文献类型：期刊文献

英文题名：Spectral computed tomography-based quantitative parameters combined with extracellular volume fraction to predict lymph node metastases in gastric cancer

作者：Zhang, Xiuling[1];Peng, Leping[1];Ma, Fang[1];Zhang, Fan[1];Zhang, Xiaoyue[2];Liang, Xiaoqin[3];Wei, Zhaokun[4];Li, Xinli[4];Ma, Yaqiong[4];Huang, Gang[4];Wang, Lili[4]

第一作者：章雪兰;张小丽;张西玲;张小莉;张晓兰;张晓凌

通信作者：Huang, G[1];Wang, LL[1]

机构：[1]Gansu Univ Chinese Med, Lanzhou 730000, Peoples R China;[2]Philips Healthcare, Dept Clin & Tech Support, Xian 710065, Peoples R China;[3]Gansu Prov Hosp, Dept Pathol, Lanzhou 730000, Peoples R China;[4]Gansu Prov Hosp, Dept Radiol, Lanzhou 730000, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Hosp, Dept Radiol, Lanzhou 730000, Peoples R China.

年份：2025

外文期刊名：EUROPEAN RADIOLOGY

收录：;Scopus(收录号：2-s2.0-105007439344);WOS:【SCI-EXPANDED(收录号:WOS:001502125100001)】；

基金：We express our gratitude to Dr. Zhu Yi from Philips Healthcare, Xi'an, for his valuable discussions and assistance in clinical and technical support. Additionally, we appreciate the efforts of the staff from the Department of Radiology and the Department of Pathology at Gansu Provincial Hospital in collecting the information used in this study.

语种：英文

外文关键词：Gastric cancer; Lymph node; Metastasis; Computed tomography

摘要：Objectives Preoperative prediction of lymph node metastasis (LNM) is important for gastric cancer (GC) diagnosis, treatment and prognosis. This study aimed to predict LNM risk in GC using quantitative parameters and extracellular volume fraction (ECV%) derived from spectral computed tomography (CT). Materials and Methods Data from 230 lymph nodes (LNs) (97 nonmetastatic, 133 metastatic) were collected from 70 GC patients and were randomly divided into a training cohort and a test cohort (6:4 ratio). LN qualitative features (including edge, shape and degree of enhancement), spectral CT-derived quantitative parameters and ECV% were assessed. Multivariate logistic regression analysis with the forward variable selection method was used to build 3 models: Model 1 (traditional features: LN edge, short axis diameter), Model 2 (spectral CT parameters: iodine concentration in arterial and delayed phases), and Model 3 (spectral CT parameters and ECV%). Diagnostic performance was evaluated using AUC and compared with the Delong test. Results In both cohorts, a significant difference in ECV% was observed between positive and negative LNs (p < 0.001), and the diagnostic efficacy of ECV% (AUC = 0.823 and 0.803, respectively, both p < 0.001) was higher than that of other parameters. Model 3 demonstrated significantly higher diagnostic efficacy than Models 1 and 2 in both cohorts (AUC = 0.858 and 0.881, respectively; both p < 0.001). Conclusion ECV% can help diagnose LNM in GC, and combining the spectral CT quantitative features with ECV% can further improve diagnosis. This finding enables accurate preoperative prediction of LNM and the GC prognosis so that patients receive personalized treatment.