Multimodal data-driven prediction of postoperative recurrence and survival in hepatocellular carcinoma: a narrative review.

Background and Objective: Hepatocellular carcinoma (HCC) is characterized by high postoperative recurrence rates and poor long-term survival despite advances in surgical and systemic therapies. Accurate prediction of postoperative recurrence and survival risk is critical for individualized surveillance, adjuvant treatment selection, and precision management. With the rapid development of artificial intelligence (AI) and medical informatics, multimodal data-driven models integrating clinical, imaging, pathological, and omics information have emerged as a promising paradigm. This narrative review aims to systematically summarize recent advances in multimodal prediction models for postoperative recurrence and survival in HCC, compare modeling strategies and fusion approaches, and discuss current challenges and future directions for clinical translation. Methods: A narrative literature review was conducted by searching PubMed, Web of Science, and Google Scholar for studies published between 2020 and 2025. Articles focusing on postoperative recurrence or survival prediction in HCC using single-modal or multimodal data were included. Relevant studies were identified using keywords related to HCC, multimodal data, AI, machine learning, deep learning, recurrence, and prognosis. Key Content and Findings: This review summarizes commonly used data modalities, including clinical variables, medical imaging, pathological features, and multi-omics data, and outlines their respective strengths and limitations. Conventional statistical models and AI-based approaches, including non-deep learning and deep learning algorithms, are compared. Particular emphasis is placed on multimodal fusion strategies at the feature level and decision level, with discussion of their methodological characteristics and suitable clinical scenarios. Overall, multimodal models consistently demonstrate superior predictive performance compared with single-modality approaches. However, key challenges remain, including data heterogeneity, limited interpretability of complex models, insufficient external validation, and the predominance of static baseline modeling. Conclusions: Multimodal data-driven prediction models represent a promising strategy for improving postoperative risk stratification and personalized management in HCC. While current evidence highlights their potential advantages over traditional prognostic tools, broader clinical adoption is hindered by methodological limitations and a lack of standardized frameworks. Future research should focus on longitudinal multimodal modeling, multi-center prospective validation, and enhanced model interpretability to facilitate integration into clinical workflows and inform precision oncology-oriented decision-making.