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journal article·2026-06-01Open access

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, Weihua Guo, Zheng Wang, Lian Yang, Yanfeng Niu

Advanced Science
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Abstract

ABSTRACT Background and aims : Conventional 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. Methods : 1196 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. Results : AI‐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. Conclusion : Multidimensional body composition is associated with prognosis in GC and may serve as a complementary biomarker for risk stratification.
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Research context

OpenAlex topics: Nutrition and Health in Aging · Gastric Cancer Management and Outcomes · Body Composition Measurement Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Hubei Provincial Clinical Research Center For Precision Radiology & Interventional Medicine Wuhan China

Hubei Key Laboratory of Molecular Imaging Wuhan China

Department of Radiology Hubei Cancer Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Department of Thoracic Surgery The First Affiliated Hospital of Wenzhou Medical University Wenzhou Zhejiang China

Department of Clinical Laboratory Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Department of Gastrointestinal Surgery Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Hubei Provincial Engineering Research Center of Clinical Laboratory and Active Health Smart Equipment Wuhan China

Hubei Key Laboratory of Regenerative Medicine and Multi‐disciplinary Translational Research Wuhan China

Key Laboratory of Biological Targeted Therapy Huazhong University of Science and Technology Ministry of Education Wuhan Hubei China

Clinical Research Center of Minimally Invasive Surgery Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Funding

National Natural Science Foundation of China

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