Computed Tomography Characteristics Predictive for Radial EBUS-Miniprobe-Guided Diagnosis of Pulmonary Lesions. Issue 3 (March 2015) - Guvenc, Canan; Yserbyt, Jonas; Testelmans, Dries; Zanca, Federica; Carbonez, An; Ninane, Vincent; Wever, Walter De; Dooms, Christophe
Abstract : Introduction: Radial endobronchial ultrasonography miniprobe (rEBUS-MP) is a technique that has increased the diagnostic yield of bronchoscopic occult pulmonary lesions. The purpose of this study was to identify computed tomography (CT) characteristics affecting the success rate of rEBUS-MP in the evaluation of these pulmonary lesions. Methods: Our study encompassed a retrospective review of all consecutive patients who underwent a rEBUS-MP examination between January 2011 and December 2013. CT characteristics including lesion size, lesion location, and bronchus sign were analyzed against two defined outcomes (visualization yield and diagnostic yield). Univariate analysis was employed to examine the individual effects of each CT parameter on visualization yield and diagnostic yield. Multivariate logistic regression was performed to identify significant predictors of diagnostic yield. Results: Seven hundred and sixty lesions (760 patients) were included. The mean ± SD longest lesion diameter was 43 ± 2 mm. rEBUS-MP could visualize 83% and a definitive diagnosis was established in 62%. In a multivariate analysis, longest lesion diameter greater than 20 mm (odds ratio [OR]: 1.97 and p = 0.036), distance lesion to secondary or tertiary carina greater than 40 mm (OR: 0.60 and p =0.016), and lobar segment (1, 3, or 6, respectively) were determined to be significant factors predicting diagnostic yield. Bronchus sign was the only parameter that indirectly influenced the diagnostic yield through enhancing visualization yield ( p < 0.001). Conclusion: Multivariate analysis revealed that lesion size, distance to carina, and segment were predictors of diagnostic yield. The presence of a bronchus sign substantially increased the diagnostic yield through the visualization yield.