TY  - JOUR
A1  - Grob, Jean-Jacques
A1  - Guminski, Alexander
A1  - Malvehy, Josep
A1  - Basset-Seguin, Nicole
A1  - Bertrand, Baptiste
A1  - Fernandez-Penas, Pablo
A1  - Kaufmann, Roland
A1  - Zalaudek, Iris
A1  - Gaudy-Marqueste, Caroline
A1  - Fargnoli, Maria Concetta
A1  - Tagliaferri, Luca
A1  - Fertil, Bernard
A1  - Del Marmol, Veronique
A1  - Stratigos, Alexander
A1  - Garbe, Claus
A1  - Peris, Ketty
T1  - 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
T2  - Journal of the European Academy of Dermatology and Venereology
N2  - 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.
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/63907
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-639079
SN  - 1468-3083
N1  - This work was supported by two unrestricted grants from Roche and SunPharma. 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 decision to submit the manuscript for publication.
VL  - 35
IS  - 10
SP  - 1949
EP  - 1956
PB  - Wiley-Blackwell
CY  - Oxford [u.a.]
ER  -