Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
This work addresses the need for more accurate and interpretable continuous biomarker prediction in computational pathology, offering an incremental improvement over existing classification approaches.
The researchers tackled the problem of predicting continuous molecular biomarkers from pathology slides, finding that regression-based deep learning outperforms classification-based methods in accuracy and interpretability, with regression scores providing higher prognostic value in colorectal cancer patients.
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.