Evidence map›Paper›PMID 37191484›Full record

ArticleRadiology2023

AI-based CT Body Composition Identifies Myosteatosis as Key Mortality Predictor in Asymptomatic Adults.

Maxime Nachit, Yves Horsmans, Ronald M Summers, Isabelle A Leclercq, Perry J Pickhardt

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 2 pooled it
18.0field-weighted citation impact, top 1% of its field
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

76 citing papers in PubMed, 2 syntheses or guidelines pooled it, 103 citations in OpenAlex.

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  7. Future of CT body composition research: Methodological discrepancies and advances.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2026
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16 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 2 countries.

Maxime NachitFrom the Laboratory of Hepato-Gastroenterology, Institut de Recherche Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.); Service d'Hépato-Gastro-Entérologie, Cliniques Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0002-2333-9213
Yves HorsmansFrom the Laboratory of Hepato-Gastroenterology, Institut de Recherche Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.); Service d'Hépato-Gastro-Entérologie, Cliniques Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0002-5779-2454
Ronald M SummersFrom the Laboratory of Hepato-Gastroenterology, Institut de Recherche Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.); Service d'Hépato-Gastro-Entérologie, Cliniques Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0001-8081-7376
Isabelle A LeclercqFrom the Laboratory of Hepato-Gastroenterology, Institut de Recherche Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.); Service d'Hépato-Gastro-Entérologie, Cliniques Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).
Perry J PickhardtFrom the Laboratory of Hepato-Gastroenterology, Institut de Recherche Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.); Service d'Hépato-Gastro-Entérologie, Cliniques Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).ORCID 0000-0002-5534-8202
Cliniques Universitaires Saint-Luc · BE

Funding

Computer Aided Detection for CT ColonographyZIACL040003 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2009 to 2025
$0k
Computer Aided Detection for Radiologic ImagesZIACL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2009 to 2025
$0k
6 · The paper itself

Abstract

Background Body composition data have been limited to adults with disease or older age. The prognostic impact in otherwise asymptomatic adults is unclear. Purpose To use artificial intelligence-based body composition metrics from routine abdominal CT scans in asymptomatic adults to clarify the association between obesity, liver steatosis, myopenia, and myosteatosis and the risk of mortality. Materials and Methods In this retrospective single-center study, consecutive adult outpatients undergoing routine colorectal cancer screening from April 2004 to December 2016 were included. Using a U-Net algorithm, the following body composition metrics were extracted from low-dose, noncontrast, supine multidetector abdominal CT scans: total muscle area, muscle density, subcutaneous and visceral fat area, and volumetric liver density. Abnormal body composition was defined by the presence of liver steatosis, obesity, muscle fatty infiltration (myosteatosis), and/or low muscle mass (myopenia). The incidence of death and major adverse cardiovascular events were recorded during a median follow-up of 8.8 years. Multivariable analyses were performed accounting for age, sex, smoking status, myosteatosis, liver steatosis, myopenia, type 2 diabetes, obesity, visceral fat, and history of cardiovascular events. Results Overall, 8982 consecutive outpatients (mean age, 57 years ± 8 [SD]; 5008 female, 3974 male) were included. Abnormal body composition was found in 86% (434 of 507) of patients who died during follow-up. Myosteatosis was found in 278 of 507 patients (55%) who died (15.5% absolute risk at 10 years). Myosteatosis, obesity, liver steatosis, and myopenia were associated with increased mortality risk (hazard ratio [HR]: 4.33 [95% CI: 3.63, 5.16], 1.27 [95% CI: 1.06, 1.53], 1.86 [95% CI: 1.56, 2.21], and 1.75 [95% CI: 1.43, 2.14], respectively). In 8303 patients (excluding 679 patients without complete data), after multivariable adjustment, myosteatosis remained associated with increased mortality risk (HR, 1.89 [95% CI: 1.52, 2.35];

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2Fatty LiverSarcopeniaAdultArtificial IntelligenceBody CompositionFemaleHumansMaleMiddle AgedMuscle, SkeletalObesityRetrospective StudiesTomography, X-Ray Computed

Identifiers

PMID37191484
PMCPMC10315523
OpenAlexW4376640752

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.