基于CT的治疗前淋巴结影像报告和数据系统（Node-RADS）评分对预测局部进展期胃癌生存结局的附加价值：与临床N分期比较
Added value of pretreatment CT-based Node-RADS score for predicting survival outcome of locally advanced gastric cancer: compared with clinical N stage.
作者信息
Sun Yan, Wen Lu, Xiang Wang, Luo Xiangtong, Chen Lian, Yang Xiaohuang, Yang Yanhui, Zhang Yi, Yu Sanqiang, Xiao Hua, Yu Xiaoping
机构信息
Department of Diagnostic Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China.
Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Changsha, China.
出版信息
BMC Cancer. 2025 Apr 2;25(1):598. doi: 10.1186/s12885-025-14032-z.
OBJECTIVES
The Node Reporting and Data System (Node-RADS) offers a reliable framework for lymph node assessment, but its prognostic significance remains unexplored. This study aims to investigate the added prognostic value of Node-RADS in patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant chemotherapy (NAC) followed by gastrectomy.
MATERIALS AND METHODS
This single-center retrospective study included 118 patients with LAGC underwent NAC and gastrectomy. The maximum Node-RADS score and the number of metastatic lymph node stations (defined as LNM-Station) were evaluated on pretreatment CT. The pretreatment Node-RADS-CT and Node-RADS-integrated models were developed using Cox regression to predict overall survival (OS) and disease-free survival (DFS). The pretreatment cN-CT models, cN-integrated models, as well as post-NAC pathological models were also developed in comparison. The performance of the models was assessed in terms of discrimination, calibration and clinical applicability.
RESULTS
The LNM-Station was significantly associated with OS and DFS (all p < 0.05). The Node-RADS-CT model showed higher Harrell's consistency index (C-index) than cN-CT model (0.755 vs. 0.693 for OS, p = 0.017; 0.759 vs. 0.706 for DFS, p = 0.018). The Node-RADS-integrated model also achieved higher C-index than cN-integrated model (0.771 vs. 0.731 for OS, p = 0.091; 0.773 vs. 0.733 for DFS, p = 0.053). The net reclassification improvement (NRI) of the Node-RADS-integrated model at 5 years was 0.379 for OS and 0.364 for DFS (all p < 0.05). The integrated discrimination improvement (IDI) of the Node-RADS-integrated model was 0.103 for OS and 0.107 for DFS (all p < 0.05). The C-indices (OS: 0.745; DFS: 0.746) of pathological models were slightly lower than those of Node-RADS-based models (all p > 0.05).
CONCLUSION
The baseline Node-RADS score and LNM-Station were effective prognostic indicators for LAGC. The pretreatment CT Node-RADS-based models can offer added prognostic value for LAGC, compared with clinical N stage.
目的
淋巴结报告与数据系统（Node-RADS）为淋巴结评估提供了一个可靠的框架，但其预后意义仍未得到探索。本研究旨在探讨Node-RADS在接受新辅助化疗（NAC）后行胃切除术的局部进展期胃癌（LAGC）患者中的额外预后价值。
材料与方法
这项单中心回顾性研究纳入了118例行NAC和胃切除术的LAGC患者。在治疗前的CT上评估最大Node-RADS评分和转移淋巴结站数（定义为LNM-Station）。使用Cox回归建立治疗前的Node-RADS-CT模型和Node-RADS综合模型，以预测总生存期（OS）和无病生存期（DFS）。同时还建立了治疗前的cN-CT模型、cN综合模型以及NAC后的病理模型进行比较。根据区分度、校准度和临床适用性对模型的性能进行评估。
结果
LNM-Station与OS和DFS显著相关（所有p<0.05）。Node-RADS-CT模型的Harrell一致性指数（C指数）高于cN-CT模型（OS：0.755对0.693，p=0.017；DFS：0.759对0.706，p=0.018）。Node-RADS综合模型的C指数也高于cN综合模型（OS：0.771对0.731，p=0.091；DFS：0.773对0.733，p=0.053）。Node-RADS综合模型在5年时的净重新分类改善（NRI）对于OS为0.379，对于DFS为0.364（所有p<0.05）。Node-RADS综合模型的综合区分改善（IDI）对于OS为0.103，对于DFS为0.107（所有p<0.05）。病理模型的C指数（OS：0.745；DFS：0.746）略低于基于Node-RADS的模型（所有p>0.05）。
结论
基线Node-RADS评分和LNM-Station是LAGC有效的预后指标。与临床N分期相比，基于治疗前CT的Node-RADS模型可为LAGC提供额外的预后价值。
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