Evidence map›Paper›PMID 41784694›Full record

ArticleAbdominal radiology (New York)2026

Longitudinal evaluation of intra-patient changes in computed tomography-based body composition measures.

B Dustin Pooler, John W Garrett, Matthew H Lee, Ronald M Summers, Perry J Pickhardt

Abstract read
In one paragraph

Article in Abdominal radiology (New York), 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

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

B Dustin PoolerSchool of Medicine and Public Health, University of Wisconsin-Madison, Madison, USA. bpooler@uwhealth.org.
John W GarrettSchool of Medicine and Public Health, University of Wisconsin-Madison, Madison, USA.
Matthew H LeeSchool of Medicine and Public Health, University of Wisconsin-Madison, Madison, USA.
Ronald M SummersImaging Biomarkers and Computer-aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, USA.
Perry J PickhardtSchool of Medicine and Public Health, University of Wisconsin-Madison, Madison, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo report longitudinal intra-patient changes in CT-based body composition using fully automated AI tools in an adult patient sample.

methodsThis retrospective longitudinal study included 15,616 adult patients (mean age at first CT, 53.0 ± 14.7 years; 7096 male, 8520 female) who underwent at least two abdominal CT examinations at least five years apart (mean study interval, 9.1 ± 3.3 years, range, 5.0-20.4 years) at a single academic institution between January 1, 2000 and February 28, 2021. CT examinations were not restricted based on patient setting, clinical indication, or IV contrast media use. Seven fully automated AI body composition tools quantifying vertebral trabecular attenuation, skeletal muscle area and attenuation, visceral adipose tissue (VAT) area and attenuation, subcutaneous adipose tissue (SAT) area, and VAT/SAT ratio (VSR) were applied to each patient's first and last available abdominal CT. Change in body composition per year were determined using the first and last available CT scans. T-test and linear regression were used to assess sex and age as predictors of longitudinal body composition change, respectively.

resultsSignificant differences in sex-specific mean rates of change were observed for all measures (p < 0.05) except muscle attenuation. Certain CT biomarkers showed varying rates of change among younger, middle-age, and older adults. Age significantly predicted body composition measures except VSR in female patients, although effect size was small (R

conclusionThere are significant age and sex-specific differences in longitudinal, intra-patient body composition changes over time.

Indexed as

Body CompositionTomography, X-Ray ComputedAdultAgedAged, 80 and overFemaleHumansLongitudinal StudiesMaleMiddle AgedRadiography, AbdominalRetrospective StudiesAbdomenArtificial intelligence (AI)Body compositionComputed tomography (CT)

Identifiers

PMID41784694
PMCPMC13476182

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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.