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

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. 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). 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. 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.