Tensor-Based Individualized Treatment Rules for Neuroimaging Applications

Oregon Convention Center 

Precision medicine aims to uncover the optimal personalized treatment plan, offering thoughtful decision support based on the characteristics of each patient. With the rapid advancement of medical imaging technology, integrating high-order patient-specific imaging features into individualized treatment rules has become critical. We introduce a novel, data-driven approach that utilizes both imaging data and additional variables to guide the selection of the best treatment options. Specifically, this study employs tensor and scalar covariates within a regression framework, estimating optimal individualized treatment rules through Tucker decomposition. This method effectively reduces the number of parameters, leading to efficient estimation and feasible computation. For handling high-dimensional tensors, we further employ sparse Tucker decomposition to reduce the parameter number. Additionally, we develop an alternating updating algorithm that incorporates an Alternating Direction Method of Multipliers. Under certain conditions, we show the asymptotic properties of these estimators. The numerical performance of our method is validated through simulations and high-order medical imaging data applications.

individualized treatment rules

tensor regression

imaging data 

Biometrics Section