超声特征评价周围型肺结核的Logistic回归模型的建立

目的 探讨灰阶超声(GSU)、超声造影(CEUS)及超声引导下经皮肺穿刺在周围型肺结核诊断中的应用价值,建立周围型肺结核的Logistic回归诊断模型.方法 回顾性分析2018年1月至2019年12月在浙江大学医学院附属杭州市胸科医院就诊并经病理及Gene X-pert MTB/RIF检查证实的周围型肺结节患者61例,...

Establishment of a Logistic regression model for evaluation of peripheral pulmonary tuberculosis by ultrasonic characteristics

Objective To explore the application of gray-scale ultrasound(GSU),contrastenhanced ultrasound(CEUS),and ultrasound-guided percutaneous lung puncture in the diagnosis of peripheral pulmonary tuberculosis,and establish a Logistic regression model for the evaluation of peripheral pulmonary tuberculosis.Methods A retrospective analysis was performed on 61 patients with 61 peripheral pulmonary nodules confirmed by pathology and Gene X-pert MTB/RIF examinations who were admitted to the Affiliated Hangzhou Chest Hospital,Zhejiang University School of Medicine from January 2018 to December 2019.The patients were divided into either a tuberculosis group(43 cases)or anon-tuberculous group(18 cases).All patients underwent GSU,CEUS,and ultrasound-guided percutaneous lung puncture,and the lesion morphology,internal echo,bronchial signs,contrast arrival time(AT),time difference between arrival of the contrast agent to the lesion and adjacent lung tissue,perfusion pattern,enhancement degree,biopsy tissue integrity,and other ultrasound characteristics were recorded to perform univariate analysis.Parameters with significance in the univariate analysis were then incorporated into Logistic multivariate regression analysis to establish a Logistic regression diagnosis model.ROC curve analysis was performed tocalculate the performance of this model to predict peripheral tuberculosis.Results Univariate analysis showed that lesion morphology,AT,time difference between arrival of the contrast agent to the lesion and adjacent lung tissue,and perfusion pattern differed significantly between the tuberculosis group and the nontuberculosis group(χ²=6.811,5.770,5.960,5.728,P<0.05).Two-category logistic regression analysis showed that four variables,namely,age,nodule morphology,contrast medium arrival time difference,and perfusion pattern,entered the Logistic regression model.The regression equation is Logit(P)=-3.565+1.868 X2+2.469 X3+1.734 X7+2.650 X8.The area under the ROC curve of the regression model for predicting peripheral pulmonary tuberculosis was 0.911.With Logit(P)≥0.50 as the cutoff value,its prediction accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 87.3%,94.6%,72.2%,87.5%,and 86.7%,respectively.Conclusion The Logistic regression model,which incorporates the age of patients with peripheral pulmonary nodules,the shape of the lesion,the time difference between the arrival of the contrast agent to the lesion and the adjacent lung tissue,and the perfusion pattern,is helpful for the diagnosis of peripheral pulmonary tuberculosis.