增强CT在术前评估非小细胞肺癌N1淋巴结转移中的诊断价值

Diagnostic Value of Enhanced CT in Preoperative Evaluation of N1 Lymph Node Metastasis in Non-Small Cell Lung Cancer

摘要: 目的：评价术前增强CT对非小细胞肺癌N1淋巴结转移的诊断价值。方法：选取2020年11月至2024年11月我院收治的160例经病理证实的NSCLC患者，根据有无N1淋巴结转移分为分N1 (+)组、N1 (−)组，分析比较两组患者临床、病理及影像特征的差异，采用Logistic回归及Lasso回归分析影响N1淋巴结转移的因素，并构建模型，采用受试者工作特征(ROC)曲线分析Logistic回归模型及Lasso回归模型对N1淋巴结转移的诊断价值；进一步构建诊断N1淋巴结转移的列线图并绘制校准曲线图进行内部验证。结果：多因素Logistic回归分析结果示，淋巴结短径和边界、病理肿瘤直径、脉管侵犯及病灶与支气管位置对N1淋巴结转移有较显著影响，其模型ROC曲线分析结果显示AUC值为0.929 (95% CI: 0.892~0.967)，灵敏度为88.89%，特异度为83.33%，Lasso回归模型的AUC为0.9416 (95% CI: 0.9084~0.9749)，与Logistic回归模型相比无显著差异(DeLong检验p = 0.10)，提示Logistic模型诊断效能较好，总体效能优于单一短径标准(AUC值为0.864，灵敏度为76.3%，特异度为96.4% (95% CI: 0.801~0.926))。结论：基于淋巴结短径和边界、病理肿瘤直径、脉管侵犯及病灶与支气管位置构建的Logistic回归模型，相较于单一淋巴结短径诊断效能更好。

Abstract: Objective: To investigate the diagnostic value of enhanced CT in preoperative assessment of N1 lymph node metastasis in non-small cell lung cancer (NSCLC). Methods: A total of 160 patients with pathologically confirmed NSCLC admitted to our hospital from November 2020 to November 2024 were selected and divided into N1 (+) and N1 (−) groups based on the presence or absence of N1 lymph node metastasis. The differences in clinical, pathological, and imaging characteristics were analyzed and compared between two groups of patients. Logistic regression analysis and Lasso regression were used to identify factors influencing N1 lymph node metastasis, and models were constructed. The diagnostic value of the Logistic regression model and Lasso regression model for N1 lymph node metastasis was evaluated using receiver operating characteristic (ROC) curve analysis. Additionally, a nomogram for diagnosing N1 lymph node metastasis was constructed, and a calibration curve was plotted for internal validation. Results: Multivariate Logistic regression analysis revealed that lymph node short axis and margin, pathological tumor diameter, vascular invasion, and lesion location relative to the bronchus were independent factors influencing N1 lymph node metastasis. The ROC curve analysis of the Logistic regression model showed an AUC value of 0.929 (95% CI: 0.892~0.967), sensitivity of 88.89%, and specificity of 83.33%, The AUC of the LASSO regression model was 0.9416 (95% CI: 0.9084~0.9749), showing no significant difference compared to the Logistic regression model (DeLong test p = 0.10), indicating that the Logistic model has better diagnostic performance. The overall performance was superior to the single short diameter standard (AUC value of 0.864, sensitivity of 76.3%, specificity of 96.4% (95% CI: 0.801~0.926)). Conclusion: A Logistic regression model constructed based on lymph node short axis and margin, pathological tumor diameter, vascular invasion, and lesion location relative to the bronchus demonstrated better diagnostic performance than lymph node short axis alone.