Evidence map›Paper›PMID 42220087›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Artificial Intelligence-Based Body Composition Analysis Reveals Sex-Specific Prognostic Markers and Their Clinical Value in Gastric Cancer: A Multicenter Study.

Tianxiang Li, Tingting Liu, Qian Yang, Zhiqing Zhang, Xiaofang Guo, Xiang Wang, Yihan Zhang, Limin Ge, Yu Su, Wenjun Wu and 4 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

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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

The trial behind it

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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

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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

14 authors.

Tianxiang LiDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Tingting LiuDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qian YangDepartment of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zhiqing ZhangDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaofang GuoDepartment of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiang WangDepartment of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Yihan ZhangDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Limin GeDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yu SuDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Wenjun WuDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Weihua GuoDepartment of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zheng WangDepartment of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Lian YangDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0000-0003-4664-2474
Yanfeng NiuDepartment of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0009-0009-3738-0338

Funding

Hubei Provincial Health Science and Technology Projects WJ2025M130Major Special Project for Technology Innovation of Hubei Province 2023BCB014National Natural Science Foundation of China 82172034National Natural Science Foundation of China 82472058National Natural Science Foundation of China 825B2054Noncommunicable Chronic Diseases-National Science and Technology Major Project 2024ZD0522802
6 · The paper itself

Abstract

BACKGROUND AND

aimsConventional body composition assessment fails to capture its multidimensional complexity in gastric cancer (GC). This study aimed to systematically evaluate multidimensional body composition and its clinical relevance in GC.

methods1196 GC patients and 983 healthy controls were retrospectively enrolled. Body composition was segmented using nnU-Net. Propensity score matching was used to compare body composition differences between patients and healthy controls. Prognostic value was assessed across treatment cohorts. TCGA pathological and transcriptomic data were integrated for exploratory analyses. Sex-specific prognostic models were developed and externally validated.

resultsAI-drived body compositions parameters showed sex-specific associations with survival. Higher muscle/fat area/index and lower fat density correlated with longer survival in surgical patients (L1 muscle area: HR = 0.438, ad p = 0.001). In ICI-treated females, higher subcutaneous fat area (SFA) improved survival (L2SFA: HR = 0.972, ad p = 0.045). Potential associations were observed between body composition and tumor microenvironmental features, including stromal composition, EMT-related pathways, and immune infiltration. Sex-specific prognostic models achieved C-indices of 0.723 (males) and 0.705 (females) in test cohorts, outperforming conventional predictors.

conclusionMultidimensional body composition is associated with prognosis in GC and may serve as a complementary biomarker for risk stratification.

Indexed as

Artificial IntelligenceBody CompositionStomach NeoplasmsAgedBiomarkers, TumorFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesSex FactorsBiomarkers, TumorArtificial intelligenceDeep learningGastric Cancer

Identifiers

PMID42220087
PMCPMC13335994

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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.