Soni Lakshya, Soopramanien Jasen, Acharya Amish, Ashrafian Hutan, Giannarou Stamatia, Fotiadis Nicos, Darzi Ara
Institute of Global Health Innovation, Imperial College London, London, UK.
Royal Marsden Hospital, London, UK.
Radiol Med. 2025 May 3. doi: 10.1007/s11547-025-02013-y.
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Intermediate-stage HCC is often treated with either transcatheter arterial chemoembolisation (TACE) or transcatheter arterial embolisation (TAE). Integrating machine learning (ML) offers the possibility of improving treatment outcomes through enhanced patient selection. This systematic review evaluates the effectiveness of ML models in improving the precision and efficacy of both TACE and TAE for intermediate-stage HCC. A comprehensive search of PubMed, EMBASE, Web of Science, and Cochrane Library databases was conducted for studies applying ML models to TACE and TAE in patients with intermediate-stage HCC. Seven studies involving 4,017 patients were included. All included studies were from China. Various ML models, including deep learning and radiomics, were used to predict treatment response, yielding a high predictive accuracy (AUC 0.90). However, study heterogeneity limited comparisons. While ML shows potential in predicting treatment outcomes, further research with standardised protocols and larger, multi-centre trials is needed for clinical integration.
肝细胞癌（HCC）是全球癌症相关死亡的主要原因之一。中期HCC通常采用经导管动脉化疗栓塞术（TACE）或经导管动脉栓塞术（TAE）进行治疗。整合机器学习（ML）通过优化患者选择，为改善治疗效果提供了可能。本系统评价评估了ML模型在提高TACE和TAE治疗中期HCC的精准度和疗效方面的有效性。对PubMed、EMBASE、科学网和考克兰图书馆数据库进行了全面检索，以查找将ML模型应用于中期HCC患者的TACE和TAE的研究。纳入了7项涉及4017例患者的研究。所有纳入研究均来自中国。使用了包括深度学习和放射组学在内的各种ML模型来预测治疗反应，预测准确率较高（AUC 0.90）。然而，研究的异质性限制了比较。虽然ML在预测治疗结果方面显示出潜力，但需要通过标准化方案以及更大规模的多中心试验进行进一步研究，以便将其整合到临床中。
