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| Verfasst von: | Brinker, Titus Josef [VerfasserIn]  |
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| | Kiehl, Lennard [VerfasserIn] |
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| | Schmitt, Max [VerfasserIn] |
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| | Jutzi, Tanja [VerfasserIn] |
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| | Krieghoff-Henning, Eva [VerfasserIn] |
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| | Krahl, Dieter [VerfasserIn] |
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| | Kutzner, Heinz [VerfasserIn] |
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| | Gholam, Patrick [VerfasserIn] |
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| | Haferkamp, Sebastian [VerfasserIn] |
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| | Klode, Joachim [VerfasserIn] |
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| | Schadendorf, Dirk [VerfasserIn] |
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| | Hekler, Achim [VerfasserIn] |
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| | Fröhling, Stefan [VerfasserIn] |
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| | Kather, Jakob Nikolas [VerfasserIn] |
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| | Haggenmüller, Sarah [VerfasserIn] |
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| | Kalle, Christof von [VerfasserIn] |
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| | Heppt, Markus V. [VerfasserIn] |
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| | Hilke, Franz [VerfasserIn] |
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| | Ghoreschi, Kamran [VerfasserIn] |
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| | Tiemann, Markus [VerfasserIn] |
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| | Wehkamp, Ulrike [VerfasserIn] |
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| | Hauschild, Axel [VerfasserIn] |
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| | Weichenthal, Michael [VerfasserIn] |
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| | Utikal, Jochen [VerfasserIn] |
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| Titel: | Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours |
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| Verf.angabe: | Titus J. Brinker, Lennard Kiehl, Max Schmitt, Tanja B. Jutzi, Eva I. Krieghoff-Henning, Dieter Krahl, Heinz Kutzner, Patrick Gholam, Sebastian Haferkamp, Joachim Klode, Dirk Schadendorf, Achim Hekler, Stefan Fröhling, Jakob N. Kather, Sarah Haggenmüller, Christof von Kalle, Markus Heppt, Franz Hilke, Kamran Ghoreschi, Markus Tiemann, Ulrike Wehkamp, Axel Hauschild, Michael Weichenthal, Jochen S. Utikal |
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| E-Jahr: | 2021 |
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| Jahr: | September 2021 |
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| Umfang: | 8 S. |
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| Fussnoten: | Available online 20 July 2021 ; Gesehen am 03.08.2023 |
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| Titel Quelle: | Enthalten in: European journal of cancer |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier, 1992 |
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| Jahr Quelle: | 2021 |
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| Band/Heft Quelle: | 154(2021) vom: Sept., Seite 227-234 |
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| ISSN Quelle: | 1879-0852 |
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| 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. |
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| DOI: | doi:10.1016/j.ejca.2021.05.026 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1016/j.ejca.2021.05.026 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S095980492100335X |
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| | DOI: https://doi.org/10.1016/j.ejca.2021.05.026 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Artificial intelligence |
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| | Biomarkers |
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| | Histology |
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| | Lymph node biopsy |
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| | Machine learning |
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| | Melanoma |
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| | Neural network model |
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| | Pathology |
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| | Sentinel |
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| | Skin cancer |
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| K10plus-PPN: | 1854260847 |
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| Verknüpfungen: | → Zeitschrift |
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Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours / Brinker, Titus Josef [VerfasserIn]; September 2021 (Online-Ressource)
