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| Verfasst von: | El Nahhas, Omar S. M. [VerfasserIn]  |
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| | Loeffler, Chiara M. L. [VerfasserIn] |
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| | Carrero, Zunamys I. [VerfasserIn] |
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| | van Treeck, Marko [VerfasserIn] |
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| | Kolbinger, Fiona [VerfasserIn] |
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| | Hewitt, Katherine J. [VerfasserIn] |
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| | Muti, Hannah S. [VerfasserIn] |
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| | Graziani, Mara [VerfasserIn] |
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| | Zeng, Qinghe [VerfasserIn] |
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| | Calderaro, Julien [VerfasserIn] |
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| | Ortiz-Brüchle, Nadina [VerfasserIn] |
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| | Yuan, Tanwei [VerfasserIn] |
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| | Hoffmeister, Michael [VerfasserIn] |
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| | Brenner, Hermann [VerfasserIn] |
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| | Brobeil, Alexander [VerfasserIn] |
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| | Reis-Filho, Jorge S. [VerfasserIn] |
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| | Kather, Jakob Nikolas [VerfasserIn] |
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| Titel: | Regression-based Deep-Learning predicts molecular biomarkers from pathology slides |
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| Verf.angabe: | Omar S.M. El Nahhas, Chiara M.L. Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, Katherine J. Hewitt, Hannah S. Muti, Mara Graziani, Qinghe Zeng, Julien Calderaro, Nadina Ortiz-Brüchle, Tanwei Yuan, Michael Hoffmeister, Hermann Brenner, Alexander Brobeil, Jorge S. Reis-Filho & Jakob Nikolas Kather |
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| E-Jahr: | 2024 |
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| Jahr: | 10 February 2024 |
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| Umfang: | 13 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 13.09.2024 |
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| Titel Quelle: | Enthalten in: Nature Communications |
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| Ort Quelle: | [London] : Springer Nature, 2010 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 15(2024), 1, Artikel-ID 1253, Seite 1-13 |
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| ISSN Quelle: | 2041-1723 |
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| Abstract: | Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology. |
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| DOI: | doi:10.1038/s41467-024-45589-1 |
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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.
kostenfrei: Volltext: https://doi.org/10.1038/s41467-024-45589-1 |
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| | DOI: https://doi.org/10.1038/s41467-024-45589-1 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Biomarkers, Tumor |
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| | Deep Learning |
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| | Humans |
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| | Neoplasms |
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| | Technology |
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| | Tumor Microenvironment |
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| K10plus-PPN: | 1902533437 |
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| Verknüpfungen: | → Zeitschrift |
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Regression-based Deep-Learning predicts molecular biomarkers from pathology slides / El Nahhas, Omar S. M. [VerfasserIn]; 10 February 2024 (Online-Ressource)
