Abstract
INTRODUCTION: This study aimed to develop and validate a predictive model for tumor shrinkage patterns in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer patients undergoing neoadjuvant chemotherapy (NAC). METHODS: A retrospective analysis was conducted on 227 HR+/HER2- breast cancer patients with a desire for breast conservation, examining their clinicopathological characteristics, traditional MRI features, and radiomics features. Patients were divided into training and validation cohorts in a 7:3 ratio. Tumor shrinkage patterns were classified into Type I and Type II based on RECIST 1.1 criteria. A clinical model was established using Ki67 quantification and enhancement pattern. Radiomics features were extracted and analyzed using machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). A combined clinical-radiomics model was also developed. RESULTS: The clinical model achieved an area under the curve (AUC) of 0.624 in the training cohort and 0.551 in the validation cohort. The RF radiomics model showed the highest predictive performance with an AUC of 0.826 in the training cohort and 0.808 in the validation cohort. The combined clinical-radiomics model further improved prediction accuracy, with an AUC of 0.831 in the training cohort and 0.810 in the validation cohort. CONCLUSION: Radiomics features based on baseline MRI significantly enhance the prediction of tumor shrinkage patterns in HR+/HER2- breast cancer patients. This approach aids in the early identification of patients likely to benefit from breast-conserving surgery and facilitates timely treatment adjustments.