1.Tumor microenvironment-specific CT radiomics signature for predicting immunotherapy response in non-small cell lung cancer.
Qizhi HUANG ; Daipeng XIE ; Lintong YAO ; Qiaxuan LI ; Shaowei WU ; Haiyu ZHOU
Journal of Southern Medical University 2025;45(9):1903-1918
OBJECTIVES:
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.
CONCLUSIONS
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.
Humans
;
Carcinoma, Non-Small-Cell Lung/therapy*
;
Tumor Microenvironment
;
Lung Neoplasms/therapy*
;
Immunotherapy
;
Tomography, X-Ray Computed
;
Nomograms
;
Retrospective Studies
;
Immune Checkpoint Inhibitors/therapeutic use*
;
Prognosis
;
Male
;
Female
;
Radiomics

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