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Models used multivariate analysis and gradient boosting to predict brain, bone, and visceral sites, achieving AUCs around 0.74–0.75. Bone metastasis was most common (57%), linked to estrogen receptor positivity, while ER-negativity increased brain risk; non-anthracycline regimens related to lower bone risk and higher visceral risk. Results support ML-guided, patient-specific surveillance after validation.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/predicting-patterns-of-distant-metastasis-in-breast-cancer-patients-following-local-regional-therapy-using-machine-learning-290959/290959/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/predicting-patterns-of-distant-metastasis-in-breast-cancer-patients-following-local-regional-therapy-using-machine-learning-290959/290959.png","ImageObject",300,407,{"name":42,"@type":43},"Quinn","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-02","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What was the goal of this study on breast cancer metastasis?","Question",{"text":63,"@type":64},"To predict the risk and first site of distant metastasis in breast cancer patients after local-regional therapy using statistical and machine learning models.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How was distant metastasis characterized in the study?",{"text":68,"@type":64},"Outcome variables included the first metastatic site (brain, bone, or visceral) and the time interval, measured in months, until distant metastasis developed.",{"name":70,"@type":61,"acceptedAnswer":71},"Which factors were associated with bone and brain metastasis risk?",{"text":72,"@type":64},"Bone metastasis odds increased with estrogen receptor positivity, while brain metastasis was associated with estrogen receptor negativity; chemotherapy regimen effects were also observed.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290959,1789643041,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":140,"language":141,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":12,"update_tm":80,"read_time":145,"preview_page_count":4},962075114765,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Article  \nPredicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning  \nAudrey Shiner 1,2,3, Alex Kiss 4, Khadijeh Saednia 1,5, Katarzyna J. Jerzak 6, Sonal Gandhi 6, Fang-I Lu 7, Urban Emmenegger 6, Lauren Fleshner 1,2,3, Andrew Lagree 2, Marie Angeli Alera 2, Mateusz Bielecki 1,2, Ethan Law 2, Brianna Law 2, Dylan Kam 2, Jonathan Klein 8, Christopher J. Pinard 2, Alex Shenfield 9, Ali Sadeghi-Naini 1,5 and William T. Tran 1,2,3,10, *  \nCitation: Shiner, A.; Kiss, A.; Saednia, K.; Jerzak, K.J.; Gandhi, S.; Lu, F.-I.; Emmenegger, U.; Fleshner, L.; Lagree, A.; Alera, M.A.; et al. Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning. Genes 2023, 14, 1768. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)genes14091768  \nAcademic Editors: Garrett M. Dancik and Spiros Vlahopoulos  \nReceived: 1 August 2023  \nRevised: 5 September 2023  \nAccepted: 6 September 2023  \nPublished: 7 September 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada; [audrey.shiner@sri.utoronto.ca](audrey.shiner@sri.utoronto.ca) (A.S.)  \n2 Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada  \n3 Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A8, Canada  \n4 Institute of Clinical Evaluative Sciences, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada  \n5 Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada  \n6 Division of Medical Oncology, Department of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada  \n7 Department of Anatomic Pathology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada  \n8 Department of Radiation Oncology, Albert Einstein College of Medicine, New York, NY 10461, USA  \n9 Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK  \n10 Department of Radiation Oncology, University of Toronto, Toronto, ON M5S 1A8, Canada  \n* [Correspondence: william.tran@sunnybrook.ca](Correspondence: william.tran@sunnybrook.ca); Tel.: +1-416-480-6100 (ext. 3746)  \nAbstract: Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML pred","cbCailXNNLN748z5","https://ap.wps.com/l/cbCailXNNLN748z5","pdf",1978886,15,"English","# Abstract\n# Introduction","[{\"question\":\"What was the goal of this study on breast cancer metastasis?\",\"answer\":\"To predict the risk and first site of distant metastasis in breast cancer patients after local-regional therapy using statistical and machine learning models.\"},{\"question\":\"How was distant metastasis characterized in the study?\",\"answer\":\"Outcome variables included the first metastatic site (brain, bone, or visceral) and the time interval, measured in months, until distant metastasis developed.\"},{\"question\":\"Which factors were associated with bone and brain metastasis risk?\",\"answer\":\"Bone metastasis odds increased with estrogen receptor positivity, while brain metastasis was associated with estrogen receptor negativity; chemotherapy regimen effects were also observed.\"}]","Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning | PDF",38]