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CLINICAL TRIAL article

Front. Surg.

Sec. Surgical Oncology

Volume 12 - 2025 | doi: 10.3389/fsurg.2025.1641441

A Machine Learning-Based Predictive Model for Complication Risks in Vacuum-Assisted Breast Biopsy

Provisionally accepted
Pingdong  SunPingdong Sun1Xinran  ShaoXinran Shao1Yunzhi  ShenYunzhi Shen2Yihan  SunYihan Sun3Shipeng  ZhengShipeng Zheng4Yan  LiYan Li5Qiushi  LiQiushi Li1Jipeng  ZhengJipeng Zheng1Ruan  TingRuan Ting6Wenjun  WuWenjun Wu7Shengsheng  YaoShengsheng Yao1Gang  LiGang Li8Jinrui  LiuJinrui Liu1Xiang  FeiXiang Fei1*Xingai  JuXingai Ju9Cui  JianchunCui Jianchun1
  • 1Department of Thyroid and Breast Surgery, Liaoning Province People's Hospital, shenyang, China
  • 2Dalian Medical University Graduate School, Dalian, China
  • 3Department of Cardiology ,Liaoning Province People's Hospital, shenyang, China
  • 4The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
  • 5The Second People's Hospital of Hami City, xinjiang, China
  • 6Liaoning University of Traditional Chinese Medicine, Shenyang, China
  • 7Changchun University of Chinese Medicine, Changchun, China
  • 8Department of Emergency,Liaoning Provincie People's Hospital, shenyang, China
  • 9Department of General Medicine, Liaoning Provincial People's Hospital, shenyang, China

The final, formatted version of the article will be published soon.

Background: 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 2017-2025. 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.Results: 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.

Keywords: vacuum-assisted breast biopsy, machine learning, Prediction model, Postoperative Complications, Breast tumor

Received: 05 Jun 2025; Accepted: 06 Aug 2025.

Copyright: © 2025 Sun, Shao, Shen, Sun, Zheng, Li, Li, Zheng, Ting, Wu, Yao, Li, Liu, Fei, Ju and Jianchun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Xiang Fei, Department of Thyroid and Breast Surgery, Liaoning Province People's Hospital, shenyang, China

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