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

TL;DR
This study shows that steatotic liver disease, detected via lung cancer screening CT scans, predicts higher mortality in lean heavy smokers.
Contribution
A deep learning model was used to assess steatotic liver disease in lung cancer screening data and evaluate its mortality risk.
Findings
SLD was associated with increased mortality in lean individuals but not in overweight/obese participants.
An increase in hepatic fat fraction within one year predicted worse outcomes compared to stable levels.
Abstract
Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end‐stage liver disease. 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). 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/m2) and overweight (BMI ≥ 25 kg/m2). The primary outcome was all‐cause mortality. Cox proportional hazard regression assessed the association…
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Taxonomy
TopicsLiver Disease Diagnosis and Treatment · Hepatocellular Carcinoma Treatment and Prognosis · MRI in cancer diagnosis
