets (EPI-T, LOB-T, EPI-H, and DER-H). These targets are well distinguished thanks to the morphological structure of the BCC, which is well defined when imaged using LC-OCT.⁸¹ For BCC, the DL model achieved an AUC of 0.95 to differentiate between EPI-H, epidermis above BCC lobules, and DER-H and BCC lobules. This high AUC indicates that molecular information can be probed accurately and noninvasively for different targets in skin cancer samples. This analysis showed that it is possible to discriminate correctly between BCC lobules and DER-H, whereas it was less obvious to discriminate between EPI-T and EPI-H. Of epidermal spectra in the tumor zone, 24.9% were predicted as EPI-H, and conversely, 14.8% of healthy epidermal spectra were predicted as epidermis in the tumor zone. This could be attributed to a molecular content more similar between the EPI-H and the tumor epidermis than between the lobules or the tumor dermis. This could also be linked to the fact that EPI-H is sometimes part of the cancerization process and therefore difficult to discriminate from epidermis at the tumor level in those cases. Nevertheless, 69.2% (resp. 79.7%) of the spectra of the epidermis in the tumor zone (resp. EPI-H) were correctly identified by the model, whereas it should be noted that the epidermis has the same LC-OCT morphological appearance in those two cases. Given the labeling uncertainty—which is difficult to fully eliminate—there is a potential for model bias, particularly a reduced sensitivity in tumor detection if areas undergoing early cancerization are labeled as healthy. To mitigate this, methods such as label smoothing or pseudo-labeling on identified noisy-labeled samples could be explored.

The chemical assignment of Raman bands indicating the presence of specific biomolecules that serve as potential biomarkers of skin cancer shows that for BCC lobules, the upper part of the band centered around 1092 cm⁻¹ corresponding to the symmetrical stretching of PO₂ DNA, appeared essential for classification. The component centered at 1089 cm⁻¹ showed significant variability, with an integration value (AUC₁₀⁸⁹) 70% higher than that of EPI-H. This result was consistent with the increased DNA content associated with the high proliferative activity observed in BCC cells.⁶⁸,⁸⁰

BCC served as a relevant proof-of-concept to show that the introduced system yields accurate chemical characterization based on Raman spectra when used in conjunction with a DL model. Nevertheless, LC-OCT alone has been shown to perform very well for accurate diagnosis and subtyping of BCC,⁸² whereas Raman characterization would be more relevant to provide an additional layer of chemical subtyping, which could for example be relevant for personalized treatment options to be explored in further studies.

When including SCC targets in addition to BCC targets, the model gave an AUC of 0.92. As in the case of BCC, the distinction between the different types of epidermis (healthy, SCC, and in the area of the BCC) was confirmed as difficult, which may be explained by the fact that the chemical composition of the epidermis does not change significantly between the different targets or that the changes are too subtle to be efficiently classified based on CRM. As for BCC, areas we considered healthy could also in fact be part of the cancerization process, leading to inaccurate labeling for the DL model. From a biomolecular perspective, six discriminant regions
were identified for the SCC epidermis centered at 821, 1012, 1220, 1431, 1585, and 2931 cm⁻¹. Most of these regions are mainly protein-related, especially collagen and keratin. These peaks are above average, suggesting overexpression of proteins. The region of the large number of waves around 2931 cm⁻¹ corresponding to the highest attention score is related to the symmetrical stretching of CH₃ groups, which is related to keratin. The AUC₂₉₃₁ integration value of SCC epidermis was 44% higher than that of EPI-H, which is consistent with previous results indicating that SCC contains higher levels of keratin, ceramides, and water compared with healthy skin.⁶⁸,⁸⁰ This allocation of keratin is further supported by the lower AUC₂₉₃₁ values observed in BCC lobules and DER-H, both of which are less dense in keratinocytes.⁷⁹

Even though our study comprises a large number of lesions, the number of spectra is limited by the need for annotation. Alternative approaches, such as semi-supervised learning—which combines carefully annotated spectra with large amounts of unlabeled spectra—could be explored in future work. For instance, Rui et al.⁸³ showed that leveraging unlabeled data can lead to improved accuracy compared with a fully supervised approach.

