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}.

翻译：识别预测协变量（用于预测个体治疗效果）对于个性化医疗等不同学科的决策至关重要。这些协变量被称为生物标志物，通常从随机对照试验的预处理数据中提取，并应与独立于治疗分配的预后生物标志物区分开来。我们的研究侧重于通过利用预处理图像来发现预测性影像生物标志物（即特定图像特征），以揭示新的因果关系。与依赖易产生偏差的手工特征的劳动密集型方法不同，我们提出了一种直接从图像中学习预测特征的新任务。我们提出了一种评估方案，通过采用统计检验和图像特征归因的综合分析，来评估模型识别预测性影像生物标志物并将其与纯预后标志物区分开来的能力。我们探讨了原本为估计条件平均处理效应（CATE）而开发的深度学习模型对此任务的适用性，这些模型主要因其CATE估计的精确性而被评估，却忽略了影像生物标志物发现能力的评价。我们的概念验证分析证明了我们的方法在从合成结果和真实世界图像数据集中发现和验证预测性影像生物标志物方面的可行性和潜力。我们的代码可在 \url{https://github.com/MIC-DKFZ/predictive_image_biomarker_analysis} 获取。