Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Authors:
raw_author_name: Marko van Treeck
raw_affiliation_string: Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
raw_author_name: Fiona R. Kolbinger
raw_affiliation_string: Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
raw_author_name: Katherine J. Hewitt
raw_affiliation_string: Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
raw_author_name: Mara Graziani
raw_affiliation_string: University of Applied Sciences of Western Switzerland (HES-SO Valais), Rue du Technopole 3, 3960, Sierre, Valais, Switzerland
raw_author_name: Qinghe Zeng
raw_affiliation_string: Centre d’Histologie, d’Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, Paris, France
raw_author_name: Nadina Ortiz-Brüchle
raw_affiliation_string: Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
raw_author_name: Tanwei Yuan
raw_affiliation_string: Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
raw_author_name: Michael Hoffmeister
raw_affiliation_string: Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
raw_author_name: Hermann Brenner
raw_affiliation_string: Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
raw_author_name: Alexander Brobeil
raw_affiliation_string: Institute of Pathology, University Hospital Heidelberg, 69120, Heidelberg, Germany
raw_author_name: Jorge S. Reis-Filho
raw_affiliation_string: Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
raw_author_name: Jakob Nikolas Kather
raw_affiliation_string: Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany

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.