Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.
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Haggenmüller Sarah, Maron Roman C, Hekler Achim, Utikal Jochen S, Barata Catarina, Barnhill Raymond L, Beltraminelli Helmut, Berking Carola, Betz-Stablein Brigid, Blum Andreas, Braun Stephan A, Carr Richard, Combalia Marc, Fernandez-Figueras Maria-Teresa, Ferrara Gerardo, Fraitag Sylvie, French Lars E, Gellrich Frank F, Ghoreschi Kamran, Goebeler Matthias, Guitera Pascale, Haenssle Holger A, Haferkamp Sebastian, Heinzerling Lucie, Heppt Markus V, Hilke Franz J, Hobelsberger Sarah, Krahl Dieter, Kutzner Heinz, Lallas Aimilios, Liopyris Konstantinos, Llamas-Velasco Mar, Malvehy Josep, Meier Friedegund, Müller Cornelia S L, Navarini Alexander A, Navarrete-Dechent Cristián, Perasole Antonio, Poch Gabriela, Podlipnik Sebastian, Requena Luis, Rotemberg Veronica M, Saggini Andrea, Sangueza Omar P, Santonja Carlos, Schadendorf Dirk, Schilling Bastian, Schlaak Max, Schlager Justin G, Sergon Mildred, Sondermann Wiebke, Soyer H Peter, Starz Hans, Stolz Wilhelm, Vale Esmeralda, Weyers Wolfgang, Zink Alexander, Krieghoff-Henning Eva, Kather Jakob N, von Kalle Christof, Lipka Daniel B, Fröhling Stefan, Hauschild Axel, Kittler Harald, Brinker Titus J
Аннотация
OBJECTIVE: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians.
METHODS: PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included.
RESULTS: A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images.
CONCLUSIONS: All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice.