Dynamic survival risk prediction with time‐varying high‐dimensional images

2026/07/17 by Bingfan Liu, Haolun Shi, Shu Jiang +4

paper · doi:10.1002/cjs.70055

crossref issued 2026/07/17 · crossref published 2026/07/17 · crossref published-online 2026/07/17 · crossref created 2026/07/17 · crossref deposited 2026/07/17 · crossref indexed 2026/08/05

Abstract

Abstract Integrating longitudinal data with survival models is a prevalent strategy for dynamic survival risk prediction while accounting for subjects' longitudinally observed variables. However, existing methods primarily focus on scalar longitudinal data and seldom tackle the complexities associated with high‐dimensional longitudinal imaging data. This article introduces a new approach that effectively incorporates longitudinal medical images as features for dynamic risk prediction. This approach ensures interpretability, enhances computational efficiency, and performs robustly even with small datasets. Our method achieves high prediction accuracy, as validated through extensive simulation studies and a real‐world application to Alzheimer's disease data. To the best of our knowledge, this is the first attempt to use longitudinal medical images for predicting dynamic survival risk.

Citations

• L1-Regularized Functional Support Vector Machine
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• Deep segmentation networks predict survival of non-small cell lung cancer
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