Interpretable multimodal deep learning for time-resolved survival prediction after hepatocellular carcinoma resection.

Existing algorithms provide coarse risk stratification or static binary predictions, failing to capture the time-varying risk of death.

We developed and externally validated TEMPO-HCC, a multimodal deep survival model with hierarchical interpretability, to estimate individualized overall survival risk trajectories after curative-intent resection.

A discrete-time survival head generated probabilities at 1, 2, 3, and 5 years after surgery.

TEMPO-HCC represents a paradigm shift from static binary classification toward clinically actionable temporal risk prediction.