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

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 d...

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Published in:Journal of the European Academy of Dermatology and Venereology 2024-07
Main Authors: Gaudy‐Marqueste, C., Grob, J. J., Garbe, C., Ascierto, P. A., Arron, S., Basset‐Seguin, N., Bohne, A. S., Lenoir, C., Dummer, R., Fargnoli, M. C., Guminski, A., Hauschild, A., Kaufmann, R., Lallas, A., del Marmol, V., Migden, M., Penicaud, M., Rembielak, A., Stratigos, A., Tagliaferri, L., Zalaudek, I., Arance, A., Badinand, D., Bossi, P., Challapalli, A., Clementi, M., Di Stefani, A., Ferrándiz‐Pulido, C., Giuffrida, R., Gravina, G. L., Ha, P., Heinzerling, L., Mallet, S., Paradisi, A., Mohr, P., Piccerillo, A., Rutkowski, D., Saiag, P., Sollena, P., Trakatelli, M., Wojcieszek, P., Yom, S. S., Zelin, E., Peris, K., Malvehy, J.
Format: Article
Language:English
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Summary: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. Objective To generate a consensual and operational classification of cSCCs. Method 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. Conclusion 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.
ISSN:0926-9959
1468-3083
1468-3083
DOI:10.1111/jdv.20209