Evidence map›Paper›PMID 42492115›Full record

ArticleEuropean journal of radiology2026

Volumetric assessment from L2 to L4 improves the reliability and validity of body composition by CT.

Alexander D Weston, Jennifer L St Sauver, Ryan T Moerer, Brandon R Grossardt, Timothy L Kline, Pralohith R Chinthalapelly, Hillary W Garner, Bradley J Erickson, Andrew D Rule

Abstract read
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Article in European journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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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.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Alexander D WestonClinical Trials and Biostatistics, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Jennifer L St SauverDivision of Epidemiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Ryan T MoererClinical Trials and Biostatistics, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Brandon R GrossardtClinical Trials and Biostatistics, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Timothy L KlineDiagnostic Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Pralohith R ChinthalapellyClinical Trials and Biostatistics, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Hillary W GarnerDiagnostic Radiology, Mayo Clinic, 4500 San Pablo Road S, Jacksonville, FL 32224, USA.
Bradley J EricksonDiagnostic Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Andrew D RuleDivision of Epidemiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. Electronic address: rule.andrew@mayo.edu.

Funding

A population-based study of deep learning derived organ and tissue measures for accelerated aging using repurposed abdominal CT imagesR01AG081223 · NIA · MAYO CLINIC ROCHESTER · PI ANDREW David RULE, JENNIFER LYNN ST SAUVER · 2023 to 2026
$2.7M
Interdisciplinary Infrastructure for Aging Research: Rochester Epidemiology ProjectR33AG058738 · NIA · MAYO CLINIC ROCHESTER · PI LEBRASSEUR, NATHAN K, OLSON, JANET E · 2020 to 2022
$2.4M
Interdisciplinary Infrastructure for Aging Research: Rochester Epidemiology ProjectR21AG058738 · NIA · MAYO CLINIC ROCHESTER · PI LEBRASSEUR, NATHAN K, OLSON, JANET E · 2018 to 2019
$437k
NIA NIH HHS R01 AG081223NIA NIH HHS R21 AG058738NIA NIH HHS R33 AG058738
6 · The paper itself

Abstract

backgroundBody composition (skeletal muscle, adipose tissue, and bone) is a general measure of health that can be determined from abdominal computed tomography (CT) using deep learning (DL) algorithms. Use of a volumetric approach, rather than the more commonly used single axial slice, likely improves the reliability and validity of body composition biomarkers, but a standardized approach is needed.

methodsA study was designed to test the reliability of volumetric body composition assessment in serial CTs. Residents of Southern Minnesota and Western Wisconsin who had two consecutive abdominal CT exams 1 to 14 days apart from 2010 to 2021 were identified. Previously-validated DL models were applied to both CT exams, and three methods of determining body composition biomarkers were compared: two-dimensional (2D) model on a slice at the midpoint of L3, three-dimensional (3D) model on a slice at the midpoint of L3, and 3D model on slices extending from L2 to L4. For each method, reliability was assessed by the intraclass correlation coefficient (ICC) separately in paired noncontrast and paired contrast-enhanced CT exams and validity was assessed by the strength of association of each body composition measure with age.

resultsThere were 3,376 persons with paired noncontrast and 9,103 persons with paired contrast-enhanced CT exams. The 3D body composition biomarkers from L2-L4 showed generally better reliability than the 2D or 3D biomarkers at the midpoint of L3 with higher ICCs observed with or without use of contrast. The 3D body composition more strongly correlated with age than 2D body composition for most biomarkers. The 3D bone area from L2-L4 more strongly correlated with age than 3D bone area at the midpoint of L3.

conclusionVolumetric L2-L4 body composition analysis shows better reliability and validity than single slice L3 analysis, and this may result in more accurate clinical associations with body composition.

Indexed as

Body CompositionImaging, Three-DimensionalLumbar VertebraeTomography, X-Ray ComputedAdultAgedDeep LearningFemaleHumansMaleMiddle AgedMinnesotaRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsWisconsin

Identifiers

PMID42492115
PMCPMC13573815

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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.