基于超声及临床特征构建胃癌异时性肝转移预后模型的研究

陈曦&nbsp;, 王立坤, 孙改霞, 武雪亮

doi:10.13418/j.issn.1001-165x.2026.1.17

中国临床解剖学杂志 >

2026 , Vol. 44 >Issue 1: 109 - 116

DOI: https://doi.org/10.13418/j.issn.1001-165x.2026.1.17

临床研究

基于超声及临床特征构建胃癌异时性肝转移预后模型的研究

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河北北方学院附属第一医院 1.超声医学科， 2.病理科， 3.普通外科，河北张家口 075000

陈曦（1989-），女，河北张家口人，主治医师，硕士研究生，研究方向：主要从事研究超声医学方向，E-mail: CHENxi1989120@126com.vip

收稿日期: 2025-06-25

网络出版日期: 2026-01-31

基金资助

河北省医学科学研究课题(20241015)

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Construction of a prognostic model for metachronous liver metastasis of gastric cancer based on ultrasound and clinical characteristics

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The First Affiliated Hospital of Hebei North University 1. Department of Ultrasound Medicine; 2. Department of Pathology; 3. Department of General Surgery, Zhangjiakou 075000, Hebei Province, China

Received date: 2025-06-25

Online published: 2026-01-31

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摘要

目的基于超声及临床特征等多维度因素，构建预测胃癌异时性肝转移预后的模型。方法回顾性分析2021年6月至2023年6月就诊的152例胃癌患者，分为转移组（n=52）和未转移组（n=100）。比较两组临床及超声特征，通过Logistic回归分析筛选影响因素，构建预测模型，并采用受试者工作特征曲线（ROC）和决策曲线分析（DCA）评估模型效能。结果身体质量指数（BMI）、癌胚抗原（CEA）、术中失血量、纵横比、肿瘤直径、形态、边界、钙化是独立风险因子。构建模型的ROC曲线下面积为0.843，敏感度为85.7%，特异度为64.6%。Bootstrap自抽样法内部验证显示ROC曲线下面积为0.856，敏感度为86.8%，特异度为68.9%。结论基于超声及临床特征构建的预测模型能有效评估胃癌异时性肝转移风险。

关键词： 超声; ; ; 临床特征; ; ; 胃癌异时性肝转移; ; ; 预后; ; ; 模型

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陈曦 , 王立坤, 孙改霞, 武雪亮 . 基于超声及临床特征构建胃癌异时性肝转移预后模型的研究[J]. 中国临床解剖学杂志, 2026 , 44(1) : 109 -116 . DOI: 10.13418/j.issn.1001-165x.2026.1.17

Abstract

Objective To construct a prognostic model for predicting metachronous liver metastasis (MLM) in gastric cancer (GC) based on multidimensional factors, including ultrasound and clinical characteristics. Methods A retrospective analysis was conducted on 152 patients with gastric cancer treated between June 2021 and June 2023. They were divided into a metastasis group (n=52) and a non-metastasis group (n=100). Clinical and ultrasound features were compared between the two groups. Independent risk factors were identified through logistic regression analysis to construct the predictive model. The model's performance was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results Body mass index (BMI), carcinoembryonic antigen (CEA), intraoperative blood loss, aspect ratio, tumor diameter, morphology, margin, and calcification were identified as independent risk factors. The area under the ROC curve (AUC) of the constructed model was 0.843, with a sensitivity of 85.7% and specificity of 64.6%. Internal validation via bootstrap resampling demonstrated an AUC of 0.856, sensitivity of 86.8%, and specificity of 68.9%. Conclusions The prognostic model based on ultrasound and clinical characteristics effectively evaluates the risk of metachronous liver metastasis in gastric cancer.

Key words： Ultrasonography; ; ; Clinical characteristics; ; ; Metachronous hepatic metastasis in gastric cancer; ; ; Prognosis; ; ; Model

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