Evidence map›Paper›PMID 39868889›Full record

ArticleJournal of internal medicine2025

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

Abstract read
In one paragraph

Article in Journal of internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jakob WeissArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.ORCID 0000-0002-0336-8519
Simon BernatzArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Justin JohnsonArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Vamsi ThiriveedhiDepartment of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.
Raymond H MakArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Andriy FedorovDepartment of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.
Michael T LuArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Hugo J W L AertsArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.

Funding

The Boston Lung Cancer Survival CohortU01CA209414 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI David C Christiani · 2017 to 2026
$12.2M
Quantitative Radiomics System Decoding the Tumor PhenotypeU24CA194354 · NCI · DANA-FARBER CANCER INST · PI AERTS, HUGO, QUACKENBUSH, JOHN · 2015 to 2019
$3.5M
Genotype and Imaging Phenotype Biomarkers in Lung CancerU01CA190234 · NCI · DANA-FARBER CANCER INST · PI AERTS, HUGO, QUACKENBUSH, JOHN · 2015 to 2019
$3.3M
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation 502050303European Union-European Research Council 866504NCI NIH HHS U01 CA190234NCI NIH HHS U01 CA209414NCI NIH HHS U24 CA194354NIH HHS NIH (NCI) 5U01CA209414NIH HHS NIH-USA R35CA22052NIH HHS NIH-USA U01CA190234NIH HHS NIH-USA U01CA209414NIH HHS NIH-USA U24CA194354
6 · The paper itself

Abstract

backgroundSteatotic 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

methodsWe 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

resultsThere 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).

conclusionSLD is an independent predictor for long-term mortality in heavy smokers beyond known clinical risk factors.

Indexed as

Early Detection of CancerFatty LiverLung NeoplasmsAgedDeep LearningFemaleHumansMaleMiddle AgedRisk FactorsTomography, X-Ray Computedartificial intelligenceCT imaginglung cancer screeningopportunisticrisk assessmentsteatotic liver disease

Identifiers

PMID39868889
PMCPMC11846076

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.