In conclusion, this new device combining LC-OCT and CRM, compatible with *in vivo* and *ex vivo* imaging, assisted by a dedicated AI model, proved capable of discriminating BCC structures, even for structures with similar morphological features. Encouraging results were obtained for SCC, although the differentiation of epidermal structures currently lacked accuracy. Considering the capability of the device for chemical differentiation of well-defined structures, in particular for BCC, future investigations using the developed system could in particular include chemical subtyping of BCC to obtain a new layer of information that could guide therapy options, or assist in developing them. Using the chemical information provided by CRM could also be useful for identifying the nature of unusual structures that could be considered BCC lobules on LC-OCT imaging examination. Finally, future technical developments should facilitate the use of the system *in vivo*, in particular by reducing the acquisition time of Raman spectra.

**Disclosures**

The authors declare no conflict of interest.

L. Waszczuk, J. Ogien, and G. Dauce were employed by DAMAE Medical. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

**Code and Data Availability**

The authors affirm that all data necessary for confirming the conclusions of the article are present within the article, figures, and tables.

**Author Contributions**

J.L.P., A.D., A.T., and J.O. conceptualized and designed the research. L.W., A.D., and J.O. developed the LC-OCT/Raman device. G.D. developed AI models. J.L.P. managed participant recruitment. M.A. performed the experiments and data collection. M.A., J.L.P., and J.O. conducted data curation. L.A., S.T., and A.T. performed the biochemical analysis. M.A., J.O., G.D., A.D., and L.A. wrote the original draft of the paper. All authors reviewed and edited the paper. All authors discussed and interpreted the results.

**Acknowledgments**

This research was funded by the French National Research Agency (Grant No. ANR-21-CE19-0056-01, Spec-LCOCT project).

We would like to express our gratitude to the academic health center of Saint-Etienne, France with the whole clinical team of Pr Jean-Luc Perrot's dermatology unit for their precious assistance in the realization of this pilot investigation.

The authors also wish to acknowledge the whole team of engineers at DAMAE Medical for technical support.
## References

1. S. J. Merrill et al., "Exponentially increasing incidences of cutaneous malignant melanoma in Europe correlate with low personal annual UV doses and suggests 2 major risk factors," *Dermatoendocrinol.* **7**(1), e1004018 (2014).

2. A. Lomas, J. Leonardi-Bee, and F. Bath-Hextal, "A systematic review of worldwide incidence of nonmelanoma skin cancer," *Br. J. Dermatol.* **166**, 1069-1080 (2012).

3. H. Sung et al., "Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," *CA Cancer J.* **7**(3), 209-249 (2021).

4. A. A. of D. Association, "Skin cancer," [Online], https://www.aad.org/media/stats/conditions/skin-cancer (2025).

5. D. Kerob et al., "54473 A comprehensive analysis of global skin cancer incidence and mortality with a focus on dermatologist density and population risk factors," *J. Am. Acad. Dermatol.* **91**(3), AB102 (2024).

6. A. M. Glazer et al., "Clinical diagnosis of skin cancer enhancing inspection and early recognition," *Dermatol. Clin.* **35**(4), 409-416 (2017).

7. A. Levine and K. Wang, "Optical coherence tomography in the diagnosis of skin cancer," *Dermatol. Clin.* **35**(4), 465-488 (2017).

8. A. Levine and O. Markowitz, "Introduction to reflectance confocal microscopy and its use in clinical practice," *JAAD Case Rep.* **4**(10), 1014-1023 (2018).

9. J. Monnier et al., "Combined reflectance confocal microscopy and optical coherence tomography to improve the diagnosis of equivocal lesions for basal cell carcinoma," *J. Am. Acad. Dermatol.* **86**(4), 934-936 (2022).

10. S. Michelini et al., "Combining reflectance confocal microscopy, optical coherence tomography and ex-vivo fluorescence confocal microscopy for margin assessment in basal cell carcinoma excision," *Dermatol. Pract. Concept.* **14**(2), e2024090 (2024).

11. A. Dubois et al., "Line-field confocal optical coherence tomography for high-resolution noninvasive imaging of skin tumors," *J. Biomed. Opt.* **23**(10), 106007 (2018).

12. J. Ogien et al., "Line-field confocal optical coherence tomography for three-dimensional skin imaging," *Front. Optoelectron.* **13**(4), 381-392 (2020).

