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.

翻译：识别预测性生物标志物——即预测个体治疗效果的指标——对于个性化医疗至关重要，并为跨学科决策提供依据。这些生物标志物从治疗前的数据中提取，通常在随机对照试验中进行，且必须与独立于治疗分配的预后性生物标志物区分开来。我们的研究聚焦于发现预测性影像生物标志物，旨在利用治疗前图像揭示新的因果关系。以往的方法依赖于劳动密集型的手工或人工推导特征，这可能引入偏差。为此，我们提出了一项直接从治疗前图像中发现预测性影像生物标志物的新任务，以学习相关的图像特征。我们为此任务提出了一个评估方案，用以评估模型识别预测性影像生物标志物并将其与预后性标志物区分开来的能力。该方案采用统计检验和图像特征归因的综合分析。我们探讨了原本为估计条件平均处理效应（CATE）而设计的深度学习模型在此任务中的适用性，这些模型先前主要评估的是CATE估计的精度，而忽视了影像生物标志物发现的评估。我们的概念验证分析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展示了有希望的结果。