Skin lesions of face and scalp – Classification by a market-approved convolutional neural network in comparison with 64 dermatologists

• Published in: European journal of cancer (1990) Vol. 144; pp. 192 - 199
• Main Authors: Haenssle, Holger Andreas, Winkler, Julia Katharina, Fink, Christine, Toberer, Ferdinand, Enk, Alexander, Stolz, Wilhelm, Deinlein, Teresa, Hofmann-Wellenhof, Rainer, Kittler, Harald, Tschandl, Philipp, Rosendahl, Cliff, Lallas, Aimilios, Blum, Andreas, Abassi, Mohamed Souhayel, Thomas, Luc, Tromme, Isabelle, Rosenberger, Albert, Bachelerie, Marie, Bajaj, Sonali, Balcere, Alise, Baricault, Sophie, Barthaux, Clément, Beckenbauer, Yvonne, Bertlich, Ines, Bouthenet, Marie-France, Brassat, Sophie, Buck, Philipp Marcel, Buder-Bakhaya, Kristina, Cappelletti, Maria-Letizia, Chabbert, Cécile, De Labarthe, Julie, DeCoster, Eveline, Dobler, Michèle, Dumon, Daphnée, Emmert, Steffen, Gachon-Buffet, Julie, Gusarov, Mikhail, Hartmann, Franziska, Hartmann, Julia, Herrmann, Anke, Hoorens, Isabelle, Hulstaert, Eva, Karls, Raimonds, Kolonte, Andreea, Kromer, Christian, Le Blanc Vasseux, Céline, Levy-Roy, Annabelle, Majenka, Pawel, Marc, Marine, Bourret, Veronique Martin, Michelet-Brunacci, Nadège, Mitteldorf, Christina, Paroissien, Jean, Picard, Camille, Plise, Diana, Reymann, Valérie, Ribeaudeau, Fabrice, Richez, Pauline, Plaine, Hélène Roche, Salik, Deborah, Sattler, Elke, Schäfer, Sarah, Schneiderbauer, Roland, Secchi, Thierry, Talour, Karen, Trennheuser, Lukas, Wald, Alexander, Wölbing, Priscila, Zukervar, Pascale
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2021; Elsevier Science Ltd; Elsevier
• Subjects: Actinic keratosis; Artificial neural networks; Basal cell carcinoma; Cancer; Datasets; Decisions; Deep learning; Dermatology; Dermoscopy; Face; Lentigo maligna; Lesions; Life Sciences; Management decisions; Medical imaging; Melanoma; Moleanalyzer-pro; Neural network; Neural networks; Scalp; Seborrheic keratosis; Sensitivity; Skin cancer; Skin diseases; Skin lesions; Solar lentigo; Deep learning; Lentigo maligna; Actinic keratosis; Basal cell carcinoma; Melanoma; Moleanalyzer-pro; Dermoscopy; Seborrheic keratosis; Neural network; Solar lentigo; Skin cancer
• ISSN: 0959-8049; 1879-0852
• DOI: 10.1016/j.ejca.2020.11.034

The clinical differentiation of face and scalp lesions (FSLs) is challenging even for trained dermatologists. Studies comparing the diagnostic performance of a convolutional neural network (CNN) with dermatologists in FSL are lacking. A market-approved CNN (Moleanalyzer-Pro, FotoFinder Systems) was used for binary classifications of 100 dermoscopic images of FSL. The same lesions were used in a two-level reader study including 64 dermatologists (level I: dermoscopy only; level II: dermoscopy, clinical close-up images, textual information). Primary endpoints were the CNN's sensitivity and specificity in comparison with the dermatologists' management decisions in level II. Generalizability of the CNN results was tested by using four additional external data sets. The CNN's sensitivity, specificity and ROC AUC were 96.2% [87.0%–98.9%], 68.8% [54.7%–80.1%] and 0.929 [0.880–0.978], respectively. In level II, the dermatologists' management decisions showed a mean sensitivity of 84.2% [82.2%–86.2%] and specificity of 69.4% [66.0%–72.8%]. When fixing the CNN's specificity at the dermatologists' mean specificity (69.4%), the CNN's sensitivity (96.2% [87.0%–98.9%]) was significantly higher than that of dermatologists (84.2% [82.2%–86.2%]; p < 0.001). Dermatologists of all training levels were outperformed by the CNN (all p < 0.001). In confirmation, the CNN's accuracy (83.0%) was significantly higher than dermatologists' accuracies in level II management decisions (all p < 0.001). The CNN's performance was largely confirmed in three additional external data sets but particularly showed a reduced specificity in one Australian data set including FSL on severely sun-damaged skin. When applied as an assistant system, the CNN's higher sensitivity at an equivalent specificity may result in an improved early detection of face and scalp skin cancers. •Face and scalp lesions (FSLs) are difficult to diagnose.•Physicians may benefit from assistance by convolutional neural networks (CNNs).•In a data set of 100 FSL cases a CNN was compared with 64 dermatologists.•The CNN outperformed dermatologists by a higher sensitivity at equivalent specificity.•The CNN may help to improve skin cancer detection in clinical routine.