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Background Third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) resistance poses a significant therapeutic challenge in advanced lung adenocarcinoma. This study aimed to develop and validate a computed tomography (CT)-based habitat radiomics model for predicting response to immunochemotherapy in EGFR-mutant lung adenocarcinoma patients after TKI resistance. Methods This retrospective multicenter study enrolled 475 patients from two medical centers. Patients were allocated to train (N = 332) and external validation (N = 143) cohorts. Habitat imaging was performed using K-means clustering to partition tumors into three distinct subregions. Radiomic features were extracted from both whole-tumor volumes and habitat subregions. A combined model combining clinical, conventional radiomics, and habitat features was constructed using machine learning algorithms and validated through cross-validation and external testing. The primary endpoint was objective response rate (ORR) based on Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria, and overall survival (OS) was used as a secondary endpoint. Results The combined model demonstrated superior predictive performance with area under the curve (AUC) of 0.904 (95% CI: 0.871–0.937) in the train cohort and 0.890 (95% CI: 0.838–0.942) in the validation cohort, significantly outperforming the clinical model, conventional whole-tumor radiomics model, and habitat model (all P 0.001). Moreover, Kaplan–Meier analysis based on the risk groups stratified by the combined model revealed significant survival differences, with high-risk groups showing markedly shorter overall survival in both cohorts (training HR = 3.688, validation HR = 2.823, both log-rank P 0.0001). Conclusion This study developed and externally validated a CT-based habitat radiomics model for predicting response to immunochemotherapy in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance. The combined model achieved improved predictive performance compared with single-modality approaches. These findings suggest that incorporating habitat-based features may enhance the characterization of intratumoral heterogeneity and improve treatment response prediction. Notably, the model demonstrated a high negative predictive value, suggesting its potential to reduce unnecessary treatment in predicted non-responders. Further prospective and multi-center validation is warranted.
Qie et al. (Tue,) studied this question.