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Hypoestrogenism is a key risk factor, leading to two major categories: type 1, estrogen-dependent, and type 2, estrogen-independent. Treatment relies on surgery, chemotherapy, and radiation, while diagnostic accuracy and prognostic prediction strongly guide management decisions. Artificial intelligence supports these steps through imaging and clinical-course prediction, using MRI-based qualitative data and improving visual feature extraction when classifying patients. This review summarizes the current state of AI applications in endometrial cancer and recent advances that help clinicians improve diagnosis and prognosis.",{"@graph":69,"@context":123},[70,84,106],{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/impact-of-artificial-intelligence-on-the-diagnosis-treatment-and-prognosis-of-endometrial-cancer-comprehensive-review/327100/",{"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/impact-of-artificial-intelligence-on-the-diagnosis-treatment-and-prognosis-of-endometrial-cancer-comprehensive-review/327100.png","ImageObject",300,407,{"name":92,"@type":93},"Finn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does AI support endometrial cancer diagnosis and staging?","Question",{"text":113,"@type":114},"AI applications include diagnosing and staging through analysis of histological slides, improving clinical assessment from imaging and pathology data.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What types of AI-based imaging and prediction are discussed for endometrial cancer?",{"text":118,"@type":114},"Diagnostic imaging and clinical-course prediction are described as two core pillars. MRI is noted for detecting preoperative endometrial cancer, and AI improves the effectiveness of visual feature extraction when classifying patients.",{"name":120,"@type":111,"acceptedAnswer":121},"What challenges limit the implementation of AI in healthcare for this setting?",{"text":122,"@type":114},"Key challenges include data security, algorithm bias, and ethical considerations such as privacy and transparency.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},327100,1790363022,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"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,"preview_page_count":4},34359740700684,"https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487","’Review  \nImpact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer  \nSamia Rauf Butt, MBBSa , Amna Soulat, MBBSb , Priyanka Mohan Lal, MBBSb , Hajar Fakhore ,  \nSiddharth Kumar Patel, MD, MPHf, Mashal Binte Ali, MBBSc , Suneel Arwani, MCPSh , Anmol Mohan, MBBSd , Koushik Majumder, MBBSi,* , Vikash Kumar, MDg , Usha Tejwaney, PharmDj , Sarwan Kumar, MDk  \nAbstract   \nEndometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.  \nKeywords: artificial intelligence, cervical intraepithelial neoplasia, CIN, EC, endometrial cancer  \nIntroduction  \nEndometrial cancer (EC) is one of the most typical cancers of female origin, and it has a survival rate of 83% after 5 years of being diagnosed and has a high prevalence in developed countries like Europe and North America[1,2] which can be attributed to sedentary lifestyle and genetics. EC has been classified into two types depending upon their reliance on oestrogen for decades[1,2] . Type 1 EC endometrial cancer is considered oestrogen-dependent, while type 2 is oestrogen independent[3] . Both of these types can be belligerent, with a bad prognosis[1] . Other classifications include histological ones such as epithelial carcinomas, mixed and epithelial, mesenchymal tumours, endometrial stromal and  \naUniversity College of Medicine and Dentistry, Lahore, bZiauddin Medical University, cDow University of Health Sciences, dKarachi Medical and Dental College, Karachi, Pakistan, eAsselin Hedelin Hospital, Yvetot, France, fUniversity of Albany, Albany, gThe Brooklyn Hospital Center, Brooklyn, NY, hMedway Maritime Hospital, Kent, UK, iChittagong Medical College, Chittagong, Bangladesh, jValley health system, Ridgewood, NJ and kWayne State University, Detroit, MI  \nSponsorships or competing interests that maybe relevant to content are disclosed atthe end of this article.  \n*Corresponding author. Address: Department of Medicine, Chittagong Medical College, Chittagong, Bangladesh. Tel.: +[880 178 398 3350. E-mail: majumder382@gmail.com](880 178 398 3350. E-mail: majumder382@gmail.com)[ ](880 178 398 3350. E-mail: majumder382@gmail.com)(K. Majumder).  \nCopyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution-ShareAlike License 4.0, which allows others to remix, tweak, and build upon the work, even for commercial purposes, as long as the author is credited and the new creations are licensed under the identical terms.  \nAnnals of Medicine & Surgery (2024) 86:1531–1539  \nReceived 26 June 2023; Accepted 8 January 2024  \nPublished online 17 January 2024  \n[http://dx.doi.org/10.1097/MS9.000000","cbCainRQEgTT327Y","https://ap.wps.com/l/cbCainRQEgTT327Y","pdf",369176,"English","# Highlights\n## AI applications in diagnosis, staging, surgery assistance, and prognosis prediction\n## Implementation challenges: data security, algorithm bias, ethics\n## Need for validation and responsible use\n# Abstract\n## Endometrial cancer background and risk classification\n## Role of AI in imaging and prognostic prediction\n# Introduction\n## Survival rate and prevalence context\n## Type 1 vs type 2 endometrial cancer and other classifications\n## Clinical decision factors and diagnostic difficulties\n## Treatment interventions and need for improved diagnostic accuracy","[{\"question\":\"How does AI support endometrial cancer diagnosis and staging?\",\"answer\":\"AI applications include diagnosing and staging through analysis of histological slides, improving clinical assessment from imaging and pathology data.\"},{\"question\":\"What types of AI-based imaging and prediction are discussed for endometrial cancer?\",\"answer\":\"Diagnostic imaging and clinical-course prediction are described as two core pillars. MRI is noted for detecting preoperative endometrial cancer, and AI improves the effectiveness of visual feature extraction when classifying patients.\"},{\"question\":\"What challenges limit the implementation of AI in healthcare for this setting?\",\"answer\":\"Key challenges include data security, algorithm bias, and ethical considerations such as privacy and transparency.\"}]","Impact of Artificial Intelligence on the Diagnosis, Treatment and Prognosis of Endometrial Cancer - Comprehensive Review | PDF",1789976306,23]