人工智能在肝细胞癌中的转化悖论：从算法过度工程到现实世界的临床应用
The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.
摘要(中文译文)
综合2025至2026年的近期文献，本小型综述批判性评估了人工智能（AI）在肝细胞癌（HCC）中从静态模式识别向机制驱动的空间与影像诊断学的方法学演进。我们揭示了一个关键的转化悖论：尽管自监督视觉基础模型（VFMs）、用于基于H&E的虚拟分子谱分析的无监督域适应以及AI协同的空间转录组学在理论上能够解析瘤内异质性并缓解跨病因域偏移，但这些复杂架构尚未在真实世界的多扫描仪队列中得到验证。相反，稳健的比较分析通过证明算法效用高度依赖于数据，对主流的多模态叙事提出了挑战：尽管复杂AI对于高维场景至关重要，但在低维生存预测中，传统Cox模型相较于高度工程化的大语言模型（LLMs）仍保持高度稳健且具有竞争力，这表明算法复杂性并不必然等同于临床效用。为打破这一转化僵局，本小型综述认为，该领域必须迫切地从脆弱的可解释性转向本质可解释的架构——如概念瓶颈模型（CBMs）——并呼吁在未来24个月内将这些透明系统严格整合到II期“机会窗口”随机试验和动态监管评估框架中。
Abstract
Synthesizing recent literature from 2025 to 2026, this mini-review critically evaluates the methodological evolution of artificial intelligence (AI) in hepatocellular carcinoma (HCC) from static pattern recognition to mechanism-driven spatial and imaging diagnostics. We expose a critical translational paradox: although self-supervised Vision Foundation Models (VFMs), unsupervised domain adaptation for H&E-based virtual molecular profiling, and AI-synergized spatial transcriptomics theoretically decode intratumoral heterogeneity and mitigate cross-etiology domain shifts, these complex architectures remain unvalidated in real-world multi-scanner cohorts. Conversely, robust comparative analyses challenge the prevailing multi-modal narrative by demonstrating that algorithmic utility is highly data-dependent: although complex AI is crucial for high-dimensional settings, traditional Cox models remain highly robust and competitive compared to highly engineered large language models (LLMs) in low-dimensional survival prediction, indicating that algorithmic complexity does not inevitably equal clinical utility. To break this translational impasse, this mini-review argues that the field must urgently pivot away from fragile explainability toward inherently interpretable architectures-such as concept bottleneck models (CBMs)-and calls for the rigorous integration of these transparent systems into Phase II "window-of-opportunity" randomized trials and dynamic regulatory evaluation frameworks within the next 24 months.
如何引用
AMA
Chen Li, Yuka Yanase, Ming-Quan Pang. The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.. Frontiers in oncology. 2026; doi:10.3389/fonc.2026.1848190.
APA
Chen Li, Yuka Yanase, Ming-Quan Pang (2026). The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.. Frontiers in oncology. https://doi.org/10.3389/fonc.2026.1848190
GB/T 7714
Chen Li, Yuka Yanase, Ming-Quan Pang. The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.[J]. Frontiers in oncology, 2026 doi:10.3389/fonc.2026.1848190.
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