Interpretable multimodal deep learning for time-resolved survival prediction after hepatocellular carcinoma resection.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, China.
- Department of General Surgery, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.
- Department of Interventional Medicine Center, The Second People's Hospital of YiBin, Yibin, China.
- Zhejiang Key Laboratory of Multi-omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Department of Radiology, Huzhou Central Hospital, Affiliated to Huzhou University, Huzhou, China.
- Department of Radiology, West China Second Hospital, Sichuan University, Chengdu, China.
- Department of Radiology, Luzhou Hospital of Traditional Chinese Medicine, Luzhou, China.
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China.
- Department of Radiology, West China Second Hospital, Sichuan University, Chengdu, China. [email protected].
Abstract
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.