CT联合炎症指标鉴别肺结节良恶性的临床研究

CT Combined with Inflammatory Markers in Differentiating Benign from Malignant Pulmonary Nodules: A Clinical Study

摘要: 目的：比较良恶性肺结节患者嗜酸性粒细胞计数与SII、NLR、PLR、LMR等炎症标志物的表达差异，并探讨血液学参数联合CT影像学征象对肺结节性质判断的诊断价值。方法：回顾性收集2019年1月至2025年5月经病理证实的268例肺结节患者资料，分析各指标差异并构建联合预测模型。结果：嗜酸性粒细胞计数及SII为恶性肺结节的独立预测因子；整合SII、嗜酸性粒细胞计数与CT特征的多指标联合模型诊断恶性肺结节的AUC为0.804 (95% CI: 0.750~0.858)，敏感度74.2%、特异度76.7%，优于单纯影像学模型的AUC为0.733 (95% CI: 0.672~0.793)。结论：嗜酸性粒细胞计数及SII联合CT影像学特征可提高恶性肺结节的鉴别诊断效能，具有一定的临床参考价值。

Abstract: Objective: To compare the expression differences of eosinophil count and inflammatory markers including SII, NLR, PLR, and LMR between patients with benign and malignant pulmonary nodules, and to investigate the diagnostic value of hematological parameters combined with CT imaging features in identifying the nature of pulmonary nodules. Methods: A retrospective analysis was conducted on 268 patients with pathologically confirmed pulmonary nodules who were treated at our hospital between January 2019 and May 2025. The differences in various indicators between the two groups were analyzed, and a combined predictive model was constructed. Results: Eosinophil count and SII were identified as independent predictors of malignant pulmonary nodules. The multi-indicator combined model integrating SII, eosinophil count, and CT features demonstrated an AUC of 0.804 (95% CI: 0.750~0.858) for diagnosing malignant pulmonary nodules, with a sensitivity of 74.2% and specificity of 76.7%, which was superior to the imaging-only model with an AUC of 0.733 (95% CI: 0.672~0.793). Conclusion: The combination of eosinophil count, SII, and CT imaging features can improve the diagnostic efficacy for distinguishing malignant pulmonary nodules and holds certain clinical reference value.