Abstract
To explore the association between pretherapeutic contrast-enhanced cone beam breast CT (CE-CBBCT) features and pathological complete response (pCR), and to develop a predictive model that integrates clinicopathological and imaging features. In this prospective study, a cohort of 200 female patients who underwent CE-CBBCT prior to neoadjuvant therapy and surgery was divided into train (n=150) and test (n=50) sets in a 3:1 ratio. Optimal predictive features were identified using univariate logistic regression and recursive feature elimination with cross-validation (RFECV). Models were constructed using XGBoost and evaluated through the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. The performance of combined model was further evaluated across molecular subtypes. Feature significance within the combined model was determined using the SHapley Additive exPlanation (SHAP) algorithm. The model incorporating three clinicopathological and six CE-CBBCT imaging features demonstrated robust predictive performance for pCR, with area under curves (AUCs) of 0.924 in the train set and 0.870 in the test set. Molecular subtype, spiculation, and adjacent vascular sign (AVS) grade emerged as the most influential SHAP features. The highest AUCs were observed for HER2-positive subgroup (train: 0.935; test: 0.844), followed by luminal (train: 0.841; test: 0.717) and triple-negative breast cancer (TNBC; train: 0.760; test: 0.583). SHAP analysis indicated that spiculation was crucial for luminal breast cancer prediction, while AVS grade was critical for HER2-positive and TNBC cases. Integrating clinicopathological and CE-CBBCT imaging features enhanced pCR prediction accuracy, particularly in HER2-positive cases, underscoring its potential clinical applicability.
Published Version
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