13. J. Ogien et al., "Line-field confocal optical coherence tomography (LC-OCT): principles and practical use," *Ital. J. Dermatol. Venereol.* **158**(3), 171-179 (2023).

14. F. Latriglia et al., "Line-field confocal optical coherence tomography (LC-OCT) for skin imaging in dermatology," *Life* **13**(12), 2268 (2023).

15. M. Deußing et al., "Unveiling the hidden boundaries: AI-assisted line-field optical coherence tomography margin mapping for precise excision of basal cell carcinoma—a step-by-step tutorial," *Skin Res. Technol.* **30**(2), e13594 (2024).

16. K. Jacobsen et al., "Surgical planning with line-field confocal optical coherence tomography for recurrent infiltrative basal cell carcinoma: visualizing subclinical tumor for margin adjustment," *J. Dtsch. Dermatol. Ges.* **22**, 462-465 (2024).

17. T. K. Nikolouzakis et al., "Current and future trends in molecular biomarkers for diagnostic, prognostic, and predictive purposes in non-melanoma skin cancer." *J. Clin. Med.* **9**(9), 2868 (2020).

18. D. Didona et al., "Non melanoma skin cancer pathogenesis overview," *Biomedicines* **6**(1), 6 (2018).

19. K. Schenke-Layland et al., "Two-photon microscopes and in vivo multiphoton tomographs: powerful diagnostic tools for tissue engineering and drug delivery," *Adv. Drug Deliv. Rev.* **58**, 878-896 (2006).

20. K. L. Montgomery et al., "Handheld multiphoton and pinhole-free reflectance confocal microscopy enables noninvasive, real-time cross-sectional imaging in skin," *Sci. Rep.* **14**(1), 26129 (2024).

21. B. Torrado et al., "BPS2025—fast large-area multiphoton exoscope for label-free in vivo imaging of immune cells in human skin," *Biophys. J.* **124**(3), 159A (2025).

22. L. Waszczuk et al., "Co-localized line-field confocal optical coherence tomography and confocal Raman microspectroscopy for three-dimensional high-resolution morphological and molecular characterization of skin tissues ex vivo," *Biomed. Opt. Express* **13**(4), 2467-2487 (2022).

23. D. W. Shipp, F. Sinjab, and I. Notingher, "Raman spectroscopy: techniques and applications in the life sciences," *Adv. Opt. Photonics* **9**(2), 315-428 (2017).

24. R. A. Boitor et al., "Diagnostic accuracy of autofluorescence-Raman microspectroscopy for surgical margin assessment during Mohs micrographic surgery of basal cell carcinoma," *Br. J. Dermatol.* **191**(3), 428-436 (2024).

25. X. Feng et al., "Biophysical basis of skin cancer margin assessment using Raman spectroscopy," *Biomed. Opt. Express* **10**, 104-118 (2019).

26. J. Choi et al., "Direct observation of spectral differences between normal and basal cell carcinoma (BCC) tissues using confocal Raman microscopy," *Biopolymers* **77**, 264-272 (2005).

27. A. Nijssen et al., "Discriminating basal cell carcinoma from its surrounding tissue by Raman spectroscopy," *J. Invest. Dermatol.* **119**, 64-69 (2002).

28. C. A. Lieber et al., "Raman microspectroscopy for skin cancer detection in vitro," *J. Biomed. Opt.* **13**, 024013 (2013).
29. I. P. Santos et al., "Improving clinical diagnosis of early-stage cutaneous melanoma based on Raman spectroscopy," *Br. J. Cancer* **119**(11), 1339-1346 (2018).

30. G. Lazzini and M. D'Acunto, "Grading of melanoma tissues by Raman microspectroscopy," *Eng. Proc.* **51**(1), 10 (2023).

31. N. Kourkoumelis et al., "Advances in the in vivo Raman spectroscopy of malignant skin tumors using portable instrumentation," *Int. J. Mol. Sci.* **16**, 14554-14570 (2015).

32. C. A. Lieber and A. Mahadevan-Jansen, "Development of a handheld Raman microspectrometer for clinical dermatologic applications," *Opt. Express* **15**, 11874-11882 (2007).

33. I. P. Santosa et al., "Raman spectroscopy for cancer detection and cancer surgery guidance: translation to the clinics," *Analyst* **142**(17), 3025-3047 (2017).

