Assessment of lung involvement in sarcoidosis – the use of an open-source software to quantify data from computed tomography.

Tomasz Urbankowski, Lucyna Opoka, Paweł Wojtan, Rafał Krenke


Computed tomography (CT) plays a pivotal role in the initial evaluation of patients suspected of sarcoidosis. Although it has significant limitations associated with radiation exposure, CT scanning is also occasionally used to follow-up patients with sarcoidosis. Hitherto, no widely accepted method of quantitative assessment of pulmonary involvement in sarcoidosis has been established. The aims of the study were as follows: (1) to assess the utility of the open-source, free of charge DICOM Viewer software in quantitative analysis of pulmonary involvement in sarcoidosis; (2) to compare the parameters of quantitative CT analysis with the results of pulmonary function tests (PFTs). We included contrast-enhanced thorax CT examinations of 80 patients with sarcoidosis. Post-processing analysis of CT data was carried out using OsiriX Lite software (Pixmeo, Switzerland). Following densitometric parameters were measured: CT-derived lung volume (CT-LV), mean lung attenuation (MLA), kurtosis, skewness and standard deviation of lung radiodensity (SDLR).  Kurtosis was significantly lower in patients with lung fibrosis comparing to those with mediastinal and/or hilar lymphadenopathy (MHL) and pulmonary involvement  (median 1.49 vs 1.93). Furthermore, SDLR was significantly higher in patients with lung fibrosis comparing to those with isolated MHL and MHL with pulmonary involvement (median 163.6 vs 137.4). Also, significant correlations between densitometric parameters and the results of PFTs were demonstrated, including correlation between CT-LV and TLC (R=0.7). Our study showed that post-processing of the CT data with the use of Osirix Lite DICOM Viewer might be a valuable method of quantitative analysis of pulmonary involvement in sarcoidosis.



Sarcoidosis, Pulmonary; Lung Diseases, Interstitial; Multidetector Computed Tomography; Image Processing, Computer-Assisted

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ISSN: 2532-179X