| |  Online-Ressource |
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| Verfasst von: | Niehues, Jan Moritz [VerfasserIn]  |
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| | Quirke, Philip [VerfasserIn] |
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| | West, Nicholas P. [VerfasserIn] |
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| | Grabsch, Heike I. [VerfasserIn] |
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| | van Treeck, Marko [VerfasserIn] |
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| | Schirris, Yoni [VerfasserIn] |
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| | Veldhuizen, Gregory P. [VerfasserIn] |
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| | Hutchins, Gordon G. A. [VerfasserIn] |
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| | Richman, Susan D. [VerfasserIn] |
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| | Foersch, Sebastian [VerfasserIn] |
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| | Brinker, Titus Josef [VerfasserIn] |
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| | Fukuoka, Junya [VerfasserIn] |
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| | Bychkov, Andrey [VerfasserIn] |
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| | Uegami, Wataru [VerfasserIn] |
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| | Truhn, Daniel [VerfasserIn] |
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| | Brenner, Hermann [VerfasserIn] |
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| | Brobeil, Alexander [VerfasserIn] |
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| | Hoffmeister, Michael [VerfasserIn] |
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| | Kather, Jakob Nikolas [VerfasserIn] |
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| Titel: | Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning |
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| Titelzusatz: | a retrospective multi-centric study |
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| Verf.angabe: | Jan Moritz Niehues, Philip Quirke, Nicholas P. West, Heike I. Grabsch, Marko van Treeck, Yoni Schirris, Gregory P. Veldhuizen, Gordon G.A. Hutchins, Susan D. Richman, Sebastian Foersch, Titus J. Brinker, Junya Fukuoka, Andrey Bychkov, Wataru Uegami, Daniel Truhn, Hermann Brenner, Alexander Brobeil, Michael Hoffmeister, and Jakob Nikolas Kather |
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| E-Jahr: | 2023 |
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| Jahr: | 18 April 2023 |
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| Umfang: | 16 S. |
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| Fussnoten: | Online verfügbar 22 March 2023, Version des Artikels 18 April 2023 ; Gesehen am 09.08.2023 |
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| Titel Quelle: | Enthalten in: Cell reports. Medicine |
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| Ort Quelle: | Cambridge, MA : Cell Press, 2020 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 4(2023), 4 vom: Apr., Artikel-ID 100980, Seite 1-16 |
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| ISSN Quelle: | 2666-3791 |
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| Abstract: | Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient. |
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| DOI: | doi:10.1016/j.xcrm.2023.100980 |
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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.xcrm.2023.100980 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S2666379123000861 |
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| | DOI: https://doi.org/10.1016/j.xcrm.2023.100980 |
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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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| | attention heatmaps |
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| | attention-based multiple-instance learning |
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| | biomarker |
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| | colorectal cancer |
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| | computational pathology |
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| | multi-input models |
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| | oncogenic mutation |
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| | self-supervised learning |
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| K10plus-PPN: | 1854989456 |
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| Verknüpfungen: | → Zeitschrift |
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Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning / Niehues, Jan Moritz [VerfasserIn]; 18 April 2023 (Online-Ressource)
