Universität zu Köln

Hekler, Achim, Utikal, Jochen Sven, Enk, Alexander H., Berking, Carola, Klode, Joachim, Schadendorf, Dirk ORCID: 0000-0003-3524-7858, Jansen, Philipp, Franklin, Cindy, Holland-Letz, Tim, Krahl, Dieter, von Kalle, Christof, Froehling, Stefan and Brinker, Titus Josef (2019). Pathologist-level classification of histopathological melanoma images with deep neural networks. Eur. J. Cancer, 115. S. 79 - 84. OXFORD: ELSEVIER SCI LTD. ISSN 1879-0852

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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. (C) 2019 The Author(s). Published by Elsevier Ltd.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Hekler, Achim UNSPECIFIED UNSPECIFIED UNSPECIFIED Utikal, Jochen Sven UNSPECIFIED UNSPECIFIED UNSPECIFIED Enk, Alexander H. UNSPECIFIED UNSPECIFIED UNSPECIFIED Berking, Carola UNSPECIFIED UNSPECIFIED UNSPECIFIED Klode, Joachim UNSPECIFIED UNSPECIFIED UNSPECIFIED Schadendorf, Dirk UNSPECIFIED orcid.org/0000-0003-3524-7858 UNSPECIFIED Jansen, Philipp UNSPECIFIED UNSPECIFIED UNSPECIFIED Franklin, Cindy UNSPECIFIED UNSPECIFIED UNSPECIFIED Holland-Letz, Tim UNSPECIFIED UNSPECIFIED UNSPECIFIED Krahl, Dieter UNSPECIFIED UNSPECIFIED UNSPECIFIED von Kalle, Christof UNSPECIFIED UNSPECIFIED UNSPECIFIED Froehling, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Brinker, Titus Josef UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-136938
DOI:	10.1016/j.ejca.2019.04.021
Journal or Publication Title:	Eur. J. Cancer
Volume:	115
Page Range:	S. 79 - 84
Date:	2019
Publisher:	ELSEVIER SCI LTD
Place of Publication:	OXFORD
ISSN:	1879-0852
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language DIGITAL PATHOLOGY; DERMATOLOGISTS; DIAGNOSIS; ALGORITHMS Multiple languages Oncology Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/13693