基于多维度CT影像特征的联合模型可提升继发性肺结核的诊断效能OA

Objective To evaluate the predictive value of CT imaging features in the diagnosis of secondary pulmonary tuberculosis(PTB)and to develop a diagnostic model based on multidimensional imaging features for assessing its performance.Methods A retrospective analysis was performed on clinical and CT imaging data from 246 patients with pulmonary shadows admitted to Guangzhou Chest Hospital from January 2023 to December 2024.The cohort comprised 160 patients with secondary PTB(PTB group)and 86 with non-tuberculous pulmonary conditions(non-PTB group).Binary logistic regression analysis was used to identify risk factors and construct a combined diagnostic model,and receiver operating characteristic(ROC)curve analysis was performed to evaluate model performance.Results Univariate analysis revealed that halo sign(OR=4.196,P=0.008),centrilobular nodules(OR=88.290,P=0.001),consolidation(OR=3.260,P=0.013),calcification(OR=2.547,P=0.031),and pleural thickening(OR=2.762,P=0.017)as risk factors for secondary PTB,while lesion distribution in the right middle lobe(OR=0.352,P=0.044)and left lingular segment(OR=0.190,P=0.003)were protective factors.Multivariate analysis identified seven independent predictors(halo sign,centrilobular nodules,consolidation,calcification,pleural thickening,right middle lobe involvement,left lingular segment involvement).The combined model achieved an area under the curve(AUC)of 0.881(95%CI:0.834-0.929),with a sensitivity of 87.5%and specificity of 79.1%,demonstrating superior diagnostic performance compared to any single imaging feature(DeLong test Z=3.89-10.50,all P<0.001).At the optimal threshold of 0.601,the positive and negative predictive values were 73.2%and 90.3%,respectively.Conclusion The combined diagnostic model based on multidimensional CT imaging features enhances the identification of secondary PTB and holds promise as a valuable tool for clinical auxiliary diagnosis.