  Identifying predictive biomarkers, which forecast individual treatment
effectiveness, is crucial for personalized medicine and informs decision-making
across diverse disciplines. These biomarkers are extracted from pre-treatment
data, often within randomized controlled trials, and have to be distinguished
from prognostic biomarkers, which are independent of treatment assignment. Our
study focuses on the discovery of predictive imaging biomarkers, aiming to
leverage pre-treatment images to unveil new causal relationships. Previous
approaches relied on labor-intensive handcrafted or manually derived features,
which may introduce biases. In response, we present a new task of discovering
predictive imaging biomarkers directly from the pre-treatment images to learn
relevant image features. We propose an evaluation protocol for this task to
assess a model's ability to identify predictive imaging biomarkers and
differentiate them from prognostic ones. It employs statistical testing and a
comprehensive analysis of image feature attribution. We explore the suitability
of deep learning models originally designed for estimating the conditional
average treatment effect (CATE) for this task, which previously have been
primarily assessed for the precision of CATE estimation, overlooking the
evaluation of imaging biomarker discovery. Our proof-of-concept analysis
demonstrates promising results in discovering and validating predictive imaging
biomarkers from synthetic outcomes and real-world image datasets.
