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European journal of cancer (Oxford, England : 1990). 2021 Sep 8. pii: S0959-8049(21)00444-5. doi: 10.1016/j.ejca.2021.06.049

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

Haggenmüller S¹, Maron RC², Hekler A³, Utikal JS⁴, Barata C⁵, Barnhill RL⁶, Beltraminelli H⁷, Berking C⁸, Betz-Stablein B⁹, Blum A¹⁰, Braun SA¹¹, Carr R¹², Combalia M¹³, Fernandez-Figueras MT¹⁴, Ferrara G¹⁵, Fraitag S¹⁶, French LE¹⁷, Gellrich FF¹⁸, Ghoreschi K¹⁹, Goebeler M²⁰, Guitera P²¹, Haenssle HA²², Haferkamp S²³, Heinzerling L²⁴, Heppt MV²⁵, Hilke FJ²⁶, Hobelsberger S²⁷, Krahl D²⁸, Kutzner H²⁹, Lallas A³⁰, Liopyris K³¹, Llamas-Velasco M³², Malvehy J³³, Meier F³⁴, Müller CSL³⁵, Navarini AA³⁶, Navarrete-Dechent C³⁷, Perasole A³⁸, Poch G³⁹, Podlipnik S⁴⁰, Requena L⁴¹, Rotemberg VM⁴², Saggini A⁴³, Sangueza OP⁴⁴, Santonja C⁴⁵, Schadendorf D⁴⁶, Schilling B⁴⁷, Schlaak M⁴⁸, Schlager JG⁴⁹, Sergon M⁵⁰, Sondermann W⁵¹, Soyer HP⁵², Starz H⁵³, Stolz W⁵⁴, Vale E⁵⁵, Weyers W⁵⁶, Zink A⁵⁷, Krieghoff-Henning E⁵⁸, Kather JN⁵⁹, von Kalle C⁶⁰, Lipka DB⁶¹, Fröhling S⁶², Hauschild A⁶³, Kittler H⁶⁴, Brinker TJ⁶⁵

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Abstract

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

Publikations ID: 34509059
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-- Cancer Care
-- Kliniken und Partner
-- CCC Cancer School
-- Young CCC
-- CCC Tumorboards
-- CCC Forschungscluster
-- CCC Units
-- CCC Platforms
-- SOPs / Leitlinien
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