孤立性肺结节64排螺旋CT征象良恶性鉴别诊断的Logistic回归分析

Logistic regression analysis of CT characteristics of solitary pulmonary nodules

目的运用Logistic回归分析与孤立性肺结节（solitary pulmonary nodule，SPN）良恶性相关的重要cT征象，以提高对SPN的鉴别诊断能力。方法收集经手术或穿刺病理证实的SPN186例（原发性肺癌125例，良性结节61例），采用随机法从中选择100例作为训练样本，分为良恶性两组，回顾分析SPN的cT表现包括（部位、大小、形态边缘、内部结构、结节与周围血管及胸膜的关系等），运用x：检验分析两组间cT征象的差别，然后再进行Logistic回归分析，运用SPSS16．0统计软件对数据进行分析。结果100例SPN中恶性72例，良性28例，经xz检验良恶性SPN的分布部位、大小、支气管征、空泡征及血管集束征差异无统计学意义；Logistic回归分析提示毛刺征、胸膜凹陷征是鉴别恶性SPN的较重要cT征象，诊断恶性的优势比及95％可信区间分别为38．529（6．677～222．336）、11．963（1．904～75．183）；回归判别的诊断准确率、敏感度及特异度分别为：95％、95．8％、92．9％。结论通过Logistic回归分析，CT征象：毛刺征、胸膜凹陷征是提示恶性SPN的重要征象。

Objective To determine the CT characteristics of benign and malignant solitary pulmonary nodules （SPN） using logistic regression analysis. Methods Of 186 histologically confirmed SPN including 125 primary lung cancer and 61 benign nodules, 72 malignant and 28 benign nodules were included in the study using completely randomized method. CT features of benign and malignant SPN including location, size, border, internal structure, relationship with surrounding blood vessels and pleura were compared using ~2 test and logistic regression analysis. Results There was no significant difference in the lesion location, size, air bronchogram, air alveologram, or hypervascularity between the benign and malignant SPN by ~2 test. Using logistic regression analysis, spiculation and pleural adhesion allowed diagnosis of malignant SPN with odds ratios （95% confidence intervals ） of 38.529 （6.677-222.336） and 11.963 （1.904-75.183）, respectively. The diagnostic accuracy （95.0%）, sensitivity （95.8%） and specificity （92.9%） of logistic regression analysis were high. Conclusions Spiculation and pleural adhesion are CT characteristics of malignant SPN using logistic regression analysis.