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<p>Hepatocellular carcinoma (HCC) exhibits substantial interpatient heterogeneity, leading to markedly variable outcomes and survival even among patients with similar stages and imaging phenotypes. Mainstream staging systems remain suboptimal, whereas pathology-dependent factors and high-cost genomic assays are neither scalable nor timely for clinical decision-making. 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. We curated a six-center cohort of 1475 patients and integrated multiphasic MRI, postoperative H&amp;E whole-slide images, and perioperative predictors. A discrete-time survival head generated probabilities at 1, 2, 3, and 5 years after surgery. TEMPO-HCC outperformed unimodal models and guideline-based staging systems, achieving C-index of 0.751 and time-dependent AUCs of 0.836, 0.781, 0.812, and 0.680 at 12, 24, 36, and 60 months, respectively, in the external validation cohort. TEMPO-HCC represents a paradigm shift from static binary classification toward clinically actionable temporal risk prediction. Its hierarchical interpretability provides an auditable evidence chain linking macro-scale radiologic phenotypes to micro-scale histopathologic patterns. Importantly, augmenting guideline staging with TEMPO-HCC improved discrimination, enabling personalized postoperative surveillance and risk-adapted clinical management.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Fan Li,
  • Huanchen Yang,
  • Ruishan Liu,
  • Rundong Wang,
  • Xu Feng,
  • Yangyang Xie,
  • Lian Yang,
  • Wei Zhou,
  • Xia Li,
  • Xiang Wei,
  • Dingbin Yuan,
  • Die Hu,
  • Hao Zhang,
  • Jiahui Zhang,
  • Yuxia Nie,
  • Haibo Qu,
  • Fei Wang,
  • Jing Jia,
  • Gang Ning

摘要

Hepatocellular carcinoma (HCC) exhibits substantial interpatient heterogeneity, leading to markedly variable outcomes and survival even among patients with similar stages and imaging phenotypes. Mainstream staging systems remain suboptimal, whereas pathology-dependent factors and high-cost genomic assays are neither scalable nor timely for clinical decision-making. 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. We curated a six-center cohort of 1475 patients and integrated multiphasic MRI, postoperative H&E whole-slide images, and perioperative predictors. A discrete-time survival head generated probabilities at 1, 2, 3, and 5 years after surgery. TEMPO-HCC outperformed unimodal models and guideline-based staging systems, achieving C-index of 0.751 and time-dependent AUCs of 0.836, 0.781, 0.812, and 0.680 at 12, 24, 36, and 60 months, respectively, in the external validation cohort. TEMPO-HCC represents a paradigm shift from static binary classification toward clinically actionable temporal risk prediction. Its hierarchical interpretability provides an auditable evidence chain linking macro-scale radiologic phenotypes to micro-scale histopathologic patterns. Importantly, augmenting guideline staging with TEMPO-HCC improved discrimination, enabling personalized postoperative surveillance and risk-adapted clinical management.