Evidence map›Paper›PMID 42676703›Full record

ArticleRadiology research and practice2026

Artificial Intelligence-Derived 3D Body Composition Analysis of the Entire Lumbar Region From CT Scans Reveals Variation Across Disease Stages at Colorectal Cancer Diagnosis.

Ke Cao, Lara Wirth, Susan Teo, Josephine Yeung, Paul N Baird, Justin M C Yeung

Abstract read
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Article in Radiology research and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Ke CaoDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia, unimelb.edu.au.ORCID https://orcid.org/0000-0002-7437-8845
Lara WirthDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia, unimelb.edu.au.ORCID https://orcid.org/0009-0003-2045-5336
Susan TeoDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia, unimelb.edu.au.ORCID https://orcid.org/0009-0001-3670-2990
Josephine YeungDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia, unimelb.edu.au.ORCID https://orcid.org/0000-0001-6462-1656
Paul N BairdDepartment of Surgery, University of Melbourne, Melbourne, Australia, unimelb.edu.au.ORCID https://orcid.org/0000-0002-1305-3502
Justin M C YeungDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia, unimelb.edu.au.ORCID https://orcid.org/0000-0002-7230-149X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: While changes in weight are often observed in patients with colorectal cancer (CRC), they provide limited information regarding how or if body composition changes might occur. We used artificial intelligence (AI) to analyse computed tomography (CT) images from patients' lumbar region to investigate how body composition differs across clinical disease stages of CRC. Methods: A retrospective analysis was performed on patients diagnosed with CRC and treated at Western Health, Melbourne (2013-2023). Three-dimensional measures of skeletal muscle (SM), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) volumes and radiodensities were automatically segmented from staging CT scans (L1-L5) using a pretrained AI model. Disease stage was determined using the Australian Clinicopathological Staging (ACPS) system. Body composition values were adjusted for physiological variation (age, sex and height) and compared across ACPS stages. Results: A total of 1138 individuals were evaluated (68.2 ± 13.0 years; 59.8% male), with 24% classified as Stage A, 30.5% as Stage B, 17.5% as Stage C, and 28% as Stage D. No significant differences were observed between Stages B and C, and these groups were therefore combined (Stage B/C) for further analysis. VAT volume was significantly lower in Stage B/C versus Stage A ( Conclusion: CRC patients were shown to exhibit distinct body composition changes at diagnosis depending on disease stage. Adipose tissue loss and density changes occurred earlier than muscle changes. Longitudinal studies are warranted to understand these trajectories but suggest that nutritional and supportive interventions could be targeted based on CRC stage at diagnosis.

Indexed as

artificial intelligencebody compositioncolorectal cancercomputed tomography

Identifiers

PMID42676703
PMCPMC13527157

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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.