南方医科大学学报2025，Vol.45Issue(9)：1903-1918,16.DOI:10.12122/j.issn.1673-4254.2025.09.10
肿瘤微环境特异性CT影像组学标签预测非小细胞肺癌免疫治疗疗效
Tumor microenvironment-specific CT radiomics signature for predicting immunotherapy response in non-small cell lung cancer
摘要
Abstract
Objective To construct a nomogram for predicting the efficacy of immune checkpoint inhibitors(ICIs)in advanced non-small cell lung cancer(aNSCLC)by integrating chest CT radiomics signature that reflects the tumor microenvironment(TME)and clinical parameters of the patients.Methods Transcriptomic and CT imaging data from TCGA,GEO and TCIA databases were integrated for weighted gene co-expression network analysis(WGCNA)of the GEO cohort to identify the immunotherapy-related genes(IRGs)associated with ICIs response.A prognostic model was built using these IRGs in the TCGA cohort to assess immune microenvironment features across different risk groups.Radiomics features were extracted from TCIA lung_3 cohort using PyRadiomics,and 94 features showing strong association with IRGs(|r|>0.4)were selected.A retrospective cohort consisting of 210 aNSCLC patients receiving first-line ICIs at Guangdong Provincial People's Hospital was analyzed and divided into training(n=147)and validation(n=63)groups.Least absolute shrinkage and selection operator was used for radiomic features selection,and logistic regression was applied to construct a combined clinical-radiomic model and nomogram for predicting ICIs therapy response.The performance of the model was evaluated using ROC curve,calibration curve,and decision curve analysis.Results WGCNA identified 84 IRGs enriched in immune activation pathways.The combined model outperformed individual models in both the training(AUC=0.725,95%CI:0.644-0.807)and validation cohorts(AUC=0.706,95%CI:0.577-0.836).Calibration curve and decision curve analyses confirmed the clinical efficacy of the nomogram for predicting ICIs therapy response in aNSCLC patients.Conclusion The genomic-radiomic-clinical multidimensional predictive framework established in this study provides an interpretable biomarker combination and clinical decision-making tool for evaluating ICIs efficacy in aNSCLC,potentially facilitating personalized immunotherapy decision-making.关键词
非小细胞肺癌/免疫检查点抑制剂/肿瘤微环境/机器学习/影像组学Key words
non-small cell lung cancer/immune checkpoint inhibitors/tumor microenvironment/machine learning/radiomics引用本文复制引用
黄启智,谢戴鹏,姚霖彤,李洽轩,吴少伟,周海榆..肿瘤微环境特异性CT影像组学标签预测非小细胞肺癌免疫治疗疗效[J].南方医科大学学报,2025,45(9):1903-1918,16.基金项目
国家自然科学基金(82472064) （82472064）
广东省国际科技合作计划(2022A0505050048) （2022A0505050048）
广东省自然科学基金(2024A1515012369) （2024A1515012369）
北京希思科临床肿瘤学研究基金(Y-HS202102-0038) Supported by National Natural Science Foundation of China(82472064). （Y-HS202102-0038）
