Enhancing Predictive Imaging Biomarker Discovery through Treatment Effect Analysis

Shuhan Xiao, Lukas Klein, Jens Petersen, Philipp Vollmuth, Paul F. Jaeger, Klaus H. Maier-Hein; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4512-4522

Identifying predictive covariates which forecast individual treatment effectiveness is crucial for decision-making across different disciplines such as personalized medicine. These covariates referred to as biomarkers are extracted from pre-treatment data often within randomized controlled trials and should be distinguished from prognostic biomarkers which are independent of treatment assignment. Our study focuses on discovering predictive imaging biomarkers specific image features by leveraging pre-treatment images to uncover new causal relationships. Unlike labor-intensive approaches relying on handcrafted features prone to bias we present a novel task of directly learning predictive features from images. We propose an evaluation protocol to assess a model's ability to identify predictive imaging biomarkers and differentiate them from purely prognostic ones by employing statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally developed for estimating the conditional average treatment effect (CATE) for this task which have been assessed primarily for their precision of CATE estimation while overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates the feasibility and potential of our approach in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets. Our code is available at https://github.com/MIC-DKFZ/predictive_image_biomarker_analysis.