Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Maron, Roman C.; Weichenthal, Michael; Utikal, Jochen S.; Hekler, Achim; Berking, Carola; Hauschild, Axel; Enk, Alexander H.; Haferkamp, Sebastian; Klode, Joachim; Schadendorf, Dirk; Jansen, Philipp; Holland-Letz, Tim; Schilling, Bastian; Kalle, Christof von; Fröhling, Stefan; Gaiser, Maria R.; Hartmann, Daniela; Gesierich, Anja; Kaehler, Katharina C.; Wehkamp, Ulrike; Karoglan, Ante; Baer, Claudia; Brinker, Titus J.; Schmitt, Laurenz; Peitsch, Wiebke K.; Hoffmann, Friederike; Becker, Jürgen C.; Drusio, Christina; Jansen, Philipp; Klode, Joachim; Lodde, Georg; Sammet, Stefanie; Schadendorf, Dirk; Sondermann, Wiebke; Ugurel, Selma; Zader, Jeannine; Enk, Alexander; Salzmann, Martin; Schäfer, Sarah; Schaekel, Knut; Winkler, Julia; Woelbing, Priscilla; Asper, Hiba; Bohne, Ann-Sophie; Brown, Victoria; Burba, Bianca; Deffaa, Sophia; Dietrich, Cecilia; Dietrich, Matthias; Drerup, Katharina Antonia; Egberts, Friederike; Erkens, Anna-Sophie; Greven, Salim; Harde, Viola; Jost, Marion; Kaeding, Merit; Kosova, Katharina; Lischner, Stephan; Maagk, Maria; Messinger, Anna Laetitia; Metzner, Malte; Motamedi, Rogina; Rosenthal, Ann-Christine; Seidl, Ulrich; Stemmermann, Jana; Torz, Kaspar; Velez, Juliana Giraldo; Haiduk, Jennifer; Alter, Mareike; Baer, Claudia; Bergenthal, Paul; Gerlach, Anne; Holtorf, Christian; Karoglan, Ante; Kindermann, Sophie; Kraas, Luise; Felcht, Moritz; Gaiser, Maria R.; Klemke, Claus-Detlev; Kurzen, Hjalmar; Leibing, Thomas; Müller, Verena; Reinhard, Raphael R.; Utikal, Jochen; Winter, Franziska; Berking, Carola; Eicher, Laurie; Hartmann, Daniela; Heppt, Markus; Kilian, Katharina; Krammer, Sebastian; Lill, Diana; Niesert, Anne-Charlotte; Oppel, Eva; Sattler, Elke; Senner, Sonja; Wallmichrath, Jens; Wolff, Hans; Giner, Tina; Glutsch, Valerie; Kerstan, Andreas; Presser, Dagmar; Schruefer, Philipp; Schummer, Patrick; Stolze, Ina; Weber, Judith; Drexler, Konstantin; Haferkamp, Sebastian; Mickler, Marion; Stauner, Camila Toledo und Thiem, Alexander (2019): Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. In: European Journal of Cancer, Bd. 119: S. 57-65

Abstract

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 0959-8049
• Sprache: Englisch