ArticleNPJ digital medicine2025
A transformer-based prognostic signature integrating tumor and body composition CT images predicts postoperative recurrence in gastric cancer.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A nomogram integrating p53 and Ki-67 expression for predicting recurrence risk in postoperative early gastric cancer.World journal of surgical oncology · 2026Article
- A multicenter-validated interpretable transformer model for pituitary microadenoma detection on non-contrast multiparametric MRI.BMC medical imaging · 2026Article
- Association between abdominal muscle mass measured by dual-source computed tomography and coronary artery calcification in patients with Type 2 diabetes mellitus.Frontiers in endocrinology · 2026Article
- Diagnostic performance of Node-RADS in assessing regional lymph node metastasis and prognosis in resected gastric cancer.Science progressArticle
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Authors and funding
14 authors.
Funding
Abstract
Accurate preoperative prognosis prediction is crucial for gastric cancer (GC) treatment planning, yet existing models overlook body composition integration. This study demonstrates the potential of integrating multimodal data, including skeletal muscle (SM), adipose tissue (AT), and primary tumor computed tomography images, to improve prognostic stratification in GC patients using an entire cohort of 1862 patients. By leveraging a Vision Transformer-based deep learning approach, we developed and validated a SM-AT-Tumor-Clinical (SMAT-TC) integrated score to predict recurrence-free survival (RFS) in GC patients. The SMAT-TC score achieved a C-index of 0.966 (95% CI: 0.937-0.990), 0.890 (95% CI: 0.866-0.915), and 0.855 (95% CI: 0.829-0.881) in the training, internal validation, and external validation cohorts, respectively, outperforming the Clinical, SM, AT, Tumor, Tumor-Clinical (TC), and SM-Tumor-Clinical (SM-TC) models. The net reclassification improvement and integrated discrimination improvement confirmed the incremental value of body composition. The SMAT-TC score was an independent risk factor for recurrence. The SMAT-TC model could stratify patients into high-, medium-, and low-risk groups with distinct 3- (99.6% vs. 67.0% vs. 10.9%) and 5-year RFS rates (98.8% vs. 61.7% vs. 2.4%). Collectively, the SMAT-TC score may serve as a novel imaging biomarker for GC patients, enhancing risk stratification and guiding individualized treatment strategies.
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Registered trials
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