# Operational classification of cutaneous squamous cell carcinomas based on unsupervised clustering of real cases by experts

C. Gaudy-Marqueste, J. J. Grob, S. Mallet, C. Garbe, P. A. Ascierto, S. Arron, N. Basset-Seguin, A. S. Bohne, A. Hauschild, C. Lenoir, et al.

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## To cite this version:

C. Gaudy-Marqueste, J. J. Grob, S. Mallet, C. Garbe, P. A. Ascierto, et al. Operational classification of cutaneous squamous cell carcinomas based on unsupervised clustering of real cases by experts. Journal of the European Academy of Dermatology and Venereology, 2024, 10.1111/jdv.20209. hal-04831055

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Submitted on 22 May 2025

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Distributed under a Creative Commons Attribution 4.0 International License
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ORIGINAL ARTICLE

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JEADV

JOURNAL OF
THE EUROPEAN
ACADEMY OF
DERMATOLOGY &
VENEREOLOGY

# Operational classification of cutaneous squamous cell carcinomas based on unsupervised clustering of real cases by experts

C. Gaudy-Marqueste¹

J. J. Grob¹

C. Garbe²

P. A. Ascierto³

S. Arron⁴

N. Basset-Seguin⁵

A. S. Bohne⁶

C. Lenoir⁷

R. Dummer⁸

M. C. Fargnoli⁹

A. Guminski¹⁰,¹¹

A. Hauschild⁶

R. Kaufmann¹²

A. Lallas¹³

V. del Marmol¹⁴

M. Migden¹⁵

M. Penicaud¹⁶

A. Rembielak¹⁷,¹⁸

A. Stratigos¹⁹

L. Tagliaferri²⁰,²¹

I. Zalaudek²²

A. Arance²³

D. Badinand²⁴

P. Bossi²⁵,²⁶

A. Challapalli²⁷

M. Clementi²⁸

A. Di Stefani²⁹,³⁰

C. Ferrándiz-Pulido³¹,³²

R. Giuffrida³³

G. L. Gravina³⁴

P. Ha³⁵

L. Heinzerling³⁶

S. Mallet¹

A. Paradisi²⁹,³⁰

P. Mohr³⁷

A. Piccerillo²⁹,³⁰

D. Rutkowski³⁸

P. Saiag³⁹

P. Sollena²⁹,³⁰

M. Trakatelli⁴⁰

P. Wojcieszek⁴¹

S. S. Yom⁴²

E. Zelin⁴³

K. Peris²⁹,³⁰

J. Malvehy⁴⁴

Correspondence

C. Gaudy-Marqueste, Dermatology and Skin
Cancer Department, Aix-Marseille University,
Hopital La Timone, 264 rue Saint Pierre,
13385 Marseille Cedex 5, France.

Email: caroline.gaudy@ap-hm.fr

Funding information

Sanofi

Abstract

**Background:** There is currently no staging system for cutaneous squamous cell carcinoma (cSCC) that is adapted to decision-making and universally used. Experts have unconscious ability to simplify the heterogeneity of clinical situations into a few relevant groups to drive their therapeutic decisions. Therefore, we have used unsupervised clustering of real cases by experts to generate an operational classification of cSCCs, an approach that was successful for basal cell carcinomas.

**Objectives:** To generate a consensual and operational classification of cSCCs.

**Methods:** Unsupervised independent clustering of 248 cases of cSCCs considered difficult-to-treat. Eighteen international experts from different specialties classified these cases into what they considered homogeneous clusters useful for management, each with freedom regarding clustering criteria. Convergences and divergences between clustering were analysed using a similarity matrix, the K-mean approach and the average silhouette method. Mathematical modelling was used to look for the best consensual clustering. The operability of the derived classification was validated on 23 new practitioners.

**Results:** Despite the high heterogeneity of the clinical cases, a mathematical consensus was observed. It was best represented by a partition into five clusters, which appeared a posteriori to describe different clinical scenarios. Applicability of this classification was shown by a good concordance (94%) in the allocation of cases between the new practitioners and the 18 experts. An additional group of easy-to-treat cSCC was included, resulting in a six-group final classification: easy-to-treat/complex to treat due to tumour and/or patient characteristics/multiple/locally advanced/regional disease/visceral metastases.

Linked article: C. Gebhardt. J Eur Acad Dermatol Venereol. 2025;39:461–462. https://doi.org/10.1111/jdv.20548.

