Tracheobronchial branching anomalies in adult patients

Aim: To determine the frequency of tracheobronchial branching anomalies in adult patients with thorax CT. Material and method: Thorax CT examinations of 1000 adult patients were retrospectively evaluated. Axial, coronal, and sagittal images in lung window, minimum intensity projection (MinIP), 3-Dimensional (3D), curved MPR images were analyzed on a workstation. Frequencies, localizations and types of the tracheal diverticulum, tracheal bronchus, accessory cardiac bronchus, frequencies of the bilateral upper lobes and right middle lobe branching anomalies, frequencies and types of subsuperior and suprasuperior bronchus in both lung lower lobes, right and left isomerism, situs inversus and bridging bronchus were evaluated. Numerical variables were shown as mean ± standard deviation and minimum-maximum values, while categorical variables in numbers and percentages. Anomaly frequencies in female and male patients were compared using the Chi-square test. p<0.05 was accepted as statistically significant. Results: Tracheobronchial branching anomalies were detected in 102 of the 1000 patients (491 females, 509 males). A single anomaly was observed in 92 cases, and double anomalies were detected in 5 cases. Preeparterial bronchi in 8 patients, left prehyparterial bronchi in 2 patients, right suprasuperior bronchi in 9 patients, right subsuperior bronchi in 33 patients, left subsuperior bronchi in 38 patients, and accessory cardiac bronchi in 7 patients were observed. Right tracheal bronchus, posteparterial bronchus, right tracheal diverticulum, upwardly displaced MLB, and situs inversus were found in one patient each. Out of the detected congenital tracheal bronchial anomalies, 11 were displaced and 82 were supernumerary. Conclusion: CT is a successful imaging method in the evaluation of tracheobronchial branching anomalies. Basic anatomical principles such as level of the carina and relationship of the upper lobe bronchi with the pulmonary arteries should be known in order not to overlook these anomalies. Using lobe-based classification will facilitate the detection and classification of these anomalies.