Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Grob, JJ; Guminski, A; Malvehy, J; Basset-Seguin, N; Bertrand, B; Fernandez-Penas, P; Kaufmann, R; Zalaudek, I; Gaudy-Marqueste, C; Fargnoli, MC; Tagliaferri, L; Fertil, B; Del, Marmol, V; Stratigos, A; Garbe, C; Peris, K.
Position statement on classification of basal cell carcinomas. Part 1: unsupervised clustering of experts as a way to build an operational classification of advanced basal cell carcinoma based on pattern recognition.
J Eur Acad Dermatol Venereol. 2021; 35(10):1949-1956
Doi: 10.1111/jdv.17466
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- Zalaudek Iris
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- Abstract:
- BACKGROUND: No simple classification system has emerged for 'advanced basal cell carcinomas', and more generally for all difficult-to-treat BCCs (DTT-BCCs), due to the heterogeneity of situations, TNM inappropriateness to BCCs, and different approaches of different specialists. OBJECTIVE: To generate an operational classification, using the unconscious ability of experts to simplify the great heterogeneity of the clinical situations into a few relevant groups, which drive their treatment decisions. METHOD: Non-supervised independent and blinded clustering of real clinical cases of DTT-BCCs was used. Fourteen international experts from different specialties independently partitioned 199 patient cases considered 'difficult to treat' into as many clusters they want (≤10), choosing their own criteria for partitioning. Convergences and divergences between the individual partitions were analyzed using the similarity matrix, K-mean approach, and average silhouette method. RESULTS: There was a rather consensual clustering of cases, regardless of the specialty and nationality of the experts. Mathematical analysis showed that consensus between experts was best represented by a partition of DTT-BCCs into five clusters, easily recognized a posteriori as five clear-cut patterns of clinical situations. The concept of 'locally advanced' did not appear consistent between experts. CONCLUSION: Although convergence between experts was not granted, this experiment shows that clinicians dealing with BCCs all tend to work by a similar pattern recognition based on the overall analysis of the situation. This study thus provides the first consensual classification of DTT-BCCs. This experimental approach using mathematical analysis of independent and blinded clustering of cases by experts can probably be applied to many other situations in dermatology and oncology.
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