Evidence map›Paper›PMID 42063606›Full record

ArticleBJR artificial intelligence2026

Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.

Chenfei Zhou, Xuekun Zhang, David P J van Dijk, Sander S Rensen, Jun Zhang, Leroy Volmer, Leonard Wee, Steven W M Olde Damink

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BJR artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chenfei ZhouDepartment of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.ORCID https://orcid.org/0000-0002-9760-3216
Xuekun ZhangDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
David P J van DijkDepartment of Surgery, NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, 6200 MD, The Netherlands.
Sander S RensenDepartment of Surgery, NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, 6200 MD, The Netherlands.
Jun ZhangDepartment of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Leroy VolmerDepartment of Radiation Oncology (MAASTRO), GROW Research Institute of Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, 6200 MD, The Netherlands.
Leonard WeeDepartment of Radiation Oncology (MAASTRO), GROW Research Institute of Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, 6200 MD, The Netherlands.ORCID https://orcid.org/0000-0003-1612-9055
Steven W M Olde DaminkDepartment of Surgery, NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, 6200 MD, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Deep learning neural network (DLNN)-based tools can automate body composition analysis for cancer cachexia research. We aimed to evaluate a DLNN tool trained on a European population of Chinese cancer patients. Methods: Computed tomography (CT) images at the 3rd lumbar vertebral (L3) level of Chinese gastric cancer patients were retrospectively collected. An externally validated DLNN tool (Mosamatic) was used to segment skeletal muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). Manual segmentation was performed using SliceOmatic software (TomoVision, version 5.0). Geometric similarity between automated and manual segmentation, and the reliability was assessed. Results: The cohort comprised 203 patients with a median body mass index (BMI) of 22.2 kg/m Conclusions: The Mosamatic tool displays excellent generalizability to analyse body compositions in Chinese gastric cancer patients and can facilitate cachexia research. Advances in knowledge: The Mosamatic tool displayed excellent generalizability without recalibration to analyse body composition on the 3rd lumbar vertebral CT images in Chinese gastric cancer patients.

Indexed as

automated segmentationbody compositioncomputed tomographydeep learning

Identifiers

PMID42063606
PMCPMC13045518

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.