{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Weiss J"],"funding":["European Research Council","European Union-European Research Council","NCI NIH HHS","NIH HHS","Deutsche Forschungsgemeinschaft (DFG, German Research Foundation"],"pagination":["276-288"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11846076"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["297(3)"],"pubmed_abstract":["<h4>Background</h4>Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.<h4>Purpose</h4>To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).<h4>Material and methods</h4>We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.4 ± 5.0 years; 41.2% female) at baseline and on the 1-year follow-up scan if no cancer was detected. SLD was defined as hepatic fat fraction (HFF) ≥5% derived from Hounsfield unit measures of the segmented liver. Participants with SLD were categorized as lean (body mass index [B"],"journal":["Journal of internal medicine"],"pubmed_title":["Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals."],"pmcid":["PMC11846076"],"funding_grant_id":["866504","NIH-USA U01CA190234","U24 CA194354","U01 CA190234","502050303","NIH (NCI) 5U01CA209414","NIH-USA U24CA194354","U01 CA209414","NIH-USA R35CA22052","NIH-USA U01CA209414"],"pubmed_authors":["Weiss J","Fedorov A","Johnson J","Mak RH","Thiriveedhi V","Lu MT","Aerts HJWL","Bernatz S"],"additional_accession":[]},"is_claimable":false,"name":"Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals.","description":"<h4>Background</h4>Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.<h4>Purpose</h4>To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).<h4>Material and methods</h4>We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.4 ± 5.0 years; 41.2% female) at baseline and on the 1-year follow-up scan if no cancer was detected. SLD was defined as hepatic fat fraction (HFF) ≥5% derived from Hounsfield unit measures of the segmented liver. Participants with SLD were categorized as lean (body mass index [B","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2026-06-03T04:50:38.512Z","creation":"2025-04-04T00:16:18.085Z"},"accession":"S-EPMC11846076","cross_references":{"pubmed":["39868889"],"doi":["10.1111/joim.20053"]}}