通过治疗效应分析增强预测性影像生物标志物发现
Enhancing predictive imaging biomarker discovery through treatment effect analysis
AI总结：
本研究提出直接从图像学习预测性影像生物标志物的新任务，设计了结合统计检验与特征归因的评估协议，验证了CATE深度学习模型用于该任务的可行性，为影像生物标志物发现提供新方法。
摘要：
识别可预测个体治疗效果的预测性协变量，对个性化医疗等不同学科的决策至关重要。这些被称为生物标志物的协变量从治疗前数据中提取，通常在随机对照试验中获取，需与独立于治疗分配的预后生物标志物区分开。本研究聚焦于发现预测性影像生物标志物（即特定图像特征），利用治疗前图像揭示新的因果关系。不同于依赖手工设计特征、易产生偏差且耗时耗力的方法，我们提出了直接从图像中学习预测特征的新任务。我们提出了一套评估协议，通过采用统计检验和全面的图像特征归因分析，来评估模型识别预测性影像生物标志物并将其与纯预后生物标志物区分开的能力。我们探究了最初用于估计条件平均治疗效应（CATE）的深度学习模型对该任务的适用性——这类模型此前主要因CATE估计的精确性被评估，却忽略了影像生物标志物发现方面的评估。我们的概念验证分析表明，我们的方法在从合成结果和真实世界图像数据集中发现并验证预测性影像生物标志物方面具备可行性与潜力。我们的代码可在https://github.com/MIC-DKFZ/predictive_image_biomarker_analysis获取。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
