Functional Individualized Treatment Regimes with Imaging Features

TL;DR

This paper introduces a new data-driven approach to incorporate patient-specific imaging features into personalized treatment regimes, enhancing precision medicine with interpretable and consistent estimators.

Contribution

It proposes a novel method that models imaging data as a stochastic process and employs smoothing techniques to construct interpretable features for individualized treatment decisions.

Findings

Method successfully applied to Alzheimer's dataset

Estimators shown to be consistent under mild conditions

Improves decision support with imaging features

Abstract

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.