A transformer-based prognostic signature integrating tumor and body composition CT images predicts postoperative recurrence in gastric cancer.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, the First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, China.
- Department of Hepatobiliary and Intestinal Surgery, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China.
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
- Department of Radiology, the First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, China. [email protected].
- Department of Radiology, the First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, China. [email protected].
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.