Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study.

Objective: Accurate prediction of pathological complete response (pCR) following neoadjuvant therapy (NAT) is critical for optimizing treatment in breast cancer. This study develops and validates an interpretable, cost-effective machine learning (ML) model integrating computed tomography (CT)-based body composition parameters with routine inflammatory and nutritional biomarkers to predict pCR. Methods: In this retrospective single-center study ( Results: Six independent predictors were identified: visceral adipose tissue density, skeletal muscle density, intramuscular adipose tissue content, albumin-to-alkaline phosphatase ratio, systemic inflammation response index, and molecular subtype. The eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance, achieving an area under the curve (AUC) of 0.888 (95% CI [0.837-0.939]) in internal validation and 0.831 (95% CI [0.723-0.938]) in the independent test set. The model exhibited good calibration (Brier score = 0.180). SHAP analysis highlighted the contribution of host-related factors alongside tumor biology. Conclusions: This interpretable ML model effectively integrates host-related body composition and inflammatory-nutritional markers to predict pCR. By utilizing routinely available data, this approach offers a practical, accessible tool for initial risk stratification, complementing existing imaging-based strategies and supporting personalized clinical decision-making.