Song Anyi, Huang Zhaoheng, Xu Jiahuan, Chen Jinghao, Gong Haipeng, Yang Chunyan, Zhu Zhengqi
Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, China.
Department of Radiology, The Second Affiliated Hospital of Nantong University, Nantong, China.
J Gastrointest Oncol. 2025 Jun 30;16(3):875-889. doi: 10.21037/jgo-24-838. Epub 2025 Jun 27.
Computed tomography (CT) body composition is associated with the prognosis of gastric cancer (GC), but few studies have investigated the prognostic value of CT body composition combined with preoperative clinical indicators in GC. This study aimed to develop and validate a nomogram model using preoperative CT-quantified body composition parameters and clinical indicators to predict recurrence-free survival (RFS) in patients undergoing radical resection for GC.
We retrospectively analyzed patients with pathologically confirmed GC who underwent preoperative CT scans between October 2018 and May 2023. Multivariate Cox regression analysis was performed on the derivation cohort to identify preoperative predictors independently associated with RFS and to construct a nomogram model. The model was then validated in a separate test set.
A total of 450 patients were included, with 268 in the derivation set and 182 in the test set. Five variables, visceral adipose tissue (VAT) density, visceral obesity, sarcopenia, neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index (PNI), were identified as independent predictors of RFS. The preoperative nomogram model demonstrated superior predictive accuracy compared to pathological tumor staging at various time points. Calibration curves showed good agreement between the model's predictions and actual outcomes. Decision curve analysis (DCA) indicated significant clinical benefit. The model effectively stratified patients into low-risk and high-risk groups for recurrence.
The preoperative nomogram model is a valuable tool for predicting RFS in patients undergoing radical resection for GC.
计算机断层扫描（CT）身体成分与胃癌（GC）的预后相关，但很少有研究探讨CT身体成分与术前临床指标相结合在GC中的预后价值。本研究旨在开发并验证一种列线图模型，该模型使用术前CT量化的身体成分参数和临床指标来预测接受GC根治性切除患者的无复发生存期（RFS）。
我们回顾性分析了2018年10月至2023年5月期间接受术前CT扫描且病理确诊为GC的患者。对推导队列进行多变量Cox回归分析，以确定与RFS独立相关的术前预测因素，并构建列线图模型。然后在一个单独的测试集中对该模型进行验证。
共纳入450例患者，其中推导集268例，测试集182例。五个变量，即内脏脂肪组织（VAT）密度、内脏肥胖、肌肉减少症、中性粒细胞与淋巴细胞比值（NLR）和预后营养指数（PNI），被确定为RFS的独立预测因素。术前列线图模型在各个时间点的预测准确性均优于病理肿瘤分期。校准曲线显示模型预测与实际结果之间具有良好的一致性。决策曲线分析（DCA）表明具有显著的临床益处。该模型有效地将患者分为复发低风险和高风险组。
术前列线图模型是预测接受GC根治性切除患者RFS的有价值工具。
