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@ARTICLE{Xiao:294874,
      author       = {S. Xiao$^*$ and L. Klein$^*$ and J. Petersen$^*$ and P.
                      Vollmuth$^*$ and P. F. Jaeger$^*$ and K. Maier-Hein$^*$},
      title        = {{E}nhancing predictive imaging biomarker discovery through
                      treatment effect analysis},
      publisher    = {arXiv},
      reportid     = {DKFZ-2024-02584},
      year         = {2024},
      abstract     = {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
                      $\url{https://github.com/MIC-DKFZ/predictive_image_biomarker_analysis}.$},
      keywords     = {Image and Video Processing (eess.IV) (Other) / Artificial
                      Intelligence (cs.AI) (Other) / Computer Vision and Pattern
                      Recognition (cs.CV) (Other) / Machine Learning (cs.LG)
                      (Other) / FOS: Electrical engineering, electronic
                      engineering, information engineering (Other) / FOS: Computer
                      and information sciences (Other)},
      cin          = {E230 / E290},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)E290-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2406.02534},
      url          = {https://inrepo02.dkfz.de/record/294874},
}