For affiliations refer to page 618.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

© 2024 The Author(s). *Journal of the European Academy of Dermatology and Venereology* published by John Wiley & Sons Ltd on behalf of European Academy of Dermatology and Venereology.
**Conclusions:** Given the methodology based on the convergence of unguided intuitive clustering of cases by experts, this new classification is relevant for clinical practice. It does not compete with staging systems, but they may complement each other, whether the objective is to select the best therapeutic approach in tumour boards or to design homogeneous groups for trials.

## INTRODUCTION

At least four different staging systems mostly derived from head and neck tumours are available for cutaneous squamous cell carcinomas (cSCCs)¹⁻⁴ but none of them are universally accepted. The European and US therapeutic guidelines are not based on TNM or other classifications.⁵ These current classifications are unable to adequately identify patients at high risk of disease progression.⁶ Furthermore, the practical decisions in tumour boards are not based on these classifications but rather on a holistic assessment of each situation. Therefore, there is an urgent need for an operational classification of the cSCCs, (1) for case discussion in tumour boards and decision-making, (2) to design clinical trials to compare therapeutic strategies in homogeneous subgroups and (3) to inform guidelines.

Building an operational classification of cSCCs is challenging for many reasons. It encompasses highly heterogeneous situations ranging from common cSCCs in locations where surgery could cause some mutilation, to highly destructive tumours that are considered inoperable. The views and treatment recommendations of medical oncologists, surgeons, radiation oncologists and dermatologists, all potentially involved in patient management, are diverse and difficult to harmonize. Finally, it is challenging to select the most relevant criteria for classification among the hundreds available, some being relative to the tumour itself (size, number, location in high-risk areas, histological features, nodal or distant metastases and the number of previous recurrences), others being linked to prior treatments, treatment options, clinician preferences and skills and patients characteristics (age, general status, comorbidities, but also fear, desires and willingness to cooperate).

Independent clustering of real cases by experts is an original approach based on the 'perceived similarity' between cases and the natural ability of the human brain to recognize patterns, allowing the identification of 'unconscious consensual patterns'. This method has been successfully used by the EADO to generate an operational staging system of basal cell carcinomas (BCCs).⁷,⁸ The EADO objective in this new study was to use a similar approach for cSCCs to converge on a consensual simple and operational classification. As most cSCCs are easy to manage and cured by surgery, we decided to focus on a subgroup of cSCCs that clinicians identified as difficult-to-treat (DTT) regardless of their reasons for considering them as such.

## METHODS

### Working hypotheses

We hypothesized (1) that experts were unconsciously able to translate the heterogeneity of DTT-cSCCs presentation into a few dominant situations to drive their therapeutic decisions, (2) that these situations were likely to be similar for most experts independently of their specialty (dermatologists, medical oncologists, H&N surgeons, plastic surgeons, dermat-surgeons and radiation oncologists). An overview of the study design is shown in Figure 1.

### Phase 1. Independent clustering of real cases

As described in a previous study for BCCs,⁷ each expert was presented with a set of real-life patient cases and was asked to cluster them, with the aim of grouping together all cSCCs cases that he/she considered close according to the criteria he/she considered as relevant. Each expert had to perform the clustering alone, 'blinded' to the other experts. The clustering was unsupervised, and each expert was free to choose his/her criteria for classifying the cases with two restrictions: the clustering had to be useful for the clinical management, and the total number of clusters had to be between two and nine, to end up with an operational classification, which would not be the case if there were too many groups. A mathematical model was used to find the convergence between the different individual clustering patterns and to find a limited number of clusters that best represented consensus. According to French law, Institutional Review Board approval was not required for this non-interventional study.

### Case selection

Patient cases of cSCCs considered to be DTT for any reason by dermatologists/oncologists/surgeons practicing in referral skin cancer centres were collected from 12 centres of the EADO. Each case was recorded on a dedicated website on a standardized one-sheet document, anonymized and blinded with one to five images of the lesion (including radiological images, if relevant) and a standardized case report (Figure 2). The latter included demography, age, sex, ECOG status, comorbidities including
<figure><img src="image_5.png" /><figcaption>**FIGURE 1** General design of the study.</figcaption></figure>

immunosuppression, total number of cSCCs, tumour location, tumour history (including recurrences and previous treatments) and finally the reasons for which this cSCC had been considered ‘difficult-to-treat’ by the treating physician (see Table S1 for a comprehensive description of the cases). One of the experts (KP) was responsible for overseeing the selection of cases, to ensure that the variability of DTT-cSCCs in the practice was well represented in the case collection.

