Background/Objectives: Hepatocellular carcinoma (HCC) immunotherapy provides limited clinical benefits, partly due to the lack of reliable efficacy biomarkers. Radiomics, which non-invasively analyzes tumor heterogeneity, shows promising potential for predicting treatment outcomes. Methods: The present study systematically evaluated the predictive performance and methodological quality of radiomics models for assessing immunotherapy efficacy in patients with HCC. A literature search was conducted in PubMed, Web of Science, Embase, and the Cochrane Library for studies published up to 21 June 2025, which developed CT- or MRI-based radiomics models to predict immunotherapy efficacy in HCC. Study quality was assessed using the radiomics quality score (RQS) and the METhodological RadiomICs Score (METRICS). Results: A total of 11 studies were included and categorized by immunotherapy regimen: ICIs alone (1/11), ICIs combined with targeted therapy (6/11), and ICIs combined with targeted therapy plus locoregional therapy (4/11). The models primarily predicted treatment response (7/11), overall survival (OS) (4/11), or progression-free survival (PFS) (4/11). In the ICI monotherapy cohort, AUC values for predicting treatment response ranged from 0.705 to 0.772. In the ICI plus targeted therapy cohorts, AUC or concordance index (C-index) values for predicting the above efficacy endpoints were 0.792–0.956, 0.63–0.77, and 0.54–0.837, respectively. In the combination therapy cohorts incorporating locoregional treatment, predictive models showed AUC or C-index values of 0.721–0.92, 0.817–0.838, and 0.59. Quality assessment revealed a median RQS of 15 (range: 11–19) and a median METRICS of 72.5% (range: 56.0–79.5%) across all studies. Conclusions: CT/MRI-based radiomics uses routine imaging to non-invasively quantify whole-tumor phenotype and heterogeneity, enabling repeatable, longitudinal assessment in hepatocellular carcinoma. Evidence suggests that it can help to identify patients likely to benefit from immunotherapy before treatment. However, clinical implementation requires standardized imaging and analysis protocols, external validation, and transparent reporting.
背景/目的：肝细胞癌（HCC）免疫治疗的临床获益有限，部分原因在于缺乏可靠的疗效生物标志物。影像组学通过无创方式分析肿瘤异质性，在预测治疗效果方面展现出良好潜力。方法：本研究系统评估了影像组学模型预测HCC患者免疫治疗疗效的预测性能与方法学质量。通过检索PubMed、Web of Science、Embase和Cochrane图书馆截至2025年6月21日发表的研究，筛选出基于CT或MRI构建影像组学模型以预测HCC免疫治疗疗效的文献。研究质量采用影像组学质量评分（RQS）和影像组学方法学评分（METRICS）进行评估。结果：共纳入11项研究，按免疫治疗方案分为三类：单纯免疫检查点抑制剂（ICIs）（1/11）、ICIs联合靶向治疗（6/11）以及ICIs联合靶向治疗加局部区域治疗（4/11）。模型主要预测治疗反应（7/11）、总生存期（OS）（4/11）或无进展生存期（PFS）（4/11）。在ICI单药治疗队列中，预测治疗反应的受试者工作特征曲线下面积（AUC）值为0.705–0.772。在ICI联合靶向治疗队列中，预测上述疗效终点的AUC或一致性指数（C-index）值分别为0.792–0.956、0.63–0.77和0.54–0.837。在包含局部区域治疗的联合治疗队列中，预测模型的AUC或C-index值分别为0.721–0.92、0.817–0.838和0.59。质量评估显示所有研究的RQS中位数为15分（范围：11–19），METRICS中位数为72.5%（范围：56.0–79.5%）。结论：基于CT/MRI的影像组学利用常规影像无创量化全肿瘤表型与异质性，可在肝细胞癌中实现可重复的纵向评估。现有证据表明其有助于在治疗前识别可能从免疫治疗中获益的患者。然而，临床转化需要标准化的影像采集与分析流程、外部验证以及透明化的报告体系。
肿瘤(癌症)患者之家