  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 hypothesized that regression-based DL outperforms
classification-based DL. Therefore, we developed and evaluated a new
self-supervised attention-based weakly supervised regression method that
predicts continuous biomarkers directly from images in 11,671 patients across
nine cancer types. We tested our method for multiple clinically and
biologically relevant biomarkers: homologous repair deficiency (HRD) 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
interpretability of the results 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.