## Experts

Eighteen internationally recognized experts familiar with DTT-cSCCs, from nine countries in Europe, the United Kingdom, the United States and Australia, and from different specialties, participated in the experiment: Seven dermatologists (NB, AS, MCF, KP, RM, RD and AH), four dermato-oncologists (JJG, CG, CGM and JM), two medical oncologists (AG and PA), one H&N surgeon (MP), two dermato-surgeons (RK and STA) and two radiation oncologists (LT and AR).

## Independent blind clustering of DTT-cSCC cases by each expert

The 18 experts independently partitioned 248 cases into clusters according to the general principle described above.

## Statistical analysis and identification of a consensual clustering

A similarity matrix between all DTT-cSCCs cases was built. If two DTT-cSCCs patient cases were found together in the same cluster from one partition proposed by a given expert, the similarity between the two cases increased by 1. Therefore, maximum similarity between two cases reached 18 for cases found together in a cluster in the 18 partitions, while minimum similarity was 0 if 2 cases were never found together in one cluster, whatever the expert. A side-by-side comparison of the clustering of each pair of experts was performed using the Bcubed statistic.⁹⁻¹¹ Three types of algorithms were tested: K-means approach,¹² hierarchical clustering¹³ and DBSACN clustering.¹⁴ For each algorithm, the best values for the hyperparameters were obtained based on the silhouette score metric.¹⁵ Nonlinear multidimensional scaling (MDS)¹⁶ allowed visualization of similarities between cases in 2D plots.

## Using the consensual clustering to make an operational classification of cSCCs

The resulting consensual partition generated by the model was presented a posteriori to the same panel of 18 experts. All the pictures of cases classified together in the consensual clusters, as well as the mean, median and distribution of all the clinical variables of that cluster, were provided to
<figure><img src="image_6.png" /><figcaption>**FIGURE 3** (a) Divergence and convergence between experts represented by the relative distance between experts on a two-dimension representation. Each dot represents one expert, its colour codes for the expert's specialty, the letters are the expert's ID and the flag is for the expert's origin. The closer the dots are, the more similar the expert's partitions are. (b) Consensual clustering from the similarity matrix. 2D projection of the cases based on the similarity matrix and the TNSE algorithm. Each dot stands for a case, its colour represents the consensual cluster (C) to which it belongs. This plot helps visualize the similarity between cases, as initially expressed by the similarity matrix. The more cases are similar in the matrix, the closer is their position on the graph. Each colour represents a single group from the consensual clustering. (c) Dendrogram illustration of the consensual clustering obtained with the hierarchical clustering. The colours represent the clusters (C). Each leaf represents a patient's case. The yellow dashed line shows the optimal cut-off level for this clustering.</figcaption></figure>

metastases distant from the primary tumour which are more representative of a regional disease and would therefore be classified in group 4.

## Testing the operability of the classification

The agreement between the 23 experts of the validation group in classifying the cases was rather high: a majority of experts agreed on 94% of the cases. However, 15 cases (6%) received distributed votes, nearly equally divided between two groups, and six cases (2%) were split over three groups. Out of these 21 cases, agreement raised to 96%.

The overall agreement (concordance in the group assignment of cases between the validation group of 23 new practitioners and the 18 initial experts) was 90%. There was no clear dominant group assignment for only 15 out of 248 cases which can therefore be considered 'ambiguous' in this classification. The most frequent ambiguities were between group 1 ('complex to treat due to tumour and/or patient characteristics') and group 3 ('locally advanced cSCC') (7 cases) and between group 1 ('complex to treat due to tumour and/ or patient characteristics') and group 2 ('multiple cSCC') (8 cases) (Table 1).

## DISCUSSION

Using unsupervised case clustering by experts, we generated a simple and understandable categorization of DTT-cSCCs into five well-defined groups, confirming our previous experience with the classification of DTT-BCCs.⁷,⁸ An additional group of easy-to-treat cSCCs, which were not part of this experiment since they are easily managed by surgery and do not require therapeutic discussion, was added to the classification a posteriori in order to cover the full spectrum of cSCCs.

Interestingly, our study shows that the experts analysed the highly variable clinical situations of DTT-cSCCs in a very simple way: visceral metastatic disease, nodal disease and three types of local disease suggesting that the overall assessment is the main driver of their decisions. This may explain why complex classifications such as AJCC, UICC, Bringham and Women's Hospital¹⁻⁴ are used for risk
<table><thead><tr><th>Stage</th><th>Characteristics</th><th>Illustrative pictures</th><th>Classification group</th></tr></thead><tbody><tr><td>I</td><td>Easy to treat cSCC</td><td>cSCC (TN0M0) easily manageable<br/>None of the other groups characteristics<br/>*Surgical excision recommended</td><td>Not included in the experiment</td></tr><tr><td>IIA</td><td>Complex to treat</td><td>cSCC (TN0M0) complex to treat due to tumor and/or patient characteristics*<br/>*Surgery is likely to be curative but functional or cosmetic consequences and/or other factors (general status, immunosuppression, comorbidities, tumor history..) may lead to therapeutic discussion including radiotherapy with curative intention and/or medical treatments</td><td>Group 1<br/>n = 65</td></tr><tr><td>Common cSCC</td><td>IIB Multiple tumors</td><td>Multiple cSCCs (TN0M0) when the number is the main problem for management, whatever the background (genetics, immunosuppression...)<br/>*Multimodal approach with either surgery/ radiotherapy/systemic therapy<br/>**when the characteristics of at least one cSCC, is the main problem and not the number itself, patients must be classified in other relevant groups according to the most problematic cSCC</td><td>Group 2<br/>n = 24</td></tr><tr><td>Advanced cSCC</td><td>IIC Locally advanced without regional metastases</td><td>Locally advanced cSCC (TN0M0)<br/>*surgery and/or radiotherapy are unlikely to be curative indication for systemic therapy / radiotherapy/ palliative care according to patient's performance status</td><td>Group 3<br/>n = 69</td></tr><tr><td>Metastatic cSCC</td><td>III Regional metastases</td><td>cSCC with regional metastases either nodal or cutaneous metastases distant from the primary (TN+M0) whatever the severity and number of cSCC<br/>*Multimodal approach with either surgery/ radiotherapy/systemic therapy</td><td>Group 4<br/>n = 59</td></tr><tr><td></td><td>IV Distant metastases</td><td>cSCC with distant metastases (TNM+), whatever the severity and number of cSCC<br/>*Multimodal approach with systemic therapy, radiotherapy or palliative care according to patient's performance status</td><td>Group 5<br/>n = 31</td></tr></tbody></table>

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

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>FIGURE 4 Six-group classification derived from the interpretation of the five consensual clusters and the addition of the easy to treat group. Each definition is the best formulation found by the experts to describe the common points between cases in each given consensual cluster. The numbers indicate the number of cases initially included in each consensual cluster.</figcaption></figure>

<table><caption>TABLE 1 Concordance in the allocation of cases between the 18 initial experts and the 23 other practitioners using the five-group classification.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="5">Other practitioners (n = 23)</th><th rowspan="2">Total nb of cases</th><th rowspan="2">Concordance</th></tr><tr><th>Group 1</th><th>Group 2</th><th>Group 3</th><th>Group 4</th><th>Group 5</th></tr></thead><tbody><tr><td rowspan="5">Experts (n = 18)</td><td>Group 1</td><td>55</td><td>8</td><td>2</td><td>0</td><td>0</td><td>65</td><td>85%</td></tr><tr><td>Group 2</td><td>1</td><td>20</td><td>3</td><td>0</td><td>0</td><td>24</td><td>83%</td></tr><tr><td>Group 3</td><td>7</td><td>2</td><td>59</td><td>0</td><td>1</td><td>69</td><td>86%</td></tr><tr><td>Group 4</td><td>0</td><td>0</td><td>0</td><td>59</td><td>0</td><td>59</td><td>100%</td></tr><tr><td>Group 5</td><td>0</td><td>0</td><td>0</td><td>2</td><td>29</td><td>31</td><td>94%</td></tr><tr><td colspan="2">Total nb of cases</td><td>63</td><td>30</td><td>64</td><td>61</td><td>30</td><td>248</td><td></td></tr></tbody></table>
<sup>29</sup>UOC Dermatologia, Dipartimento di Scienze Mediche e Chirurgiche Addominali ed Endocrino Metaboliche, Fondazione Policlinico Universitario A. Gemelli - IRCCS, Rome, Italy

<sup>30</sup>Dermatologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Rome, Italy

<sup>31</sup>Department of Dermatology, University Hospital Vall d'Hebron, Barcelona, Spain

