Radiomics Model Predicts Efficacy of First-Line CDK4/6 Inhibitor Combined with Endocrine Therapy in HR+/HER2- Advanced Breast Cancer.

INTRODUCTION: The combination of CDK4/6 inhibitors and endocrine therapy is the first-line standard regimen for advanced HR⁺/HER₂⁻ breast cancer. However, due to tumor heterogeneity, some patients derive limited benefit. This study aimed to develop and validate a radiomics-based model to predict treatment efficacy. MATERIALS AND METHODS: This retrospective study screened 481 patients with advanced HR+/HER2- breast cancer. After applying inclusion and exclusion criteria, 102 first-line patients were enrolled and randomly split into training (n = 82) and testing (n = 20) sets. Radiomic features were extracted from baseline contrast-enhanced CT images of multiple metastatic sites. LASSO and recursive feature elimination were used for feature selection. Cox proportional-hazards and XGBoost algorithms were employed to develop models for predicting progression-free survival (PFS) and treatment response, respectively. Model performance was evaluated using the concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). RESULTS: The combined radiomics-clinical model achieved C-index values of 0.85 (95% CI: 0.73-0.98) and 0.76 (95% CI: 0.54-0.99) in the training and testing sets, respectively, for PFS prediction, significantly outperforming both the clinical-only and radiomics-only models (all P < 0.05). In the test set, time-dependent AUC values at 6, 12, and 18 months were 0.938, 0.785, and 0.836, respectively (training set: 0.895, 0.897, and 0.960). For response prediction, the best overall response model achieved an AUC of 0.80, and the early treatment response model achieved an AUC of 0.70. CONCLUSION: In this exploratory cohort, radiomics models show preliminary promise in predicting the efficacy of CDK4/6 inhibitor plus endocrine therapy in advanced breast cancer. However, these findings require validation in larger, multicenter, independent cohorts before clinical implementation.