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Updated: Mar 25, 2026
Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
Published on: March 21, 2021
Determination of a new gastric cancer mortality predictor based on body composition radiodensity variables
Mariana Caleffi1, Daniela M H Padilha2, Vinicius Bassete3
1Department of Radiology and Oncology, School of Medical, Sciences, University of Campinas, Campinas, SP, Brazil.
Background & Aims:
Prognostic assessment in gastric cancer relies primarily on TNM staging, yet outcomes vary substantially among patients within the same stage. Computed tomography (CT)-derived body composition parameters, particularly adipose and muscle radiodensity, have emerged as independent prognostic factors. We aimed to evaluate a novel variable integrating visceral adipose tissue (VAT) and skeletal muscle (SM) radiodensity as a prognostic marker in gastric cancer.
Methods:
In this retrospective study, 461 patients with gastric adenocarcinoma diagnosed between 2009 and 2018 at the University of Campinas Hospital (Brazil) were included. CT images at the L3 level were segmented to quantify VAT, subcutaneous adipose tissue, and SM. Median radiodensity values (Hounsfield units, HU) were extracted. Combinations of CT-derived variables were tested for prognostic value, and the difference between VAT and SM median radiodensity (VMD) was selected. Patients were stratified into tertiles, and survival analyses were performed using Kaplan-Meier and Cox regression models adjusted for clinicopathological covariates.
Results:
Higher VMD values were strongly associated with poorer overall survival (OS) and disease-free survival (DFS). Median OS was 13.8 months in the highest tertile versus 58.5 months in the lowest tertile (p < 0.001). In multivariable analysis, VMD remained independently associated with mortality (HR 1.60, 95% CI 1.14-2.24, p < 0.05). Associations were particularly pronounced among women.
Conclusions:
VMD represents a simple and reproducible CT-derived biomarker that complements TNM staging and enhances prognostic precision in gastric cancer. Its clinical feasibility supports future multicenter validation and potential integration into routine risk assessment.
