Brinker, Titus J. and Kiehl, Lennard and Schmitt, Max and Jutzi, Tanja B. and Krieghoff-Henning, Eva I. and Krahl, Dieter and Kutzner, Heinz and Gholam, Patrick and Haferkamp, Sebastian and Klode, Joachim and Schadendorf, Dirk and Hekler, Achim and Froehling, Stefan and Kather, Jakob N. and Haggenmueller, Sarah and von Kalle, Christof and Heppt, Markus and Hilke, Franz and Ghoreschi, Kamran and Tiemann, Markus and Wehkamp, Ulrike and Hauschild, Axel and Weichenthal, Michael and Utikal, Jochen S. (2021) Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours. EUROPEAN JOURNAL OF CANCER, 154. pp. 227-234. ISSN 0959-8049, 1879-0852
Full text not available from this repository. (Request a copy)Abstract
Aim: Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this study, we therefore aimed to develop a digital biomarker that can predict lymph node metastasis non invasively from digitised H&E slides of primary melanoma tumours. Methods: A total of 415 H&E slides from primary melanoma tumours with known sentinel node (SN) status from three German university hospitals and one private pathological practice were digitised (150 SN positive/265 SN negative). Two hundred ninety-one slides were used to train artificial neural networks (ANNs). The remaining 124 slides were used to test the ability of the ANNs to predict sentinel status. ANNs were trained and/or tested on data sets that were matched or not matched between SN-positive and SN-negative cases for patient age, ulceration, and tumour thickness, factors that are known to correlate with lymph node status. Results: The best accuracy was achieved by an ANN that was trained and tested on unmatched cases (61.8% +/- 0.2%) area under the receiver operating characteristic (AUROC). In contrast, ANNs that were trained and/or tested on matched cases achieved (55.0% +/- 3.5%) AUROC or less. Conclusion: Our results indicate that the image classifier can predict lymph node status to some, albeit so far not clinically relevant, extent. It may do so by mostly detecting equivalents of factors on histological slides that are already known to correlate with lymph node status. Our results provide a basis for future research with larger data cohorts. (C) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | CLASSIFICATION; RISK; STRATIFICATION; DERMATOLOGISTS; SUPERIOR; Melanoma; Skin cancer; Artificial intelligence; Neural network model; Lymph node biopsy; Sentinel; Histology; Machine learning; Biomarkers; Pathology |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Dermatologie und Venerologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 27 Sep 2022 05:59 |
| Last Modified: | 27 Sep 2022 05:59 |
| URI: | https://pred.uni-regensburg.de/id/eprint/47989 |
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