Open Access

Jean-Jacques Grob, Caroline Gaudy-Marqueste, Alexander Guminski, Joseph Malvehy, Nicole Basset-Seguin, Baptiste Bertrand, Pablo ‪Fernandez-Peñas‬, Roland Kaufmann, Iris Zalaudek, Maria Concetta Fargnoli, Luca Tagliaferri, Bernard Fertil, Veronique Del Marmol, Alexander Stratigos, Claus Garbe, Ketty Peris

• Background: No simple staging system has emerged for basal cell carcinomas (BCCs), since they do not follow the TNM process, and practitioners failed to agree on simple clinical or pathological criteria as a basis for a classification. Operational classification of BCCs is required for decision-making, trials and guidelines. Unsupervised clustering of real cases of difficult-to-treat BCCs (DTT-BCCs; part 1) has demonstrated that experts could blindly agree on a five groups classification of DTT-BCCs based on five patterns of clinical situations. Objective: Using this five patterns to generate an operational and comprehensive classification of BCCs. Method: Testing practitioner's agreement, when using the five patterns classification to ensure that it is robust enough to be used in the practice. Generating the first version of a staging system of BCCs based on pattern recognition. Results: Sixty-two physicians, including 48 practitioners and the 14 experts who participated in the generation of the five different patterns of DTT-BCCs, agreed on 90% of cases when classifying 199 DTT-BCCs cases using the five patterns classification (part 1) attesting that this classification is understandable and usable in practice. In order to cover the whole field of BCCs, these five groups of DTT-BCCs were added a group representing the huge number of easy-to-treat BCCs, for which sub-classification has little interest, and a group of very rare metastatic cases, resulting in a four-stage and seven-substage staging system of BCCs. Conclusion: A practical classification adapted to the specificities of BCCs is proposed. It is the first tumour classification based on pattern recognition of clinical situations, which proves to be consistent and usable. This EADO staging system version 1 will be improved step by step and tested as a decision tool and a prognostic instrument.