分子影像学杂志2026，Vol.49Issue(3)：294-303,10.DOI:10.12122/j.issn.1674-4500.2026.03.03
无创预测非小细胞肺癌患者的免疫治疗疗效:基于代谢重编程的CT影像基因组学模型
Noninvasive prediction of immunotherapy response in non-small cell lung cancer patients:a CT radiogenomics signature based on metabolic reprogramming
摘要
Abstract
Objective To develop and validate a CT-based radiogenomics model driven by metabolic reprogramming for the noninvasive prediction of immunotherapy efficacy in patients with non-small cell lung cancer(NSCLC).Methods This study integrated transcriptomic,clinical,and CT image data.Differentially expressed genes(DEGs)associated with metabolic reprogramming in NSCLC were identified using The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)databases.A metabolic reprogramming risk score(MRRS)model was established via Cox regression analysis,and its correlation with the tumor immune microenvironment was evaluated.Radiomic features were extracted from CT images of NSCLC patients in The Cancer Imaging Archive(TCIA)utilizing PyRadiomics.Following feature selection via LASSO regression,a radiogenomics model with MRRS as the target variable was constructed and evaluated.Furthermore,an independent validation cohort comprising 206 patients with advanced NSCLC who received immunotherapy was enrolled from Shanxi Provincial People's Hospital.ROC curves were employed to quantify the predictive performance of the model for immunotherapy efficacy.Results A total of 156 metabolic reprogramming-related DEGs were identified.From these candidates,nine key genes were selected to construct the MRRS model.In the TCGA-NSCLC training set,the AUCs for predicting 1-,3-and 5-year overall survival were 0.638,0.685 and 0.648,respectively,which were higher than those based on conventional clinical parameters.Patients in the high-risk group exhibited decreased immune cell infiltration(P<0.01)and poorer overall survival(P<0.001).Subsequently,six optimal radiomic features were selected from CT images to formulate the radiogenomics model,which achieved AUCs of 0.742 and 0.726 in the training and test sets for predicting MRRS,respectively.In the independent immunotherapy cohort,the radiogenomics model yielded an AUC of 0.704 for predicting treatment efficacy,effectively distinguishing responders from non-responders.Conclusion The proposed CT-based radiogenomics model enables the noninvasive assessment of metabolic reprogramming status in NSCLC.Furthermore,it demonstrates promising clinical potential for predicting the efficacy of immunotherapy.关键词
CT图像/影像基因组学/代谢重编程/非小细胞肺癌/免疫治疗Key words
CT images/radiogenomics/metabolic reprogramming/non-small cell lung cancer/immunotherapy引用本文复制引用
刘小军,武炜,冯对平..无创预测非小细胞肺癌患者的免疫治疗疗效:基于代谢重编程的CT影像基因组学模型[J].分子影像学杂志,2026,49(3):294-303,10.基金项目
山西省基础研究计划资助项目(202403021212276) （202403021212276）