<sup>32</sup>Facultad de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain

<sup>33</sup>Department of Clinical and Experimental Medicine, Dermatology - University of Messina, Messina, Italy

<sup>34</sup>Department of Biotechnological and Applied Clinical Sciences, Radiotherapy and Radiobiology Unit, University of L'Aquila, L'Aquila, Italy

<sup>35</sup>Department of Otolaryngology Head and Neck Surgery, University of California San Francisco, San Francisco, California, USA

<sup>36</sup>Department of Dermatology and Allergy, Ludwig-Maximilian University Hospital, LMU, Munich, Germany

<sup>37</sup>Department of Dermatology, Elbe-Kliniken Buxtehude, Buxtehude, Germany

<sup>38</sup>Department of Dermatology, Manchester Foundation Trust, Manchester, UK

<sup>39</sup>Department of General and Oncologic Dermatology, Ambroise Paré Hospital, APHP, & EA 4340 "Biomarkers in Cancerology and Hemato-Oncology", UVSQ, Université Paris-Saclay, Boulogne-Billancourt, France

<sup>40</sup>Second Department of Dermatology, Aristotle University Medical School, Papageorgiou General Hospital, Thessaloniki, Greece

<sup>41</sup>Brachytherapy Department, Maria Sklodowska-Curie National Research Institute of Oncology (MSCNRIO), Gliwice, Poland

<sup>42</sup>Department of Radiation Oncology, University of California San Francisco, San Francisco, California, USA

<sup>43</sup>Institute of Dermatology, Santa Maria Della Misericordia University Hospital, Udine, Italy

<sup>44</sup>Dermatology Department, Hospital Clinic of Barcelona, IDIBAPS, University of Barcelona and CIBER de enfermedades Raras, ICIII, Barcelona, Spain

## ACKNOWLEDGEMENTS

The authors are grateful to Dr J Kleemann and A Kloechel for their participation into the experiment assessing the operability of classification.

## FUNDING INFORMATION

This work was supported by a grant from Sanofi. The funders had no role in the design and conduct of the study, collection, management, analysis and interpretation of the data, preparation, review or approval of the manuscript; and the decision to submit the manuscript for publication.

## CONFLICT OF INTEREST STATEMENT

Dr C Gaudy-Marqueste reports institutional fees for clinical trials from Amgen, Astra Zeneca, BMS, Catalyst, Day One biopharmaceuticals, GSK, Huyabio, IFX, Immunocore, IO Biotech, Jansen, Kartos, Kinnate, MSD, Novartis, Pfizer, Philogen, Pierre Fabre, Regeneron, Replimune, Roche, Sairopa, Sanofi and Sotio; personal consulting fees from Pierre Fabre, BMS and MSD; Payment or honoraria for lectures from BMS, MSD and Pierre Fabre; support for attending meetings and/or travel from Pierre-Fabre, BMS and MSD outside of the submitted work. Dr JJ Grob reports consulting fees from Novartis and BMS; Participation on a Data Safety Monitoring Board or Advisory Board for Novartis, BMS, MSD, philogen, Pierre Fabre Sanofi, Roche and amgen, outside of the submitted work. Dr Garbe reports consulting fees from CeCaVa, Merck Sharp & Dohme, NeraCare and Philogen, outside of the submitted work. Dr PA Ascierto reports grants or contracts from Bristol Myers Squibb, Roche-Genentech, Pfizer and Sanofi; consulting fees from Bristol Myers Squibb, Roche-Genentech, Merck Sharp & Dohme, Novartis, Merck Serono, Pierre-Fabre, Sun Pharma, Sanofi,

