Tumor microenvironment-specific CT radiomics signature for predicting immunotherapy response in non-small cell lung cancer.
10.12122/j.issn.1673-4254.2025.09.10
- Author:
Qizhi HUANG
1
;
Daipeng XIE
2
;
Lintong YAO
3
;
Qiaxuan LI
4
;
Shaowei WU
3
;
Haiyu ZHOU
1
Author Information
1. Guangdong Provincial Institute of Cardiovascular Diseases, Guangzhou 510080, China.
2. Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China.
3. Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China.
4. Department of Lung Transplantation, Second Affiliated Hospital of Zhejiang University, Hangzhou 310000, China.
- Publication Type:Journal Article
- Keywords:
immune checkpoint inhibitors;
machine learning;
non-small cell lung cancer;
radiomics;
tumor microenvironment
- MeSH:
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
- From:
Journal of Southern Medical University
2025;45(9):1903-1918
- CountryChina
- Language:Chinese
-
Abstract:
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.
