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000288058 1001_ $$00000-0002-2542-2117$$aEl Nahhas, Omar S M$$b0
000288058 245__ $$aRegression-based Deep-Learning predicts molecular biomarkers from pathology slides.
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000288058 520__ $$aDeep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
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000288058 7001_ $$aLoeffler, Chiara M L$$b1
000288058 7001_ $$aCarrero, Zunamys I$$b2
000288058 7001_ $$avan Treeck, Marko$$b3
000288058 7001_ $$aKolbinger, Fiona R$$b4
000288058 7001_ $$aHewitt, Katherine J$$b5
000288058 7001_ $$aMuti, Hannah S$$b6
000288058 7001_ $$00000-0003-3456-945X$$aGraziani, Mara$$b7
000288058 7001_ $$00000-0002-1473-8327$$aZeng, Qinghe$$b8
000288058 7001_ $$aCalderaro, Julien$$b9
000288058 7001_ $$aOrtiz-Brüchle, Nadina$$b10
000288058 7001_ $$0P:(DE-He78)b9e439a1aa1244925f92d547c0919349$$aYuan, Tanwei$$b11$$udkfz
000288058 7001_ $$0P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f$$aHoffmeister, Michael$$b12$$udkfz
000288058 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b13$$udkfz
000288058 7001_ $$aBrobeil, Alexander$$b14
000288058 7001_ $$00000-0003-2969-3173$$aReis-Filho, Jorge S$$b15
000288058 7001_ $$aKather, Jakob Nikolas$$b16
000288058 773__ $$0PERI:(DE-600)2553671-0$$a10.1038/s41467-024-45589-1$$gVol. 15, no. 1, p. 1253$$n1$$p1253$$tNature Communications$$v15$$x2041-1723$$y2024
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