[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-381839-105":59,"doc-detail-381839-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","multifactor-articial-intelligence-model-assists-axillary-lymph-node-surgery-in-breast-cancer-after-neoadjuvant-chemotherapy-multicenter-retrospective-cohort-study","Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy - multicenter retrospective cohort study","","A retrospective multicenter study evaluates an AI-assisted surgical pipeline to detect axillary lymph node (ALN) metastasis after neoadjuvant chemotherapy (NAC) in breast cancer. Using longitudinal radiomics from pre- and post-NAC magnetic resonance images, the study selects significant features and builds a stacking machine learning model. The model shows strong discrimination with AUCs of 0.958, 0.881, and 0.882 across cohorts. AI-assisted surgery reduces false negative rates and may limit unnecessary axillary lymph node dissection.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multifactor-articial-intelligence-model-assists-axillary-lymph-node-surgery-in-breast-cancer-after-neoadjuvant-chemotherapy-multicenter-retrospective-cohort-study/381839/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multifactor-articial-intelligence-model-assists-axillary-lymph-node-surgery-in-breast-cancer-after-neoadjuvant-chemotherapy-multicenter-retrospective-cohort-study/381839.png","ImageObject",300,407,{"name":92,"@type":93},"4398046744996","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address after neoadjuvant chemotherapy in breast cancer?","Question",{"text":112,"@type":113},"The study targets the high false negative rate of sentinel lymph node biopsy, which can lead to unnecessary axillary lymph node dissection after NAC.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the multifactor AI model built in this research?",{"text":117,"@type":113},"Longitudinal radiomics features are extracted from pre- and post-NAC MRI, significant features are selected using statistical and correlation methods, and a machine learning stacking model is trained to detect ALN metastasis.",{"name":119,"@type":110,"acceptedAnswer":120},"What improvements does AI-assisted surgery achieve compared with sentinel lymph node biopsy alone?",{"text":121,"@type":113},"Introducing AI-assisted surgery reduces false negative rates substantially across primary, external validation, and prospective cohorts, with further reductions when more than two sentinel lymph nodes are removed.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},381839,1790694407,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":66,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":41},4398046744996,"’Retrospective Cohort Study  \nMultifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study  \nTeng Zhu, MDa , Yu-Hong Huang, PhDa , Wei Li, MDb , Yi-Min Zhang, MDf, Ying-Yi Lin, MDa,d , Min-Yi Cheng, MDa , Zhi-Yong Wu, MDe , Guo-Lin Ye, MDb,* , Ying Lin, MD, PhDc,* , Kun Wang, MD, PhDa,*  \nBackground: The high false negative rate (FNR) associated with sentinel lymph node biopsy often leads to unnecessary axillary lymph node dissection following neoadjuvant chemotherapy (NAC) in breast cancer. The authors aimed to develop a multifactor artificial intelligence (AI) model to aid in axillary lymph node surgery.  \nMaterials and Methods: A total of 1038 patients were enrolled, comprising 234 patients in the primary cohort, 723 patients in three external validation cohorts, and 81 patients in the prospective cohort. For predicting axillary lymph node response to NAC, robust longitudinal radiomics features were extracted from pre-NAC and post-NAC magnetic resonance images. The U test, the least absolute shrinkage and selection operator, and the spearman analysis were used to select the most significant features. A machine learning stacking model was constructed to detect ALN metastasis after NAC. By integrating the significant predictors, we developed a multifactor AI-assisted surgery pipeline and compared its performance and false negative rate with that of sentinel lymph node biopsy alone. Results: The machine learning stacking model achieved excellent performance in detecting ALN metastasis, with an area under the curve (AUC) of 0 .958 in the primary cohort, 0 .881 in the external validation cohorts, and 0 .882 in the prospective cohort. Furthermore, the introduction of AI-assisted surgery reduced the FNRs from 14 .88 (18/121) to 4 . 13%(5/121) in the primary cohort, from 16 .55 (49/296) to 4.05%(12/296) in the external validation cohorts, and from 13 .64 (3/22) to 4 .55%(1/22) in the prospective cohort. Notably, when more than two SLNs were removed, the FNRs further decreased to 2.78%(2/72) in the primary cohort, 2.38%(4/168) in the external validation cohorts, and 0%(0/15) in the prospective cohort.  \nConclusion: Our study highlights the potential of AI-assisted surgery as a valuable tool for evaluating ALN response to NAC, leading to a reduction in unnecessary axillary lymph node dissection procedures.  \nKeywords axillary lymph node metastasis, breast cancer, longitudinal radiomics, machine learning, neoadjuvant chemotherapy, sentinel lymph node biopsy  \nIntroduction  \nBreast cancer is the most common cancer and the leading cause of cancer-related death in female worldwide. Neoadjuvant  \nchemotherapy (NAC) is increasingly utilized in patients with clinically positive axillary lymph nodes (ALN) breast cancer. Accurately identifying the ALN status is crucial for determining the optimal axillary surgical strategy following NAC. The  \naDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, bDepartment of Breast Cancer, The First People ’s Hospital ofFoshan, Foshan, cBreast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, dShantou University Medical College, Shantou, Guangdong, eDiagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital and fClinical Research Centre & Breast  \nDisease Diagnosis and Treatment Centre, Shantou Central Hospital, Shantou, People’s Republic of China Teng Zhu, Yu-Hong Huang, and Wei Li contributed equally to this work.  \nSponsorships or competing interests that may be relevant to content are disclosed at the end of this article.  \n*Corresponding author. Address: Department of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, Guangdong, People’s Republic of China. Tel: + ","cbCaibXawusCLZ2M","https://ap.wps.com/l/cbCaibXawusCLZ2M","pdf",1309503,12,"English","# Background\n# Materials and Methods\n## Patient cohorts and imaging features\n## Feature selection and model construction\n# Results\n# Conclusion","[{\"question\":\"What problem does the study address after neoadjuvant chemotherapy in breast cancer?\",\"answer\":\"The study targets the high false negative rate of sentinel lymph node biopsy, which can lead to unnecessary axillary lymph node dissection after NAC.\"},{\"question\":\"How is the multifactor AI model built in this research?\",\"answer\":\"Longitudinal radiomics features are extracted from pre- and post-NAC MRI, significant features are selected using statistical and correlation methods, and a machine learning stacking model is trained to detect ALN metastasis.\"},{\"question\":\"What improvements does AI-assisted surgery achieve compared with sentinel lymph node biopsy alone?\",\"answer\":\"Introducing AI-assisted surgery reduces false negative rates substantially across primary, external validation, and prospective cohorts, with further reductions when more than two sentinel lymph nodes are removed.\"}]","Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy - multicenter retrospective cohort study | PDF",1790247416]