Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals

Jakob Weiss, Simon Bernatz, Justin Johnson, Vamsi Thiriveedhi, Raymond H Mak, Andriy Fedorov, Michael T Lu, Hugo J W L Aerts*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease. Purpose: 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). Material and methods: 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 [BMI] < 25 kg/m 2) and overweight (BMI ≥ 25 kg/m 2). The primary outcome was all-cause mortality. Cox proportional hazard regression assessed the association between (1) SLD and mortality at baseline and (2) the association between a change in HFF and mortality within 1 year. Results: There were 5.1% (1000/19,760) all-cause deaths over a median follow-up of 6 (range, 0.8–6) years. At baseline, SLD was associated with increased mortality in lean but not in overweight/obese participants as compared to participants without SLD (hazard ratio [HR] adjusted for risk factors: 1.93 [95% confidence interval 1.52–2.45]; p = 0.001). Individuals with an increase in HFF within 1 year had a significantly worse outcome than participants with stable HFF (HR adjusted for risk factors: 1.29 [1.01–1.65]; p = 0.04). Conclusion: SLD is an independent predictor for long-term mortality in heavy smokers beyond known clinical risk factors.

Original languageEnglish
Pages (from-to)276-288
Number of pages13
JournalJournal of Internal Medicine
Volume297
Issue number3
Early online date27 Jan 2025
DOIs
Publication statusPublished - Mar 2025

Keywords

  • CT imaging
  • artificial intelligence
  • lung cancer screening
  • opportunistic
  • risk assessment
  • steatotic liver disease

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