Non-invasive prediction of NSCLC immunotherapy efficacy and tumor microenvironment through unsupervised machine learning-driven CT Radiomic subtypes: a multi-cohort study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.
- Hubei Key Laboratory of Molecular Imaging, Wuhan, China.
- Department of Radiotherapy, Fuzong Clinical Medical College (900th Hospital), Fujian Medical University, Fuzhou, China.
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China.
- School of Medicine, Wuhan College of Arts and Science, Wuhan, China.
- Department of Medical Imaging, Geriatric Hospital Affiliated with Wuhan University of Science and Technology, Wuhan, China.
Abstract
Radiomics analyzes quantitative features from medical images to reveal tumor heterogeneity, offering new insights for diagnosis, prognosis, and treatment prediction. This study explored radiomics based biomarkers to predict immunotherapy response and its association with the tumor microenvironment in non-small cell lung cancer (NSCLC) using unsupervised machine learning models derived from CT imaging. This study included 1539 NSCLC patients from seven independent cohorts. For 1834 radiomic features extracted from 869 NSCLC patients, K-means unsupervised clustering was applied to identify radiomic subtypes. A random forest model extended subtype classification to external cohorts, model accuracy, sensitivity, and specificity were evaluated. By conducting bulk RNA sequencing (RNA-seq) and single-cell transcriptome sequencing (scRNA-seq) of tumors, the immune microenvironment characteristics of tumors can be obtained to evaluate the association between radiomic subtypes and immunotherapy efficacy, immune scores, and immune cells infiltration. Unsupervised clustering stratified NSCLC patients into two subtypes (Cluster 1 and Cluster 2). Principal component analysis confirmed significant distinctions between subtypes across all cohorts. Cluster 2 exhibited significantly longer median overall survival (35 vs. 30 months, P = 0.006) and progression-free survival (19 vs. 16 months, P = 0.020) compared to Cluster 1. Multivariate Cox regression identified radiomic subtype as an independent predictor of overall survival (HR: 0.738, 95% CI 0.583-0.935, P = 0.012), validated in two external cohorts. Bulk RNA seq showed elevated interaction signaling and immune scores in Cluster 2 and scRNA-seq demonstrated higher proportions of T cells, B cells, and NK cells in Cluster 2. This study establishes a radiomic subtype associated with NSCLC immunotherapy efficacy and tumor immune microenvironment. The findings provide a non-invasive tool for personalized treatment, enabling early identification of immunotherapy-responsive patients and optimized therapeutic strategies.