34. L. Waszczuk et al., "A platform prototype for co-localized line-field confocal optical coherence tomography (LC-OCT) and confocal Raman microspectroscopy (CRM) acquisitions on ex vivo skin tissues," in *Opt. Coherence Imaging Tech. Imaging Scatt. Media V*, Optica Publishing Group, p. 1263210 (2023).

35. I. W. Schie et al., "Looking for a perfect match: multimodal combinations of Raman spectroscopy for biomedical applications," *J. Biomed. Opt.* **26**, 080601 (2021).

36. D. Bovenkamp et al., "Combination of high-resolution optical coherence tomography and Raman spectroscopy for improved staging and grading in bladder cancer," *Appl. Sci.* **8**(12), 2371 (2018).

37. A. F. Kukk et al., "Multimodal system for optical biopsy of melanoma with integrated ultrasound, optical coherence tomography and Raman spectroscopy," *J. Biophotonics* **15**(10), e202200129 (2022).

38. X. Ren et al., "Optical coherence tomography-guided confocal Raman microspectroscopy for rapid measurements in tissues," *Biomed. Opt. Express* **13**, 344-357 (2022).

39. C. You et al., "Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning," *J. Biomed. Opt.* **28**, 096005 (2023).

40. Z. Wu et al., "Precise in vivo tissue micro-Raman spectroscopy with simultaneous reflectance confocal microscopy monitoring using a single laser," *Opt. Lett.* **44**(6), 1383-1386 (2019).

41. C. A. Patil et al., "A handheld laser scanning confocal reflectance imaging—confocal Raman microspectroscopy system," *Biomed. Opt. Express* **3**, 488-502 (2012).

42. H. Wang et al., "A method for accurate in vivo micro-Raman spectroscopic measurements under guidance of advanced microscopy imaging," *Sci. Rep.* **3**(1), 1890 (2013).

43. R. Boitor et al., "Automated multimodal spectral histopathology for quantitative diagnosis of residual tumour during basal cell carcinoma surgery," *Biomed. Opt. Express* **8**, 5749-5766 (2017).

44. A. Rigal et al., "Raman confocal microscopy and biophysics multiparametric characterization of the skin barrier evolution with age," *J. Biophotonics* **14**(9), e202100107 (2021).

45. A. Rigal et al., "Skin surface lipid composition in women: increased 2,3-oxidosqualene correlates with older age," *Eur. J. Dermatol.* **30**, 103-110 (2020).

46. M. Ashtikar et al., "Non-invasive depth profile imaging of the stratum corneum using confocal Raman microscopy: first insights into the method," *Eur. J. Pharm. Sci.* **50**, 601-608 (2013).

47. X. Feng et al., "Raman active components of skin cancer," *Biomed. Opt. Express* **8**, 2835-2850 (2017).

48. E. Rimskaya et al., "Multi-wavelength Raman differentiation of malignant skin neoplasms," *Int. J. Mol. Sci.* **25**(13), 7422 (2024).

49. L. Silveira, Jr et al., "Normal-subtracted preprocessing of Raman spectra aiming to discriminate skin actinic keratosis and neoplasias from benign lesions and normal skin tissues," *Lasers Med. Sci.* **35**, 1141-1151 (2020).

50. K. E. Tomnikova and I. A. Matveeva, "Machine learning methods for classifying Raman scattering spectra of the skin," in *X Int. Conf. Inf. Technol. Nanotechnol. (INTN)*, Russian Federation, Samara, pp. 1-4 (2024).

51. X. Qiu et al., "Raman spectroscopy combined with deep learning for rapid detection of melanoma at the single cell level," *Spectrochim. Acta Part A Mol. Biomol. Spectrosc.* **286**, 122029 (2023).

52. D. Castro Araujo et al., "Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning," *Artif. Intell. Med.* **120**, 102161 (2021).

53. J. Zhao et al., "Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation," *Front. Oncol.* **14**, 1320220 (2024).

54. M. Wu et al., "Deep learning data augmentation for Raman spectroscopy cancer tissue classification," *Sci. Rep.* **11**(1), 23842 (2021).

55. J. Ogien et al., "Dual-mode line-field confocal optical coherence tomography for ultrahigh-resolution vertical and horizontal section imaging of human skin in vivo," *Biomed. Opt. Express* **11**, 1327-1335 (2020).

