Immunotherapy, particularly with checkpoint inhibitors, has revolutionized non-small cell lung cancer treatment. Enhancing the selection of potential responders is crucial, and researchers are exploring predictive biomarkers. Delta radiomics, a derivative of radiomics, holds promise in this regard. For this study, a meta-analysis was conducted that adhered to PRISMA guidelines, searching PubMed, Embase, Web of Science, and the Cochrane Library for studies on the use of delta radiomics in stratifying lung cancer patients receiving immunotherapy. Out of 223 initially collected studies, 10 were included for qualitative synthesis. Stratifying patients using radiomic models, the pooled analysis reveals a predictive power with an area under the curve of 0.81 (95% CI 0.76–0.86,p< 0.001) for 6-month response, a pooled hazard ratio of 4.77 (95% CI 2.70–8.43,p< 0.001) for progression-free survival, and 2.15 (95% CI 1.73–2.66,p< 0.001) for overall survival at 6 months. Radiomics emerges as a potential prognostic predictor for lung cancer, but further research is needed to compare traditional radiomics and deep-learning radiomics.
免疫疗法，特别是检查点抑制剂的应用，已彻底改变了非小细胞肺癌的治疗格局。提升潜在应答者的筛选精准度至关重要，研究人员正致力于探索预测性生物标志物。作为影像组学的衍生领域，Delta影像组学在此方面展现出潜力。本研究遵循PRISMA指南进行荟萃分析，通过检索PubMed、Embase、Web of Science及Cochrane图书馆中关于Delta影像组学在免疫治疗肺癌患者分层应用的研究。从最初收集的223项研究中，最终纳入10项进行定性综合。通过影像组学模型对患者进行分层，汇总分析显示：该模型对6个月治疗应答的预测效能曲线下面积为0.81（95% CI 0.76–0.86，p<0.001），无进展生存期的汇总风险比为4.77（95% CI 2.70–8.43，p<0.001），6个月总生存期的汇总风险比为2.15（95% CI 1.73–2.66，p<0.001）。影像组学可作为肺癌潜在的预后预测工具，但未来仍需进一步研究以比较传统影像组学与深度学习影像组学的效能差异。
肿瘤(癌症)患者之家