A machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy

Ultrasound-guided vacuum-assisted breast biopsy (VABB) has become the standard minimally invasive procedure for diagnosing and treating benign breast lesions. Despite its widespread adoption, postoperative complications such as bruising, residual tumors, and skin injury remain significant clinical challenges that can impact patient outcomes and satisfaction. Current risk assessment methods lack precision, highlighting the need for more sophisticated predictive tools.

We conducted a multicenter retrospective study analyzing 1,064 VABB procedures performed at three medical centers between 2017 and 2025. Using a comprehensive set of 12 preoperative variables including tumor characteristics and anatomical relationships, we developed and validated six machine learning models. The random forest algorithm demonstrated superior performance in our five-fold cross-validation analysis, with particular strength in predicting postoperative bruising and operative duration.

Our predictive model achieved exceptional performance for bruising risk assessment (AUC 0.971, accuracy 96.7%) and moderate surgical duration prediction. SHAP analysis identified three key predictive features: tumor size (mean SHAP value 0.32), blood flow grade (0.28), and distance to pectoralis muscle (0.25). The model maintained strong performance in external validation (AUC 0.945), confirming its generalizability. However, prediction of rare complications like tumor residual showed limited effectiveness (AUC 0.68).

This study presents a clinically validated machine learning tool that accurately predicts common VABB complications, particularly postoperative bruising. By incorporating specific anatomical and tumor characteristics into preoperative planning, surgeons can better anticipate and potentially mitigate these adverse outcomes. The model's integration into clinical practice could enhance surgical decision-making and improve patient counseling regarding expected recovery experiences.

https://www.chictr.org.cn/index.html, identifier ChiCTR2500095736.