Abstract:

Objective To analyze the characteristics of CT signs and dynamic changes of early-stage tuberculous pleurisy, and to improve the accuracy of diagnosis.Methods The imaging data of 38 patients diagnosed as tuberculous pleurisy in Beijing Chest Hospital affiliated to Capital Medical University from March 2015 to February 2017 were collected. The subjects included 16 men and 22 women. The age was ranged 21-61 years, and the average age was (35.74±11.92) years. All the patients were treated within 7-10 days of the first onset. The CT image data of the early stage of the disease were analyzed, and the imaging features and signs were extracted.Results Among the 38 subjects, there were 34 cases with unilateral pleural lesions (including 18 in the left pleura and 16 in the right pleural) and 4 with bilateral lesions; there were 14 patients involving the mediastinal pleura (including 6 in the left mediastinal pleural and 8 in the right mediastinal pleura), and 34 involving the interlobular pleura. There were 38 cases showing encapsulated pleural effusion, 24 cases showing pleural septal thickening, and 24 cases showing pleural stripe. During the dynamic follow-up period, the thickening of the pleura was reduced in 37 patients. In 1 case, the thickening degree was aggravated at 6 months, and the thickest was about 1.1 cm. The thickening of pleura was rechecked at 12 months, and it was found to become thinned. During follow-up, the pleural effusion of all patients showed decreased absorption, the pleural septal thickening showed decreased absorption, and the pleural stripe was gradually reduced and thinned. Another 22 patients had pleural tuberculoma during follow-up.Conclusion In the early stage of the disease, unilateral pleural thickening and unsmoothness, interlobular fissure involvement, multiple miliary and micronodules, encapsulated pleural effusion, pleural septal thickening and stripe were found in CT images. We may consider tuberculous pleurisy.

Key words: Tuberculosis, pleural, Tomography, X-ray computed, Disease attributes, Diagnostic imaging