Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Nature Communications, vol. 15, pp. 1253
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.
Authors 17
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Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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University Hospital Carl Gustav Carus · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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University Hospital Carl Gustav Carus · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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University Hospital Carl Gustav Carus · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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HES-SO University of Applied Sciences and Arts Western Switzerland · HES-SO Valais-Wallis
Affiliation as printed
University of Applied Sciences of Western Switzerland (HES-SO Valais), Rue du Technopole 3, 3960, Sierre, Valais, Switzerland
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Inserm · Université Paris Cité · Sorbonne Université · Centre de Recherche des Cordeliers
Affiliation as printed
Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, Paris, France
Centre d’Histologie, d’Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, Paris, France
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Centre Hospitalier Universitaire Henri-Mondor · Assistance Publique – Hôpitaux de Paris · Hôpitaux Universitaires Henri-Mondor
Affiliation as printed
Assistance Publique-Hôpitaux de Paris, Département de Pathologie, CHU Henri Mondor, F-94000, Créteil, France
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Cologne, Germany
Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
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Affiliation as printed
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Affiliation as printed
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases · Deutsches Konsortium für Translationale Krebsforschung
Affiliation as printed
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
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Heidelberg University · University Hospital Heidelberg · National Center for Tumor Diseases
Affiliation as printed
Institute of Pathology, University Hospital Heidelberg, 69120, Heidelberg, Germany
Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120, Heidelberg, Germany
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Memorial Sloan Kettering Cancer Center
Affiliation as printed
Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Jakob Nikolas Kather corresponding
University of Leeds · Heidelberg University · University Hospital Heidelberg · National Center for Tumor Diseases · University Hospital Carl Gustav Carus · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de
Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. jakob_nikolas.kather@tu-dresden.de
Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom. jakob_nikolas.kather@tu-dresden.de
Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
Cited by 106 stored of 106
Cited by patents worldwide 1 (Lens.org)
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DUAL-MODALITY MODELS FOR DIGITAL PATHOLOGYWO2025255578A1 2025-12-11 Pending