56. V. Mazet et al., "Background removal from spectra by designing and minimising a non-quadratic cost function," *Chemom. Intell. Lab. Syst.* **76**, 121-133 (2005).

57. T. Chen and C. Guestrin, "XGBoost: a scalable tree boosting system," in *Int. Conf. Knowl. Discov. Data Min. (KDD '16)*, ACM, pp. 785-794 (2016).

58. Y. Wu and K. He, "Group normalization," *arXiv:1803.08494* (2018).
59. S. Ioffe and C. Szegedy, "Batch normalization: accelerating deep network training by reducing internal covariate shift," arXiv:1502.03167 (2015).

60. D. Lunter et al., "Novel aspects of Raman spectroscopy in skin research," *Exp. Dermatol.* **31**(9), 1311–1329 (2022).

61. M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," arXiv:1311.2901 (2012).

62. J. Scheleusener et al., "In vivo study for the discrimination of cancerous and normal skin using fibre probe-based Raman spectroscopy," *Exp. Dermatol.* **2**, 767–772 (2015).

63. M. D. M. S. Benito Bodanese et al., "Discrimination of basal cell carcinoma and melanoma from normal skin biopsies in vitro through Raman spectroscopy and principal component analysis," *Photomed. Laser Surg.* **30**, 381–387 (2012).

64. L. Kandolf et al., "European consensus-based interdisciplinary guideline for diagnosis, treatment and prevention of actinic keratoses, epithelial UV-induced dysplasia and field cancerization on behalf of European Association of Dermato-Oncology, European Dermatology Forum, E," *J. Eur. Acad. Dermatol. Venereol.* **38**, 1024–1047 (2024).

65. H. Lui et al., "Real-time Raman spectroscopy for in vivo skin cancer diagnosis," *Am. Assoc. Cancer Res.* **72**, 2491–2500 (2012).

66. A. C. S. Talari et al., "Raman spectroscopy of biological tissues," *Appl. Spectrosc. Rev.* **50**(1), 46–111 (2014).

67. F. R. Paolillo et al., "Noninvasive assessments of skin glycated proteins by fluorescence and Raman techniques in diabetics and nondiabetics," *J. Biophotonics* **12**(1), e201800162 (2018).

68. M. Wu, B. Gao, and X. Wei, "Recent advances in Raman spectroscopy for skin diagnosis," *J. Innov. Opt. Health Sci.* **16**, 2330003 (2023).

69. W. Léna, *Tomographie par Cohérence Optique Confocale à Balayage de Ligne (LC-OCT) Multimodale pour le Diagnostic des Cancers Cutanés*, thèse de l'université Paris-Saclay (2023).

70. J. Zhao et al., "Real-time Raman spectroscopy for noninvasive in vivo skin analysis and diagnosis," *New Dev. Biomed. Eng.* **24**, 455–474 (2010).

71. M. Larraona-Puy et al., "Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma," *J. Biomed. Opt.* **14**(5), 054031 (2009).

72. P. Labroo et al., "Physical characterization of swine and human skin: correlations between Raman spectroscopy, tensile testing, atomic force microscopy (AFM), scanning electron microscopy (SEM), and multiphoton microscopy (MPM)," *Ski. Res. Technol.* **27**, 501–510 (2020).

73. I. Matveeva et al., "Multivariate curve resolution alternating least squares analysis of in vivo skin Raman spectra," *Sensors* **22**(24), 9588 (2022).

74. Y. Kanemura et al., "Assessment of skin inflammation using near-infrared Raman spectroscopy combined with artificial intelligence analysis in an animal model," *Analyst* **147**(12), 2843–2850 (2022).

75. M. A. Davies, "Cleansing-induced changes in skin measured by in vivo confocal Raman spectroscopy," *Ski. Res. Technol.* **26**(1), 30–38 (2019).

76. L. Liendl, J. Grillari, and M. Schosserer, "Raman fingerprints as promising markers of cellular senescence and aging," *GeroScience* **42**, 377–387 (2020).

77. A. Girard et al., "Raman spectroscopic analysis of skin as a diagnostic tool for human African trypanosomiasis," *PLOS Pathog.* **17**(11), e1010060 (2021).

78. Y. Liu and D. J. Lunter, "Systematic investigation of the effect of non-ionic emulsifiers on skin by confocal Raman spectroscopy—a comprehensive lipid analysis," *Pharmaceutics* **12**(3), 223 (2020).

