<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Weiss J</submitter><funding>European Research Council</funding><funding>European Union-European Research Council</funding><funding>NCI NIH HHS</funding><funding>NIH HHS</funding><funding>Deutsche Forschungsgemeinschaft (DFG, German Research Foundation</funding><pagination>276-288</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11846076</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>297(3)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.&lt;h4>Purpose&lt;/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).&lt;h4>Material and methods&lt;/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</pubmed_abstract><journal>Journal of internal medicine</journal><pubmed_title>Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals.</pubmed_title><pmcid>PMC11846076</pmcid><funding_grant_id>866504</funding_grant_id><funding_grant_id>NIH-USA U01CA190234</funding_grant_id><funding_grant_id>U24 CA194354</funding_grant_id><funding_grant_id>U01 CA190234</funding_grant_id><funding_grant_id>502050303</funding_grant_id><funding_grant_id>NIH (NCI) 5U01CA209414</funding_grant_id><funding_grant_id>NIH-USA U24CA194354</funding_grant_id><funding_grant_id>U01 CA209414</funding_grant_id><funding_grant_id>NIH-USA R35CA22052</funding_grant_id><funding_grant_id>NIH-USA U01CA209414</funding_grant_id><pubmed_authors>Weiss J</pubmed_authors><pubmed_authors>Fedorov A</pubmed_authors><pubmed_authors>Johnson J</pubmed_authors><pubmed_authors>Mak RH</pubmed_authors><pubmed_authors>Thiriveedhi V</pubmed_authors><pubmed_authors>Lu MT</pubmed_authors><pubmed_authors>Aerts HJWL</pubmed_authors><pubmed_authors>Bernatz S</pubmed_authors></additional><is_claimable>false</is_claimable><name>Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals.</name><description>&lt;h4>Background&lt;/h4>Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.&lt;h4>Purpose&lt;/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).&lt;h4>Material and methods&lt;/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</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Mar</publication><modification>2026-06-03T04:50:38.512Z</modification><creation>2025-04-04T00:16:18.085Z</creation></dates><accession>S-EPMC11846076</accession><cross_references><pubmed>39868889</pubmed><doi>10.1111/joim.20053</doi></cross_references></HashMap>