[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-332009-105":59,"doc-detail-332009-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","machine-learning-and-new-insights-for-breast-cancer-diagnosis","Machine learning and new insights for breast cancer diagnosis","","Breast cancer (BC) is the most prominent cancer in females worldwide, and current detection relies on X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography, and thermographic techniques. Increasingly, machine learning supports efficient diagnosis by turning imaging features and mathematical analyses into models that distinguish benign and malignant lesions. Radiomics is widely used in research and clinics, while deep learning and artificial neural networks enable precise image stratification and tumor diagnosis. This review consolidates prior medical image studies for BC detection and intervention using radiomics, ML, and DL, aiming to guide scientists in research and clinical use.",{"@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/machine-learning-and-new-insights-for-breast-cancer-diagnosis/332009/",{"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/machine-learning-and-new-insights-for-breast-cancer-diagnosis/332009.png","ImageObject",300,407,{"name":92,"@type":93},"Ava Thompson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which imaging modalities are currently used for breast cancer detection?","Question",{"text":112,"@type":113},"Current methods include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography, and breast thermographic techniques.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do machine learning models contribute to breast cancer diagnosis?",{"text":117,"@type":113},"Imaging features and mathematical analyses are used to build ML models that stratify, differentiate, and detect benign versus malignant breast lesions.",{"name":119,"@type":110,"acceptedAnswer":120},"What role do radiomics and deep learning play in this review?",{"text":121,"@type":113},"The review summarizes investigations applying radiomics with machine learning and deep learning for breast cancer detection and intervention, highlighting how deep learning directly processes unstructured data and supports clinical image stratification and tumor diagnosis.","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},332009,1790014951,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1649267921044,"https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345","Machine learning and new insights for breast cancer diagnosis  \nYa Guo 1,*, Heng Zhang2,*, Leilei Yuan 1, Weidong Chen 1, Haibo Zhao 1,  \nQing-Qing Yu3,† and Wenjie Shi4,†  \nJournal of International Medical Research 2024, Vol. 52(4) 1–29! The Author(s) 2024 Article reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/03000605241237867](DOI: 10.1177/03000605241237867)[ ](DOI: 10.1177/03000605241237867)[journals.sagepub.com/home/imr](journals.sagepub.com/home/imr)  \nAbstract  \nBreast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.  \n1 Department of Oncology, Jining No. 1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China  \n2Department of Laboratory Medicine, Shandong Daizhuang Hospital, Jining, Shandong Province, China 3Phase I Clinical Research Centre, Jining No. 1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China  \n4Molecular and Experimental Surgery, University Clinic for General-, Visceral-, Vascular-and Trans-Plantation Surgery, Medical Faculty University Hospital Magdeburg, Otto-von Guericke University, Magdeburg, Germany  \n*These authors contributed equally to this work.†These authors contributed equally to this work. Corresponding author:  \nWenjie Shi, Molecular and Experimental Surgery, University Clinic for General-, Visceral-, Vascular-and Trans-Plantation Surgery, Medical Faculty University Hospital Magdeburg, Otto-von Guericke University, 39120 Magdeburg, Germany.  \nEmail: wenjie.shi@uni-oldenburg.de  \nCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative  \nCommons Attribution-NonCommercial 4.0 License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ([https://us.sagepub.com/en-us/nam/open-access-at-sage](https://us.sagepub.com/en-us/nam/open-access-at-sage)).  \nKeywords  \nBreast cancer, machine learning, radiomics, artificial intelligence, deep learning  \nDate received: 21 August 2023; accepted: 21 February 2024  \nIntroduction  \nBreast cancer (BC) is the most prominent malignant tumour among females worldwide and it accounts of almost 10.4% of all cancers.1 It is characterized by an aberrant, disorderly, and invasive proliferation of breast cells. Moreover, BC cells can readily escape to the circulatory or lymphatic system, where they generate new tumoursand invade distant vital organs.2 At present, BC is the largest contribu","cbCaijzSZw3GrnFw","https://ap.wps.com/l/cbCaijzSZw3GrnFw","pdf",595764,29,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Which imaging modalities are currently used for breast cancer detection?\",\"answer\":\"Current methods include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography, and breast thermographic techniques.\"},{\"question\":\"How do machine learning models contribute to breast cancer diagnosis?\",\"answer\":\"Imaging features and mathematical analyses are used to build ML models that stratify, differentiate, and detect benign versus malignant breast lesions.\"},{\"question\":\"What role do radiomics and deep learning play in this review?\",\"answer\":\"The review summarizes investigations applying radiomics with machine learning and deep learning for breast cancer detection and intervention, highlighting how deep learning directly processes unstructured data and supports clinical image stratification and tumor diagnosis.\"}]","Machine learning and new insights for breast cancer diagnosis | PDF",1789996290,73]