[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124370-en":3,"doc-seo-124370-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124370,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",7,"Healthcare","A machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy","Ultrasound-guided vacuum-assisted breast biopsy (VABB) is widely used to diagnose and treat benign breast lesions, yet postoperative complications such as bruising, residual tumors, and skin injury remain clinically challenging. A multicenter retrospective study analyzed 1,064 VABB procedures from three centers (2017–2025) to build and validate six machine learning models using 12 preoperative variables. The random forest model showed the best performance, especially for bruising risk assessment, with SHAP highlighting tumor size, blood flow grade, and distance to pectoralis muscle. External validation supported generalizability, while rare complication prediction remained limited.","TYPE Clinical Trial  \nPUBLISHED 08 September 2025 DOI 10.3389/fsurg.2025.1641441  \nEDITED BY  \nMohamed Shehata,  \nMidway College, United States  \nREVIEWED BY  \nTorill Sauer,  \nUniversity of Oslo, Norway Gianluca Marcaccini, University of Siena, Italy  \n*CORRESPONDENCE  \nFei Xiang  \n [fx057619@sina.com](fx057619@sina.com)[ ](fx057619@sina.com)Cui Jianchun  \n [cjc7162003@aliyun.com](cjc7162003@aliyun.com)  \n†These authors have contributed equally to this work and share first authorship ‡These authors have contributed equally to  \nthis work  \nRECEIVED 05 June 2025  \nACCEPTED 06 August 2025  \nPUBLISHED 08 September 2025  \nCITATION  \nPingdong S, Xinran S, Yunzhi S, Yihan S, Shipeng Z, Yan L, Qiushi L, Jipeng Z, Ting R, Wenjun W, Shengsheng Y, Gang L, Jinrui L, Xingai J, Xiang F and Jianchun C (2025) A machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy.  \nFront. Surg. 12:1641441 .  \ndoi: 10.3389/fsurg.2025.1641441  \nCOPYRIGHT  \n© 2025 Pingdong, Xinran, Yunzhi, Yihan, Shipeng, Yan, Qiushi, Jipeng, Ting, Wenjun, Shengsheng, Gang, Jinrui, Xingai, Xiang 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) and the copyright owner(s) 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.  \nA machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy  \nSun Pingdong1,2,3†, Shao Xinran1†, Shen Yunzhi2‡, Sun Yihan4‡, Zheng Shipeng5‡, Li Yan6‡, Li Qiushi1, Zheng Jipeng7, Ruan Ting8, Wu Wenjun9, Yao Shengsheng1, Li Gang10, Liu Jinrui1, Ju Xingai7, Fei Xiang1* and Cui Jianchun1*  \n1Department of Thyroid and Breast Surgery, People’s Hospital of China Medical University, Shenyang, China, 2Graduate School, Dalian Medical University, Dalian, China, 3Liaoyang County Hospital, Liaoyang, China, 4 Department of Cardiology, People’s Hospital of China Medical University, Shenyang, China, 5Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, 6Department of Breast Surgery, The Second People’s Hospital of Hami, Hami, China, 7Department of General Medicine, People’s Hospital of China Medical University, Shenyang, China, 8The Fourth Affiliated Hospital, Liaoning University of Traditional Chinese Medicine, Shenyang, China, 9Changchun University of Chinese Medicine, Changchun, China, 10Department of Emergency Medicine, People’s Hospital of China Medical University, Shenyang, China  \nBackground: 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. Methods: 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.  \nResults: 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), bloo","cbCaipe1oNJ43fIz","https://ap.wps.com/l/cbCaipe1oNJ43fIz","pdf",1654089,1,10,"English","en",105,"# Background\n## Current challenges in VABB risk assessment\n# Methods\n## Study design and dataset\n## Machine learning model development and validation\n## Five-fold cross-validation\n# Results\n## Bruising and surgical duration prediction performance\n## Explainability via SHAP feature importance\n## External validation and limitations for rare complications\n# Conclusions\n## Clinical utility for preoperative planning and counseling\n# Clinical trial registration\n## Registration identifier","[{\"question\":\"What postoperative complications does the predictive model target in vacuum-assisted breast biopsy?\",\"answer\":\"The study focuses on predicting common complications including postoperative bruising, surgical duration-related outcomes, and tumor residual, as well as skin injury risks discussed in the background.\"},{\"question\":\"How was the machine learning model developed and validated?\",\"answer\":\"A multicenter retrospective cohort of 1,064 VABB procedures was analyzed using 12 preoperative variables, and six machine learning models were trained. The random forest model was assessed with five-fold cross-validation and further tested using external validation.\"},{\"question\":\"Which preoperative features were most important according to SHAP analysis?\",\"answer\":\"SHAP analysis identified tumor size, blood flow grade, and distance to the pectoralis muscle as three key predictive features.\"}]","A machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy | PDF",1785821855,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-based-predictive-model-for-complication-risks-in-vacuum-assisted-breast-biopsy","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-based-predictive-model-for-complication-risks-in-vacuum-assisted-breast-biopsy/124370/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What postoperative complications does the predictive model target in vacuum-assisted breast biopsy?","Question",{"text":75,"@type":76},"The study focuses on predicting common complications including postoperative bruising, surgical duration-related outcomes, and tumor residual, as well as skin injury risks discussed in the background.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning model developed and validated?",{"text":80,"@type":76},"A multicenter retrospective cohort of 1,064 VABB procedures was analyzed using 12 preoperative variables, and six machine learning models were trained. 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