Jin Jin, Jiang Ying, Zhao Yu-Lan, Huang Pin-Tong
Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (J.J., Y.J., Y.-L.Z., P.-L.H.).
Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (J.J., Y.J., Y.-L.Z., P.-L.H.); Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (P.-L.H.); Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, P.R. China (P.-L.H.).
Acad Radiol. 2024 Feb;31(2):467-479. doi: 10.1016/j.acra.2023.09.008. Epub 2023 Oct 20.
Recurrence of hepatocellular carcinoma (HCC) is a major concern in its management. Accurately predicting the risk of recurrence is crucial for determining appropriate treatment strategies and improving patient outcomes. A certain amount of radiomics models for HCC recurrence prediction have been proposed. This study aimed to assess the role of radiomics models in the prediction of HCC recurrence and to evaluate their methodological quality.
Databases Cochrane Library, Web of Science, PubMed, and Embase were searched until July 11, 2023 for studies eligible for the meta-analysis. Their methodological quality was evaluated using the Radiomics Quality Score (RQS). The predictive ability of the radiomics model, clinical model, and the combined model integrating the clinical characteristics with radiomics signatures was measured using the concordance index (C-index), sensitivity, and specificity. Radiomics models in included studies were compared based on different imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound/sonography (US), contrast-enhanced ultrasound (CEUS).
A total of 49 studies were included. On the validation cohort, radiomics model performed better (CT: C-index = 0.747, 95% CI: 0.70-0.79; MRI: C-index = 0.788, 95% CI: 0.75-0.83; CEUS: C-index = 0.763, 95% CI: 0.60-0.93) compared to the clinical model (C-index = 0.671, 95% CI: 0.65-0.70), except for ultrasound-based models (C-index = 0.560, 95% CI: 0.53-0.59). The combined model outperformed other models (CT: C-index = 0.790, 95% CI: 0.76-0.82; MRI: C-index = 0.826, 95% CI: 0.79-0.86; US: C-index = 0.760, 95% CI: 0.65-0.87), except for CEUS-based combined models (C-index = 0.707, 95% CI: 0.44-0.97).
Radiomics holds the potential to predict HCC recurrence and demonstrates enhanced predictive value across various imaging modalities when integrated with clinical features. Nevertheless, further studies are needed to optimize the radiomics approach and validate the results in larger, multi-center cohorts.
肝细胞癌（HCC）复发是其治疗中的主要关注点。准确预测复发风险对于确定合适的治疗策略和改善患者预后至关重要。已经提出了一定数量的用于HCC复发预测的放射组学模型。本研究旨在评估放射组学模型在HCC复发预测中的作用，并评估其方法学质量。
检索Cochrane图书馆、Web of Science、PubMed和Embase数据库，直至2023年7月11日，以获取符合荟萃分析条件的研究。使用放射组学质量评分（RQS）评估其方法学质量。使用一致性指数（C-index）、敏感性和特异性来衡量放射组学模型、临床模型以及将临床特征与放射组学特征相结合的联合模型的预测能力。基于不同的成像方式（包括计算机断层扫描（CT）、磁共振成像（MRI）、超声检查（US）、对比增强超声（CEUS））对纳入研究中的放射组学模型进行比较。
共纳入49项研究。在验证队列中，除基于超声的模型（C-index = 0.560，95% CI：0.53 - 0.59）外，放射组学模型的表现优于临床模型（C-index = 0.671，95% CI：0.65 - 0.70）（CT：C-index = 0.747，95% CI：0.70 - 0.79；MRI：C-index = 0.788，95% CI：0.75 - 0.83；CEUS：C-index = 0.763，95% CI：0.60 - 0.93）。联合模型优于其他模型（CT：C-index = 0.790，95% CI：0.76 - 0.82；MRI：C-index = 0.826，95% CI：0.79 - 0.86；US：C-index = 0.760，95% CI：0.65 - 0.87），除基于CEUS的联合模型（C-index = 0.707，95% CI：0.44 - 0.97）外。
放射组学具有预测HCC复发的潜力，并且与临床特征相结合时在各种成像方式中均显示出增强的预测价值。然而，需要进一步研究来优化放射组学方法并在更大的多中心队列中验证结果。
