临床外科杂志 ›› 2025, Vol. 33 ›› Issue (4): 405-409.doi: 10.3969/j.issn.1005-6483.20240424

• 论著 • 上一篇    下一篇

基于增强CT影像组学特征和参数诊断胃腺癌术前淋巴结转移的列线图预测模型

彭伟生,杨毅,杨翠婷,王成立,何桂凤,郑宇强,黄莹   

  1. 362000 福建泉州,解放军联勤保障部队第九一〇医院放射诊断科
  • 收稿日期:2024-03-26 出版日期:2025-04-20 发布日期:2025-04-20
  • 通讯作者: 黄莹,Email:huangyingct@aliyun.com

A column chart prediction model for preoperative lymph node metastasis diagnosis in gastric adenocarcinoma based on enhanced CT image radiomics features and parameters

PENG Weisheng,YANG Yi,YANG Cuiting,WANG Chengli,HE Guifeng,ZHENG YuQiang,HUANG Ying   

  1. Department of Radiology,910th Hospital of PLA Joint Logistic Support Force,Quanzhou 362000,China
  • Received:2024-03-26 Online:2025-04-20 Published:2025-04-20

摘要: 目的 基于增强CT影像组学特征和参数构建诊断胃腺癌术前淋巴结转移(LNM)的列线图预测模型,探讨其对胃腺癌病人LNM的应用价值。方法 2019年8月~2023年10月行胃癌根治术(毕Ⅱ式)+淋巴结清扫手术治疗的胃腺癌病人131例,分别记录术前增强CT提取病灶、淋巴结影像组学特征及病理检查LNM情况,通过对CT影像进行分析,提取组学特征,结合机器学习算法,将有统计学意义的增强CT影像组学特征和参数,多因素Logistic回归分析胃腺癌LNM(+)的独立危险因素并构建列线图,并对其性能进行评估。采用受试者工作特性(ROC)曲线评价预测模型的性能及临床价值,并对列线图进行内部验证。结果 单因素分析结果显示,肿瘤最大径、淋巴结状态、淋巴结组中最大淋巴结短轴长度、体积及所有淋巴结的短轴长度之和与发生LNM有关,差异有统计学意义(P<0.05);多因素Logistic回归分析提示,增强CT影像学检查中肿瘤最大径≥15mm、淋巴结状态阳性、淋巴结组中最大淋巴结短轴长度≥7mm、所有淋巴结的短轴长度之和≥11mm、淋巴结组中最大淋巴结短轴体积≥300mm3是胃腺癌病人伴LNM(+)的独立危险因素(P<0.05);使用ROC曲线绘制了模型的曲线下面积(AUC),AUC=0.816(95%CI 0.810~0.939),敏感度为0.91,特异度0.86。采用包含500次重采样的BOOTSTRAP方法进行模型验证,结果提示,所构建的列线图模型拟合曲线与理想曲线之间的差异不显著,有效性和可靠性较好。结论 基于增强CT影像组学特征和参数建立的列线图预测模型能有效预测胃腺癌术前LNM情况,为胃腺癌病人的临床决策提供有力支持。

关键词: 胃腺癌, 淋巴结转移, 增强CT影像, 影像组学, 列线图预测模型

Abstract: Objective To explore its application value of LNM in patients with gastric adenocarcinoma based on the construction of a radiomic nomogram prediction model for preoperative lymph node metastasis in gastric adenocarcinoma using enhanced CT imaging features and parameters.Methods 131 patients with gastric adenocarcinoma who underwent radical gastric cancer surgery(Billroth Ⅱ) + lymph node dissection in our hospital from August 2019 to October 2023 were retrospectively analysed,and the preoperative enhancement CT extracted lesions,histological features of lymph node images,and pathological examination of LNM were recorded respectively,and the statistically significant Enhanced CT image histological features and parameters,multifactorial Logistic regression to analyse the independent risk factors of gastric adenocarcinoma LNM(+) and construct a column-line diagram,and evaluate its performance.The performance and clinical value of the prediction model were evaluated using subject work characteristic(ROC) curves,and the column-line diagram was internally validated.Results The results of univariate analysis showed that the maximum diameter of the tumour,the lymph node status,the short-axis length and volume of the largest lymph node in the lymph node group and the sum of the short-axis lengths of all the lymph nodes were associated with the occurrence of LNM,and the difference was statistically significant(P<0.05);multifactorial Logistic regression analysis suggested that the maximum diameter of the tumour was ≥15mm and the lymph node status was positive in the enhanced CT imaging examination,maximum lymph node short-axis length ≥7mm in the lymph node group,the sum of short-axis lengths of all lymph nodes ≥11mm,and the maximum lymph node short-axis volume ≥300mm3 in the lymph node group were the independent risk factors for gastric adenocarcinoma patients with LNM(+)(P<0.05);the area under the curve of the model was plotted using a ROC curve with an AUC=0.816(95%CI 0.810-0.939),with the sensitivity of 0.91 and the specificity of 0.86.Model validation was performed using the BOOTSTRAP method containing 500 resamples,and the results suggested that the differences between the fitted curves and the ideal curves of the constructed column-line graphical model were not significant,and the validity and reliability were good.Conclusion The radiomic nomogram prediction model based on enhanced CT imaging features and parameters can effectively predict preoperative lymph node metastasis in gastric adenocarcinoma,providing strong support for clinical decision-making in gastric adenocarcinoma patients.

Key words: gastric adenocarcinoma, lymph node metastasis, enhanced CT imaging, radiomics, predictive model

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