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The text explains how AI-enabled genomic and variant interpretation tools support more sensitive, faster workflows, and how machine learning can classify BRCA1/2 variants using ACMG-AMP criteria with high reported accuracy. It further highlights tumor genomics models that infer homologous recombination deficiency from routine pathology, while emphasizing implementation requirements including standardized reporting, ethics, clinician education, and global collaboration.",{"@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/artificial-intelligence-in-cancer-genomics-refining-diagnosis-of-hereditary-breast-and-ovarian-cancer-syndromes/439935/",{"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/artificial-intelligence-in-cancer-genomics-refining-diagnosis-of-hereditary-breast-and-ovarian-cancer-syndromes/439935.png","ImageObject",300,407,{"name":92,"@type":93},"Mimi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How do BRCA1/2 mutations influence HBOC risk in the article?","Question",{"text":112,"@type":113},"BRCA1/2 germline mutations are described as primary drivers of HBOC syndromes, substantially increasing lifetime cancer risk. The text notes mutation carriers face a markedly elevated lifetime risk compared with the general population.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What role can AI play in early screening and patient pre-screening?",{"text":117,"@type":113},"The article describes AI-powered tools such as chatbots used to pre-screen patients based on family history, aiming to improve engagement and extend screening support beyond traditional pathways. It also notes automated prescreening tools being deployed in some countries for low-resource settings.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the article describe AI improving variant interpretation and tumor genomics?",{"text":121,"@type":113},"For variant interpretation, the text highlights machine learning models that apply ACMG-AMP criteria to classify BRCA1/2 variants and improve consistency versus generic approaches. For tumor genomics, it presents a CNN example that predicts homologous recombination deficiency from H&E slides and supports identification of candidates for targeted therapies.","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},439935,1791003194,{"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":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"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":24,"preview_page_count":4},2336477974920,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Letter to the Editor  \nArtificial intelligence in cancer genomics: refiningdiagnosis of hereditary breast and ovarian cancersyndromes  \nMuhammad Khizar, MDa , Muhammad Zaib, MDa , Meerab Babar, MDb , Hasiba Karimi, MDc ,Hasibullah Aminpoor, MDd ,*  \n# Abstract\n\nThe integration of artificial intelligence (AI) into soft tissue sarcoma (STS) diagnostics represents a significant leap toward precisiononcology. AI-driven genomic tools, especially in the detection of fusion genes, enhance diagnostic sensitivity, speed, andaccuracy, facilitating improved patient stratification and personalized therapy. The convergence of AI with multi-omic data –including transcriptomic, epigenomic, and imaging datasets – offers a comprehensive view of sarcoma biology and supportsearlier detection and optimized treatment selection. However, effective implementation requires international collaboration,standardized reporting frameworks, and continuous clinician education. AI holds transformative potential to advance the precision,efficiency, and clinical impact of STS management.  \nKeywords: artificial intelligence, genomics, precision oncology, soft tissue sarcoma  \nDear Editor,  \nHereditary breast and ovarian cancer (HBOC) syndromes,primarily due to germline BRCA1/2 mutations, significantlyelevate lifetime cancer risk. BRCA1/2 mutations account for10–20% of all ovarian cancers, with mutation carriers facingup to a 44% lifetime risk[1] . Early detection is critical, enablingpreventive strategies and surveillance. In high-income countrieslike the UK, universal BReast CAncer (BRCA) testing for ovar -ian cancer is now considered cost-effective[1] . However, accessremains limited in low- and middle-income countries. In India,population-wide BRCA screening could prevent nearly 97 000ovarian cancer cases and 78 000 deaths[1], underscoring theglobal need for scalable, cost-effective HBOC diagnostics.  \nArtificial intelligence (AI) offers promising solutions across theHBOC care continuum. AI-powered chatbots, for instance, arebeing used to pre-screen patients based on family history. A USstudy using the chatbot “Gia”found that trust, cost, and physiciancommunication influenced patient engagement[2] . Nonetheless,  \naFaculty of Medicine, Georgian American University, Manifest Medical ResearchCo., Tbilisi, Georgia, bFaculty of Medicine, Rawalpindi Medical University,Rawalpindi, Pakistan, cFaculty of Medicine, Bezmialem Vakif University, Istanbul,Turkey and dFaculty of Medicine, Kabul University ofMedical Sciences “Abu Ali IbnSina”, Kabul, Afghanistan  \nSponsorships or competing interests that maybe relevant to content are disclosedatthe end of this article.  \n*Corresponding author. Address: Faculty of Medicine, Kabul University of MedicalSciences “Abu Ali Ibn Sina”, Ata Turk Avenue, Jamal Mena, 3rd District, Kabul1001, Afghanistan. Tel.: +93 730097097. E-mail: hasibaminpoor786@gmail.com(H. Aminpoor).  \nCopyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. This isanopen access article distributed under the Creative Commons Attribution License 4.0(CCBY), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly cited.  \nAnnals of Medicine & Surgery (2026) 88:999–1000Received 10 October 2025; Accepted 10 October 2025Published online 18 November 2025http://dx.doi.org/10.1097/MS9.0000000000004340  \nsuch tools can help extend genetic screening to underserved andrural populations. Similar automated prescreening tools are beingdeployed in the UK and Australia and hold promise for low -resource settings.  \nAI also enhances variant interpretation. Next-generationsequen -cing often detects rare variants of uncertain significance, complicat -ing clinical decision making. Machine learning (ML) tools such asMARGINAL apply ACMG-AMP criteria to classify BRCA1/2 var -iants with up to 98% accuracy[3] . BRCA-specific ML classifiersalso outperform generic models in pathogenicity prediction[4],improving co","cbCaiuJVmxmKqCh2","https://ap.wps.com/l/cbCaiuJVmxmKqCh2","pdf",175912,"English","# Abstract\n# Ethical approval\n# Consent\n# Sources of funding\n# Author contributions\n# Conflicts of interest disclosure","[{\"question\":\"How do BRCA1/2 mutations influence HBOC risk in the article?\",\"answer\":\"BRCA1/2 germline mutations are described as primary drivers of HBOC syndromes, substantially increasing lifetime cancer risk. The text notes mutation carriers face a markedly elevated lifetime risk compared with the general population.\"},{\"question\":\"What role can AI play in early screening and patient pre-screening?\",\"answer\":\"The article describes AI-powered tools such as chatbots used to pre-screen patients based on family history, aiming to improve engagement and extend screening support beyond traditional pathways. It also notes automated prescreening tools being deployed in some countries for low-resource settings.\"},{\"question\":\"How does the article describe AI improving variant interpretation and tumor genomics?\",\"answer\":\"For variant interpretation, the text highlights machine learning models that apply ACMG-AMP criteria to classify BRCA1/2 variants and improve consistency versus generic approaches. For tumor genomics, it presents a CNN example that predicts homologous recombination deficiency from H\\u0026E slides and supports identification of candidates for targeted therapies.\"}]","Artificial intelligence in cancer genomics - refining diagnosis of hereditary breast and ovarian cancer syndromes | PDF",1790690555]