Sandoz, Italfarmaco, Nektar, Pfizer, Lunaphore and Medicenna. Bio-Al Health, ValoTx, Replimmune, Bayer, Philogen, BionTech; support for attending meetings and/or travel from Pfizer, Bio-Al Health, Replimmune, MSD; Participation on a Data Safety Monitoring Board or Advisory Board for Bristol Myers Squibb, Roche-Genentech, Merck Sharp & Dohme, Novartis, AstraZeneca, Boehringer-Ingelheim, Eisai, Regeneron, Daiichi Sankyo, Oncosec, Nouscom, Seagen, iTeos and Erasca outside of the submitted work. Dr ST Arron reports consulting fees from Regeneron, Replimune and Castle Biosciences; honoraria from Regeneron and Castle Biosciences; payment for expert testimony from Forensis Group; support for attending meetings and/or travel from Castle Biosciences; Participation on a Data Safety Monitoring Board or Advisory Board for Replimune; Leadership or fiduciary role as Immediate Past President of the International Transplant Skin Cancer Collaborative; Stock or stock options from Rakuten Medical, Genentech, 23andMe. Dr N Basset-Seguin reports consulting fees from Sun Pharma, Galderma and Regeneron; support for attending meetings and/or travel from Regeneron, Sun Pharmaceuticals; Participation on a Data Safety Monitoring Board or Advisory Board for Regeneron, Sun Pharmaceuticals and Almiral. Dr AS Bohne reports support for attending meetings and/or travel from Sun-Pharma, Pierre-Fabre and BMS outside of the submitted work. Dr C Lenoir reports consulting fees from Regeneron. Dr R Dummer reports intermittent, project focused consulting fees and/or advisory relationships with Amgen, BMS, MSD, Novartis, Pierre-Fabre, Roche, Sun-Pharma, Takeda, Sanofi, Caralyn, Second-Genome, Regeneron, Alligator, T3 Pharma, MaxiVAX SA and Pfizer; Simcere and touchIME outside of the submitted work. Dr MC Fargnoli reports consulting fees from Almirall, Honoraria from Sanofi, support for attending meetings and/or travel from Sanofi, Participation on a Data Safety Monitoring Board or Advisory Board for Sanofi and financial support to her institution for participation into Regeneron clinical trials. Dr A Guminski reports Unrestricted grant support for an investigator-initiated trial from Sun Phrama, honoraria for presentations from BMS, support for attending meetings and/or travel from BMS, Sun Pharma; Participation on a Data Safety Monitoring Board or Advisory Board for Regeneron and MSD. Dr A Hauschild reports grants and personal fees from Amgen, grants and personal fees from BMS, grants and personal fees from MerckPfizer, grants and personal fees from MSD/Merck, grants and personal fees from Philogen, grants and personal fees from Pierre Fabre, grants and personal fees from Regeneron, grants and personal fees from Roche, grants and personal fees from Sanofi-Genzyme, grants and personal fees from Novartis Pharma, grants and personal fees from Eisai, personal fees from Immunocore, grants and personal fees from Replimune, personal fees from Seagen, personal fees from IO Biotech, personal fees from Dermagnostix, personal fees from Incyte, grants and personal fees from NeraCare, personal fees from Highlight Therapeutics, grants from Huya Biosciences, personal fees from Kyowa Kirin and
member of the Italian Society of Dermatology (SIDeMaST). Dr J Malvehy reports honoraria from Almirall, Pierre Fabre, ISDIN, BMS, MSD and Sun Pharma; support for attending meetings from Almirall, ISDIN and SunPharma; stock or stock options from Athena and Dermavision. Dr A Lallas, Dr V Del Marmol, Dr M Penicaud, Dr D Badinand, Dr M Clementi, Dr GL Gravina, Dr S Mallet, Dr A Piccerillo, Dr D Rutkowski, Dr P Sollena, Dr P Wojcieszek have no COI to disclose.

## DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

## ETHICS STATEMENT

The patients in this manuscript have given written informed consent to the publication of their case details.

## ORCID

C. Garbe https://orcid.org/0000-0001-8530-780X

C. Lenoir https://orcid.org/0009-0004-5174-9932

R. Dummer https://orcid.org/0000-0002-2279-6906

M. C. Fargnoli https://orcid.org/0000-0002-7249-2556

R. Kaufmann https://orcid.org/0000-0002-1614-694X

A. Lallas https://orcid.org/0000-0002-7193-0964

P. Bossi https://orcid.org/0000-0003-0135-0224

A. Challapalli https://orcid.org/0000-0002-3683-9583

R. Giuffrida https://orcid.org/0000-0002-5492-3033

A. Piccerillo https://orcid.org/0000-0003-1734-7310

M. Trakatelli https://orcid.org/0000-0002-4312-6557

E. Zelin https://orcid.org/0000-0001-9276-3627

K. Peris https://orcid.org/0000-0003-1957-6600

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Gaudy-Marqueste C, Grob JJ, Garbe C, Ascierto PA, Arron S, Basset-Seguin N, et al. Operational classification of cutaneous squamous cell carcinomas based on unsupervised clustering of real cases by experts. J Eur Acad Dermatol Venereol. 2025;39:612-621. https://doi.org/10.1111/jdv.20209