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

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 enh

SUBMITTER: El Nahhas OSM 

PROVIDER: S-EPMC10858881 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature