biostudies-literatureUnknown12Pingdong S<h4>Background</h4>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.<h4>Methods</h4>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 lFrontiers in surgery1641441https://www.ebi.ac.uk/biostudies/studies/S-EPMC12450878biostudies-literatureA machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy.PMC12450878Yan LJipeng ZYunzhi SYihan SShipeng ZJianchun CWenjun WXinran SGang LTing RJinrui LXiang FPingdong SQiushi LShengsheng YXingai JfalseA machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy.<h4>Background</h4>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.<h4>Methods</h4>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 l2025-01-01T00:00:00Z20252026-05-30T03:09:23.218Z2026-05-30T03:06:25.471ZS-EPMC124508784098949110.3389/fsurg.2025.1641441