[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-333304-105":59,"doc-detail-333304-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","a-multi-cancer-early-detection-blood-test-using-machine-learning-detects-early-stage-cancers-lacking-uspstf-recommended-screening","A multi-cancer early detection blood test using machine learning detects early-stage cancers lacking USPSTF-recommended screening","","US Preventive Services Task Force guidelines recommend single-cancer screening for select cancers, yet most cancer deaths in adults 50–79 are driven by cancers without recommended screening. A multi-cancer early detection (MCED) blood test (Galleri) uses a methylation assay of circulating cell-free DNA with a machine-learning algorithm to identify shared cancer signals and predict likely cancer signal origin among 21 options. Real-world cases describe detected signals in three individuals later diagnosed with ovarian, kidney, and head/neck cancers lacking USPSTF screening.",{"@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":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/a-multi-cancer-early-detection-blood-test-using-machine-learning-detects-early-stage-cancers-lacking-uspstf-recommended-screening/333304/",{"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/a-multi-cancer-early-detection-blood-test-using-machine-learning-detects-early-stage-cancers-lacking-uspstf-recommended-screening/333304.png","ImageObject",300,407,{"name":92,"@type":93},"Olivia Brown","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",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},"Why is single-cancer screening insufficient for many cancer deaths?","Question",{"text":112,"@type":113},"USPSTF-recommended screening covers only select cancers, while a majority of cancer deaths among adults 50–79 are caused by cancers without recommended screening. This creates a large unmet detection gap.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the MCED blood test work to detect multiple cancer types?",{"text":117,"@type":113},"The test uses a single blood draw followed by a targeted methylation assay of circulating cell-free DNA. A machine-learning algorithm then detects a shared cancer signal and predicts likely cancer signal origin across predefined options.",{"name":119,"@type":110,"acceptedAnswer":120},"What real-world outcomes are described in this case report?",{"text":121,"@type":113},"Three individuals had cancer signals detected by the MCED test and were subsequently diagnosed with cancers of the ovary, kidney, and head/neck that lack USPSTF-recommended screening. The cases support the potential for detecting early-stage, potentially curable cancers.","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},333304,1790183912,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"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":29,"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":144,"preview_page_count":4},16904993612988,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","npj | precision oncology Case report  \n\n| Published in partnership with The Hormel Institute, University of Minnesota |  | |\n| --- | --- | --- |\n| [https://doi.org/10.1038/s41698-024-00568-z](https://doi.org/10.1038/s41698-024-00568-z) |  |  |\n| A multi-cancer early detection blood test using machine learning detects earlystage cancers lacking USPSTFrecommended screening\u003Cbr> Check for updates |  |  |\n| Janet Vittone1, David Gill2, Alex Goldsmith3, Eric A. Klein4 & Jordan J. Karlitz 4  |  |  |\n| US Preventive Services Task Force (USPSTF) guidelines recommend single-cancer screening for select cancers (e.g., breast, cervical, colorectal, lung). Advances in genome sequencing and machine learning have facilitated the development of blood-based multi-cancer early detection (MCED) tests intended to complement single-cancer screening. MCED tests can interrogate circulating cell-free DNA to detect a shared cancer signal across multiple tumor types. We report real-world experience with an MCED test that detected cancer signals in three individuals subsequently diagnosed with cancersofthe ovary, kidney, and head/neckthat lack USPSTF-recommended screening. These cases illustrate the potential of MCED tests to detect early-stage cancers amenable to cure. |  |  |\n| Only select cancers (e.g., breast, cervical, colorectal, and lung) have United States Preventive Services Task Force (USPSTF)-recommended screening guidelines1. Although the use of these single-cancer screening tests has reduced cancer-related mortality for these malignancies, ~70% of deaths due to cancer in the U.S. among those 50–79 years of age are caused by cancers without USPSTF-recommended screening. Partly as a result, US cancer mortality exceeds 600,000 cases/year2.\u003Cbr>A recently developed multi-cancer early detection (MCED) test (Galleri®, GRAIL, LLC, Menlo Park, CA), intended to complement USPSTF-recommended screening is clinically available as a laboratory-developed test (LDT) performed in GRAIL’s clinical laboratory with accreditations from the College of American Pathologists (CAP) and certification under Clinical Laboratory Improvement Amendments (CLIA)3. This MCED test comprises a single blood draw followed by a targeted methylation assay of cellfree DNA (cfDNA) and a machine learning-based algorithm to detect a shared cancer signal across multiple cancer types4. The test capitalizes on a preclinical detection window created by tumor shedding of cfDNA (Fig. 1) with results provided within 10 working days of sample receipt. The test report indicates whether a cancer signal was detected, and if so, provides up to two predictions, ranked by signal strength, for the likely organ or organ system of cancer involvement (i.e., cancer signal origin or CSO) out of 21 pre-specified options, allowing for targeted diagnostic evaluations5. The MCED platform | supporting the test has been extensively studied with >385,000 participants having completed or currently enrolled/enrolling in clinical studies.\u003Cbr>This MCED test was developed and validated in the Circulating CellFree Genome Atlas study (CCGA; NCT02889978, N = 15,254)4,6,7, a casecontrol study in which it detected a cancer signal across >50 cancer types with a low false positive rate (specificity of 99.5%) and predicted the CSO with approximately 90% accuracy7. In CCGA substudy 1, which focused on the discovery and selection of the highest performing assays, whole genome bisulfite sequencing outperformed targeted and whole genome sequencing approaches and a methylation-based assay was selected for further development in the subsequent substudies. The overall sensitivity for cancer detection was 51.5%, which varied by cancer type and stage7,8. Stage I-III sensitivity in 12 pre-specified cancers that account for nearly two-thirds of annual cancer deaths in the US was 67.6%(95% confidence interval: 64.4% to70.6%)7. Cancers included in this subgroupwere pancreatic, ovarian, head and neck, and multiple other aggressive cancer types","cbCaisudHAvaErzv","https://ap.wps.com/l/cbCaisudHAvaErzv","pdf",1699951,"English","# Background and rationale\n## Single-cancer screening gaps\n## MCED test design and workflow\n# Study and validation evidence\n## Circulating Cell-Free Genome Atlas (CCGA)\n## PATHFINDER implementation and feasibility\n# Real-world case report findings\n## Detected cancer signals without USPSTF screening\n## Early-stage diagnostic resolution","[{\"question\":\"Why is single-cancer screening insufficient for many cancer deaths?\",\"answer\":\"USPSTF-recommended screening covers only select cancers, while a majority of cancer deaths among adults 50–79 are caused by cancers without recommended screening. This creates a large unmet detection gap.\"},{\"question\":\"How does the MCED blood test work to detect multiple cancer types?\",\"answer\":\"The test uses a single blood draw followed by a targeted methylation assay of circulating cell-free DNA. A machine-learning algorithm then detects a shared cancer signal and predicts likely cancer signal origin across predefined options.\"},{\"question\":\"What real-world outcomes are described in this case report?\",\"answer\":\"Three individuals had cancer signals detected by the MCED test and were subsequently diagnosed with cancers of the ovary, kidney, and head/neck that lack USPSTF-recommended screening. The cases support the potential for detecting early-stage, potentially curable cancers.\"}]","A multi-cancer early detection blood test using machine learning detects early-stage cancers lacking USPSTF-recommended screening | PDF",1790001844,15]