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Trained and validated on multiple independent datasets, it demonstrated strong predictive discrimination at 1 year and meaningful concordance over 6 years, supporting personalized screening workflows.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/sybil-a-validated-deep-learning-model-to-predict-future-lung-cancer-risk-from-a-single-low-dose-chest-computed-tomography/378099/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/sybil-a-validated-deep-learning-model-to-predict-future-lung-cancer-risk-from-a-single-low-dose-chest-computed-tomography/378099.png","ImageObject",300,407,{"name":42,"@type":43},"Angel","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-25","2026-09-24",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",7,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why was Sybil developed for lung cancer screening?","Question",{"text":63,"@type":64},"Most eligible people do not receive screening, and existing risk tools often need demographic, clinical factors, and radiologic annotations. Sybil was designed to provide personalized future cancer risk assessment using only a single LDCT scan.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What inputs does Sybil require to make predictions?",{"text":68,"@type":64},"Sybil requires only one low-dose chest CT (LDCT) and does not require additional demographic or clinical data, radiologist annotations, or other structured inputs.",{"name":70,"@type":61,"acceptedAnswer":71},"How was Sybil validated and what were the main performance outcomes?",{"text":72,"@type":64},"Sybil was validated on three independent datasets, including heldout NLST data and external datasets from Massachusetts General Hospital and Chang Gung Memorial Hospital. It achieved strong predictive performance at 1 year and reported concordance over multiple years across these sets.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},378099,1790617688,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":55,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145},687207412472,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","original  \nSybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography  \nreports abstract  \nASSOCIATED CONTENT  \nSee accompanying editorial on page 2141  \nAppendix  \nData Supplement  \nAuthor affiliationsand support information (if applicable) appear at the end of this article.  \nAccepted on November 29, 2022 and published at  \n[ascopubs.org/journal/](ascopubs.org/journal/)[ ](ascopubs.org/journal/)jco on January 12, 2023: DOI [https://doi](https://doi). org/10.1200/JCO.22 . 01345  \nPeter G. Mikhael, BSc1,2; Jeremy Wohlwend, ME1,2; Adam Yala, PhD1,2; Ludvig Karstens, MSc1,2; Justin Xiang, ME1,2;  \nAngelo K. Takigami, MD3,4; Patrick P. Bourgouin, MD3,4; PuiYee Chan, PhD5; Sofiane Mrah, MSc4; Wael Amayri, BSc4;  \nYu-Hsiang Juan, MD6,7; Cheng-Ta Yang, MD6,8; Yung-Liang Wan, MD6,7; Gigin Lin, MD, PhD6,7; Lecia V. Sequist, MD, MPH3,5; Florian J. Fintelmann, MD3,4; and Regina Barzilay, PhD1,2  \nPURPOSE Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.  \nMETHODS We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST) . Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers) .  \nRESULTS Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0 .92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0 . 75 (95% CI, 0 . 72 to 0 . 78), 0 .81 (95% CI, 0 . 77 to 0 .85), and 0 .80 (95% CI, 0 . 75 to 0 .86) for NLST, MGH, and CGMH, respectively.  \nCONCLUSION Sybil can accurately predict an individual’s future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil’s clinical applications. Our model and annotations are publicly available.  \nJ Clin Oncol 41:2191-2200 . © 2023 by American Society of Clinical Oncology  \nCreative Commons Attribution Non-Commercial No Derivatives 4.0 License   \nINTRODUCTION  \nTwo large randomized controlled trials have established the efficacy of lung cancer screening (LCS) using lowdose computed tomography (LDCT) in cigarette smokers, with 20% and 24% decreases in lung cancer mortality in the National Lung Screening Trial (NLST) and the NELSON trial, respectively.1 Hence, the US Preventive Services Task Force recommends annual LDCTs for those age 50 years and older with a 20 packyear history of smoking.2 There are currently major shortcomings in achieving appropriate LCS. For instance, in the United States, a dismal , 10% of the eligible population is being screened.3-5 Evidence also suggests those being screened are not being optimally routed to follow-up or kept engaged in long-term screening.6-8 In parallel, lung cancer diagnoses among never-and lighter-smokers are rapidly rising,9 , 10 suggesting that if we continue to focus research about LCS only on heavier smokers, a gap will persist between the screen population and the disease population.  \nOne strategy that could help address these disparate LCS obstacles is to improve the efficiency and benefits of LCS by individualizing assessment of future lung cancer risk.","cbCail09ahhbuUdN","https://ap.wps.com/l/cbCail09ahhbuUdN","pdf",817739,17,"English","# Purpose\n# Methods\n## Data sources and model requirements\n# Results\n# Conclusion\n# Context","[{\"question\":\"Why was Sybil developed for lung cancer screening?\",\"answer\":\"Most eligible people do not receive screening, and existing risk tools often need demographic, clinical factors, and radiologic annotations. Sybil was designed to provide personalized future cancer risk assessment using only a single LDCT scan.\"},{\"question\":\"What inputs does Sybil require to make predictions?\",\"answer\":\"Sybil requires only one low-dose chest CT (LDCT) and does not require additional demographic or clinical data, radiologist annotations, or other structured inputs.\"},{\"question\":\"How was Sybil validated and what were the main performance outcomes?\",\"answer\":\"Sybil was validated on three independent datasets, including heldout NLST data and external datasets from Massachusetts General Hospital and Chang Gung Memorial Hospital. It achieved strong predictive performance at 1 year and reported concordance over multiple years across these sets.\"}]","Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography | PDF",1790228492,43]