79. C. Choe et al., "Keratin-water-NMF interaction as a three layer model in the human stratum corneum using in vivo confocal Raman microscopy," *Sci. Rep.* **7**(1), 15900 (2017).

80. X. Feng et al., "Raman biophysical markers in skin cancer diagnosis," *J. Biomed. Opt.* **23**(5), 057002 (2018).

81. M. Suppa et al., "Line-field confocal optical coherence tomography of basal cell carcinoma: a descriptive study," *J. Eur. Acad. Dermatol. Venereol.* **35**, 1099–1110 (2021).

82. L. Mtimet et al., "Line-field confocal optical coherence tomography of basal cell carcinoma: a retrospective study on diagnostic performance," *J. Eur. Acad. Dermatol. Venereol.* **35**(5), 1099–1110 (2023).

83. M. Rui et al., "Application of semi-supervised convolutional neural network regression model based on data augmentation and process spectral labeling in Raman predictive modeling of cell culture processes," *Biochem. Eng. J.* **191**, 108774 (2023).

84. S. A. Overman et al., "Novel tyrosine markers in Raman spectra of wild-type and mutant (Y21M and Y24M) Ff virions indicate unusual environments for coat protein phenoxylst," *Biochemistry* **33**, 1037–1042 (1994).

85. P. T. C. Freire et al., "Raman spectroscopy of amino acid crystals," *Raman Spectrosc. Appl.* **201**, 171 (2017).

86. C. A. Lieber et al., "In vivo nonmelanoma skin cancer diagnosis using Raman microspectroscopy," *Lasers Surg. Med.* **40**, 461–467 (2008).

87. J. Bakar et al., "Biomolecular modifications during keratinocyte differentiation: Raman spectroscopy and chromatographic techniques," *Analyst* **146**, 2965–2973 (2021).
88. H. Wu et al., "In vivo lipidomics using single-cell Raman spectroscopy," *Acta Phys. Sin.* **108**, 3809–3814 (2011).

89. A. Tfayli, "Caracterisation structurale et moleculaire de la peau par microspectroscopies optiques vibrationnelles. Applications au diagnostic precoce des tumeurs cutanees et a l'étude de la diffusion de principes actifs a visee therapeutique," Universite De Reims Champagne-Ardenne, [Online], https://www.theses.fr/2007REIMP208 (2007).

90. G. Pezzotti et al., "Raman spectroscopy of human skin: looking for a quantitative algorithm to reliably estimate human age," *J. Biomed. Opt.* **20**(6), 065008 (2015).

91. B. Bodanese et al., "Discrimination of basal cell carcinoma and melanoma from normal skin biopsies in vitro through Raman spectroscopy and principal component analysis," *Photomed. Laser Surg.* **30**, 381–387 (2012).

92. D. Wu, A. F. Kukk, and B. Roth, "Detection of melanin influence on skin samples based on Raman spectroscopy and optical coherence tomography dual-modal approach," *J. Biophotonics* **16**(8), e202300080 (2023).

93. J. Udensi et al., "Raman spectroscopy of carotenoid compounds for clinical applications—a review," *Molecules* **27**(24), 9017 (2022).

94. M. Gniadecka et al., "Distinctive molecular abnormalities in benign and malignant skin lesions: studies by Raman spectroscopy," *Photochem. Photobiol.* **66**(4), 418–423 (1997).

95. M. Imanbekova et al., "Identification of amyloid beta in small extracellular vesicles via Raman spectroscopy," *Nanoscale Adv.* **3**, 4119–4132 (2021).

Arnaud Dubois received his PhD in physics from Paris-Saclay University in 1997. Since 2006, he has been a professor of optics at "Institut d'Optique Graduate School" in Palaiseau, France. His current research focuses on biophotonics and optical imaging. A pioneer in optical coherence microscopy (OCM) in the early 2000s, he has been a key contributor to the advancement of this technology. He has authored over 180 research articles and 15 book chapters, and has participated at more than 250 scientific conferences. He also holds 5 patents. In 2014, he co-founded DAMAE Medical, a startup developing innovative OCM-based solutions for the non-invasive, in situ diagnosis of skin diseases.

Biographies of the other authors are not available.