[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122709-en":3,"doc-seo-122709-105":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},122709,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",7,"Healthcare","Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning","Up to 30% of breast cancer patients develop distant metastases, for which curative options remain limited. This study developed statistical and machine learning models to estimate the risk of site-specific distant metastasis after local-regional therapy. The retrospective cohort included 175 invasive breast cancer patients who later developed distant metastases, using clinicopathological variables to model both the first metastatic site and the time to metastasis. Gradient boosting models predicted brain, bone, and visceral sites with AUCs around 0.73–0.75. Results linked estrogen receptor positivity with higher odds of bone metastasis, with HER2 positivity and non-anthracycline chemotherapy associated with reduced bone risk, while non-anthracycline chemotherapy alone predicted visceral metastasis.","Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning  \nSHINER, Audrey, KISS, Alex, SAEDNIA, Khadijeh, JERZAK, Katarzyna J. , GANDHI, Sonal, LU, Fang-I, EMMENEGGER, Urban, FLESHNER, Lauren, LAGREE, Andrew, ALERA, Marie Angeli, BIELECKI, Mateusz, LAW, Ethan, LAW, Brianna, KAM, Dylan, KLEIN, Jonathan, PINARD, Christopher J. , SHENFIELD, Alex \u003C [http://orcid.org/0000-0002-2931-8077](http://orcid.org/0000-0002-2931-8077)>, SADEGHI-NAINI, Ali and TRAN, William T.  \nAvailable from Sheffield Hallam University Research Archive (SHURA) at: [https://shura.shu.ac.uk/32347/](https://shura.shu.ac.uk/32347/)  \nThis document is the Published Version [VoR]  \nCitation:  \nSHINER, Audrey, KISS, Alex, SAEDNIA, Khadijeh, JERZAK, Katarzyna J. , GANDHI, Sonal, LU, Fang-I, EMMENEGGER, Urban, FLESHNER, Lauren, LAGREE, Andrew, ALERA, Marie Angeli, BIELECKI, Mateusz, LAW, Ethan, LAW, Brianna, KAM, Dylan, KLEIN, Jonathan, PINARD, Christopher J. , SHENFIELD, Alex, SADEGHI-NAINI, Ali and TRAN, William T. (2023) . Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning. Genes, 14 (9): 1768. [Article]  \nCopyright and re-use policy  \nSee [http://shura.shu.ac.uk/information.html](http://shura.shu.ac.uk/information.html)  \nSheffield Hallam University Research Archive  \n[http://shura.shu.ac.uk](http://shura.shu.ac.uk)  \nArticle  \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@su","cbCainiutruF0J9A","https://ap.wps.com/l/cbCainiutruF0J9A","pdf",1956603,1,16,"English","en",105,"# Abstract\n# Introduction\n# Methods and Modeling\n## Statistical and Machine Learning Approach\n## Outcome Variables and Cohort\n# Results\n## Predictive Performance\n## Associated Clinical and Treatment Factors\n# Discussion and Implications","[{\"question\":\"What clinical outcomes did the models predict in distant metastasis for breast cancer patients?\",\"answer\":\"They predicted both the first site of distant metastasis (brain, bone, or visceral) and the time interval in months to developing distant metastasis.\"},{\"question\":\"How effective were the machine learning models at predicting metastatic site?\",\"answer\":\"The gradient boosting models showed AUCs of about 0.74 for brain, 0.75 for bone, and 0.73 for visceral metastasis.\"},{\"question\":\"Which factors were associated with higher or lower risk of specific metastatic sites?\",\"answer\":\"Estrogen receptor positivity increased odds of bone metastasis, HER2 positivity and non-anthracycline chemotherapy regimens decreased risk of bone metastasis, and brain metastasis was associated with ER negativity; non-anthracycline chemotherapy alone predicted visceral metastasis.\"}]","Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning | PDF",1785812449,40,{"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},"predicting-patterns-of-distant-metastasis-in-breast-cancer-patients-following-local-regional-therapy-using-machine-learning","",{"@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/predicting-patterns-of-distant-metastasis-in-breast-cancer-patients-following-local-regional-therapy-using-machine-learning/122709/",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 clinical outcomes did the models predict in distant metastasis for breast cancer patients?","Question",{"text":75,"@type":76},"They predicted both the first site of distant metastasis (brain, bone, or visceral) and the time interval in months to developing distant metastasis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How effective were the machine learning models at predicting metastatic site?",{"text":80,"@type":76},"The gradient boosting models showed AUCs of about 0.74 for brain, 0.75 for bone, and 0.73 for visceral metastasis.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were associated with higher or lower risk of specific metastatic sites?",{"text":84,"@type":76},"Estrogen receptor positivity increased odds of bone metastasis, HER2 positivity and non-anthracycline chemotherapy regimens decreased risk of bone metastasis, and brain metastasis was associated with ER negativity; 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