Pathologist-level classification of histopathological melanoma images with deep neural networks. (July 2019)

• Record Type: Journal Article
• Authors: Hekler, Achim; Utikal, Jochen Sven; Enk, Alexander H.; Berking, Carola; Klode, Joachim; Schadendorf, Dirk; Jansen, Philipp; Franklin, Cindy; Holland-Letz, Tim; Krahl, Dieter; von Kalle, Christof; Fröhling, Stefan; Brinker, Titus Josef
• Is Part Of: European journal of cancer. Volume 115(2019)
• Page Start: 79
• Page End: 83
• Publication Date: 2019-07
• Subjects: Melanoma -- Pathology -- Histopathology -- Deep learning -- Artificial intelligence; Cancer -- Periodicals; Neoplasms -- Periodicals; Cancer -- Périodiques; Cancer; Tumors; Electronic journals; Periodicals; Electronic journals; 616.994
• DOI: 10.1016/j.ejca.2019.04.021
• Languages: English
• ISSNs: 0959-8049

Abstract

Abstract: Background: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25–26% of discordance for classifying a benign nevus versus malignant melanoma. Deep learning was successfully implemented to enhance the precision of lung and breast cancer diagnoses. The aim of this study is to illustrate the potential of deep learning to assist human assessment for a histopathologic melanoma diagnosis. Methods: Six hundred ninety-five lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi and 345 melanomas). Only the haematoxylin and eosin stained (H&E) slides of these lesions were digitalised using a slide scanner and then randomly cropped. Five hundred ninety-five of the resulting images were used for the training of a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison with the original class labels. Findings: The total discordance with the histopathologist was 18% for melanoma (95% confidence interval [CI]: 7.4–28.6%), 20% for nevi (95% CI: 8.9–31.1%) and 19% for the full set of images (95% CI: 11.3–26.7%). Interpretation: Even in the worst case, the discordance of the CNN was about the same compared with the discordance between human pathologists as reported in the literature. Despite the vastly reduced amount of data, time necessary for diagnosis and cost compared with the pathologist, our CNN archived on-par performance. Conclusively, CNNs indicate to be a valuable tool to assist human melanoma diagnoses. Highlights: A convolutional neural network (CNN) was trained with 595 histopathologic images of melanomas and nevi that were classified by an expert dermatohistopathologist. The CNN was then tested with 100 additional images (melanoma/nevi = 1:1) and revealed a discordance of only 19% to the histopathologist. Thus, even in the worst case, the discordance of the CNN is about the same compared with the discordance between human pathologists as reported in the literature (25–26%).