Skin cancer classification via convolutional neural networks : systematic review of studies involving human experts.
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Author
Haggenmüller, SarahMaron, 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 I.
Kather, Jakob N.
von Kalle, Christof
Lipka, Daniel B.
Fröhling, Stefan
Hauschild, Axel
Kittler, Harald
Brinker, Titus J.
Affiliation
German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany; Heidelberg University, Mannheim, Germany; South Warwickshire University NHS Foundation Trust; et al.Publication date
2021-10
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Background: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice. 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. Keywords: Artificial intelligence; Convolutional neural network(s); Deep learning; Dermatology; Digital biomarkers; Machine learning; Malignant melanoma; Skin cancer classification.Citation
Haggenmüller S, Maron RC, Hekler A, Utikal JS, Barata C, Barnhill RL, Beltraminelli H, Berking C, Betz-Stablein B, Blum A, Braun SA, Carr R, Combalia M, Fernandez-Figueras MT, Ferrara G, Fraitag S, French LE, Gellrich FF, Ghoreschi K, Goebeler M, Guitera P, Haenssle HA, Haferkamp S, Heinzerling L, Heppt MV, Hilke FJ, Hobelsberger S, Krahl D, Kutzner H, Lallas A, Liopyris K, Llamas-Velasco M, Malvehy J, Meier F, Müller CSL, Navarini AA, Navarrete-Dechent C, Perasole A, Poch G, Podlipnik S, Requena L, Rotemberg VM, Saggini A, Sangueza OP, Santonja C, Schadendorf D, Schilling B, Schlaak M, Schlager JG, Sergon M, Sondermann W, Soyer HP, Starz H, Stolz W, Vale E, Weyers W, Zink A, Krieghoff-Henning E, Kather JN, von Kalle C, Lipka DB, Fröhling S, Hauschild A, Kittler H, Brinker TJ. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. Eur J Cancer. 2021 Oct;156:202-216. doi: 10.1016/j.ejca.2021.06.049. Epub 2021 Sep 8.Type
ArticlePMID
34509059Journal
European Journal of CancerPublisher
Elsevierae974a485f413a2113503eed53cd6c53
10.1016/j.ejca.2021.06.049