Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours. (September 2021)

• Record Type: Journal Article
• Authors: Brinker, Titus J.; Kiehl, Lennard; Schmitt, Max; Jutzi, Tanja B.; Krieghoff-Henning, Eva I.; Krahl, Dieter; Kutzner, Heinz; Gholam, Patrick; Haferkamp, Sebastian; Klode, Joachim; Schadendorf, Dirk; Hekler, Achim; Fröhling, Stefan; Kather, Jakob N.; Haggenmüller, Sarah; von Kalle, Christof; Heppt, Markus; Hilke, Franz; Ghoreschi, Kamran; Tiemann, Markus; Wehkamp, Ulrike; Hauschild, Axel; Weichenthal, Michael; Utikal, Jochen S.
• Is Part Of: European journal of cancer. Volume 154(2021)
• Page Start: 227
• Page End: 234
• Publication Date: 2021-09
• Subjects: Melanoma -- Skin cancer -- Artificial intelligence -- Neural network model -- Lymph node biopsy -- Sentinel -- Histology -- Machine learning -- Biomarkers -- Pathology; Cancer -- Periodicals; Neoplasms -- Periodicals; Cancer -- Périodiques; Cancer; Tumors; Electronic journals; Periodicals; Electronic journals; 616.994
• DOI: 10.1016/j.ejca.2021.05.026
• Languages: English
• ISSNs: 0959-8049

Abstract

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. Highlights: Lymph node analysis conveys relevant prognostic information. Surgical removal of lymph nodes can be associated with considerable morbidity. Thus, biomarkers that allow prediction of lymph node status are needed. We trained a CNN on H&E slides of node positive and -negative primary melanomas. This classifier could predict sentinel node status to some extent.