Predictive Modeling of Anatomy with Genetic and Clinical Data

TL;DR

This paper introduces a semi-parametric generative model that predicts future anatomical scans of patients using baseline images combined with genetic and clinical data, enabling personalized longitudinal analysis.

Contribution

It presents a novel predictive modeling approach that integrates population regression with individual health indicators for anatomy prediction from a single baseline scan.

Findings

Accurate prediction of follow-up anatomical scans in the ADNI cohort

Enables comparison of patient scans to predicted healthy trajectories

Provides a new tool for personalized longitudinal biomarker studies

Abstract

We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject's health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient's scans to the predicted subject-specific healthy anatomical trajectory. The code is available at https://github.com/adalca/voxelorb.