Abdominal radiology (New York) · 2025
A nomogram model based on CT-assessed body composition parameters for predicting postoperative recurrence in advanced gastric cancer.
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Xu Mengying, Wang Le, Sun Shuangshuang, Zhou Zhengyang, Liu Song
Аннотация
PURPOSE: This study aimed to develop a nomogram model based on computed tomography (CT) assessed body composition parameters to predict recurrence-free survival (RFS) and stratify the risk of recurrence in advanced gastric cancer (GC) patients.
METHODS: This retrospective study included 111 patients with locally advanced GC. Preoperative CT-assessed body composition and parenchymal fat parameters of all patients were collected. Univariate and multivariate Cox analyses were performed to determine independent predictors for RFS. A nomogram model was subsequently established on the basis of the independent risk factors. The performance of the nomogram was evaluated utilizing the concordance index (C-index), calibration curve, and receiver operating characteristic curve analysis.
RESULTS: The nomogram model integrating four independent predictors, including the skeletal muscle index, visceral adipose tissue radiation attenuation, the body of pancreatic density (PD), and PD (tail), was established for predicting RFS in advanced GCs and achieved a C-index of 0.743 (95% confidence interval: 0.678-0.808). The calibration curves showed good concordances. In addition, compared to the pathological tumor-node-metastasis classification, the nomogram model showed comparable performance for predicting 1-year RFS and better efficacy for predicting 3- and 5-year RFS. The Kaplan-Meier curves demonstrated the ability of the nomogram to stratify patients according to risk (p < 0.001).
CONCLUSION: The nomogram model exhibited favorable predictive performance and could stratify patients according to the risk of postoperative recurrence for advanced GCs, which might help enhance individualized surveillance in clinical practice.
METHODS: This retrospective study included 111 patients with locally advanced GC. Preoperative CT-assessed body composition and parenchymal fat parameters of all patients were collected. Univariate and multivariate Cox analyses were performed to determine independent predictors for RFS. A nomogram model was subsequently established on the basis of the independent risk factors. The performance of the nomogram was evaluated utilizing the concordance index (C-index), calibration curve, and receiver operating characteristic curve analysis.
RESULTS: The nomogram model integrating four independent predictors, including the skeletal muscle index, visceral adipose tissue radiation attenuation, the body of pancreatic density (PD), and PD (tail), was established for predicting RFS in advanced GCs and achieved a C-index of 0.743 (95% confidence interval: 0.678-0.808). The calibration curves showed good concordances. In addition, compared to the pathological tumor-node-metastasis classification, the nomogram model showed comparable performance for predicting 1-year RFS and better efficacy for predicting 3- and 5-year RFS. The Kaplan-Meier curves demonstrated the ability of the nomogram to stratify patients according to risk (p < 0.001).
CONCLUSION: The nomogram model exhibited favorable predictive performance and could stratify patients according to the risk of postoperative recurrence for advanced GCs, which might help enhance individualized surveillance in clinical practice.