Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

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 o...

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Detalhes bibliográficos
Autores: 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 I., Kather, Jakob N., Von Kalle, Christof, Lipka, Daniel B., Fröhling, Stefan, Hauschild, Axel, Kittler, Harald, Brinker, Titus J.
Tipo de documento: artigo
Data de publicação:2021
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:20.500.12328/2874
Acesso em linha:http://hdl.handle.net/20.500.12328/2874
https://dx.doi.org/10.1016/j.ejca.2021.06.049
Access Level:Acceso aberto
Palavra-chave:Classificació del càncer de pell
Biomarcadors
Biomarcadors digitals
Càncer de pell
Xarxa neuronal de convolució
Intel·ligència artificial
Aprenentatge automàtic
Aprenentatge profund
Dermatologia
Melanoma maligne
Clasificación del cáncer de piel
Biomarcadores
Biomarcadores digitales
Cáncer de piel
Red neuronal de convolución
Inteligencia artificial
Aprendizaje automático
Aprendizaje profundo
Dermatología
Melanoma maligno
Classification of skin cancer
Biomarkers
Digital biomarkers
Skin cancer
Neural network of convolution
Artificial intelligence
Machine learning
Deep learning
Dermatology
Malignant melanoma
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Descrição
Resumo: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.

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