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AI / informaticsNPJ digital medicine · 2026

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

Fan Li, Huanchen Yang, Ruishan Liu … Gang Ning

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SurgeryAI / informatics

TEMPO-HCC model accurately predicts survival after hepatocellular carcinoma resection.

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

Fan Li et al. · NPJ digital medicine · 2026

Background

Hepatocellular carcinoma (HCC) exhibits substantial interpatient heterogeneity, leading to markedly variable outcomes and survival even among patients with similar stages and imaging phenotypes.

Methods

We curated a six-center cohort of 1475 patients and integrated multiphasic MRI, postoperative H&E whole-slide images, and perioperative predictors.

n = 1475 patients

C-index of 0.751

Results

This indicates the model's high accuracy in predicting survival after surgery

n = 1475 patients

More results

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.

More results

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.

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Conclusion

Importantly, augmenting guideline staging with TEMPO-HCC improved discrimination, enabling personalized postoperative surveillance and risk-adapted clinical management.

Post

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

TEMPO-HCC model accurately predicts survival after hepatocellular carcinoma resection.

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