Precision medicine seeks to discover an optimal personalized treatment plan and thereby provide informed and principled decision support, based on the characteristics of individual patients. With recent advancements in medical imaging, it is crucial to incorporate patient-specific imaging features in the study of individualized treatment regimes. We propose a novel, data-driven method to construct interpretable image features which can be incorporated, along with other features, to guide optimal treatment regimes. The proposed method treats imaging information as a realization of a stochastic process, and employs smoothing techniques in estimation. We show that the proposed estimators are consistent under mild conditions. The proposed method is applied to a dataset provided by the Alzheimer's Disease Neuroimaging Initiative.

翻译：精准医学旨在根据个体患者特征，发现最优个性化治疗方案，从而提供基于证据的原则性决策支持。随着医学影像技术的进步，将患者特异性影像特征纳入个体化治疗方案研究至关重要。我们提出了一种新颖的数据驱动方法，用于构建可解释的影像特征，这些特征可与其他特征结合，指导最优治疗方案。所提方法将影像信息视为随机过程的实现，并在估计过程中采用平滑技术。我们证明，在温和条件下，所提估计量具有相合性。该方法被应用于阿尔茨海默病神经影像学倡议提供的数据集。