Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.
Nat Commun
; 15(1): 1253, 2024 Feb 10.
Article
in En
| MEDLINE
| ID: mdl-38341402
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Deep Learning
/
Neoplasms
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Nat Commun
/
Nature communications
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2024
Document type:
Article
Affiliation country:
Alemania
Country of publication:
Reino Unido