In recent years, significant advancements in immunotherapy for hepatocellular carcinoma (HCC) have shown the potential to further improve the prognosis of patients with advanced HCC. However, in clinical practice, there is still a lack of effective biomarkers for identifying the patient who would benefit from immunotherapy and predicting the tumor response to immunotherapy. The immune microenvironment of HCC plays a crucial role in tumor development and drug responses. However, due to the complexity of immune microenvironment, currently, no single pathological or molecular biomarker can effectively predict tumor responses to immunotherapy. Magnetic resonance imaging (MRI) images provide rich biological information; existing studies suggest the feasibility of using MRI to assess the immune microenvironment of HCC and predict tumor responses to immunotherapy. Nevertheless, there are limitations, such as the suboptimal performance of conventional MRI sequences, incomplete feature extraction in previous deep learning methods, and limited interpretability. Further study needs to combine qualitative features, quantitative parameters, multi-omics characteristics related to the HCC immune microenvironment, and various deep learning techniques in multi-center research cohorts. Subsequently, efforts should also be undertaken to construct and validate a visual predictive tool of tumor response, and assess its predictive value for patient survival benefits. Additionally, future research endeavors must aim to provide an accurate, efficient, non-invasive, and highly interpretable method for predicting the effectiveness of immune therapy.
近年来，肝癌免疫治疗领域取得显著进展，为改善晚期肝癌患者预后带来新希望。然而，临床实践中仍缺乏有效生物标志物来筛选免疫治疗获益人群及预测肿瘤治疗反应。肝癌免疫微环境在肿瘤发生发展与药物应答中起关键作用，但由于其复杂性，目前尚无单一病理或分子标志物能有效预测免疫治疗反应。磁共振成像（MRI）可提供丰富的生物学信息，现有研究表明利用MRI评估肝癌免疫微环境并预测免疫治疗反应具有可行性。但当前研究仍存在局限，如常规MRI序列性能欠佳、既往深度学习方法特征提取不完整、模型可解释性有限等问题。未来需在多中心研究队列中，结合肝癌免疫微环境相关的定性特征、定量参数、多组学特征与多种深度学习技术开展深入研究，进而构建并验证肿瘤治疗反应的可视化预测工具，评估其对患者生存获益的预测价值。此外，还需建立精准、高效、无创且具有高可解释性的免疫治疗疗效预测方法。
肿瘤(癌症)患者之家