摘要
目的:探讨二元逻辑回归分析在多层螺旋CT(MSCT)鉴别诊断局灶性机化性肺炎(FOP)及周围型肺癌(PLC)中的价值。方法:选取2016年3月—2022年5月中部战区总医院经病理证实的53例局灶性机化性肺炎患者作为研究组,61例周围型肺癌患者作为对照组,分析比较两组病变CT征象,对差异有统计学意义的定性征象构建回归模型,通过受试者工作特征(ROC)曲线计算曲线下面积、灵敏度、特异度。结果:二元多因素Logistic回归分析显示边界不清、长毛刺、短毛刺、空气支气管征是预测区分OP与PLC的独立影响因素;联合预测模型ROC曲线下面积为0.945,灵敏度为79.20%,特异度为98.40%。结论:边界不清、长毛刺、短毛刺、空气支气管征象有助于FOP和PLC的鉴别诊断,联合预测模型可提高鉴别二者的诊断效能,为临床提供重要辅助信息。
Objective To explore the value of binary logistic regression analysis in the differential diagnosis of focal organizing pneumonia(FOP)and peripheral lung cancer(PLC)using multi-slice spiral CT(MSCT).Methods 53 patients with focal organizing pneumonia confirmed by pathology were selected as the study group,and 61 patients with peripheral lung cancer were selected as the control group.The CT signs of the two groups were analyzed and compared,and a regression model was constructed by using statistically significant features.The area under the curve,sensitivity,and specificity were calculated by receiver operating characteristic(ROC)curves.Results Binary multivariate logistic regression analysis showed that unclear boundaries,long thorn,short thorn,and air bronchogram were independent predictive factors for distinguishing FOP from PLC;The area under the ROC curve of the model is 0.945,with a sensitivity of 79.20%and a specificity of 98.40%.Conclusion Unclear boundaries,long thorn,short thorn,and air bronchogram signs are helpful for the differential diagnosis of FOP and PLC.The predictive model can improve the diagnostic efficiency and provide important auxiliary information for clinical practice.
作者
魏微微
邹佳妮
WEI Weiwei;ZOU Jiani(Department of Radiology,Central Theater General Hospital,Wuhan,Hubei 437000,China)
出处
《影像研究与医学应用》
2024年第7期28-30,34,共4页
Journal of Imaging Research and Medical Applications
关键词
多层螺旋CT
局灶性机化性肺炎
周围型肺癌
灵敏度
特异度
Multi-slice spiral CT
Focal organizing pneumonia
Peripheral lung cancer
Sensitivity
Specificity
