RESUMO
Pulmonary vasculitis identifies a heterogeneous group of diseases characterized by inflammation, damage and necrosis of the wall of pulmonary vessels. The most common approach to classify vasculitis is according to etiology, therefore dividing them into primary and secondary, with a further sub-classification of primary vasculitis based on the size of the affected vessels (large, medium, and small). Pulmonary involvement is frequently observed in patients with systemic vasculitis and radiological presentation is not pathognomonic, but may vary between diseases. The main findings using high-resolution computed tomography (HRCT) include small vessel wall thickening, nodular lesions, cavitary lesions, reticular opacities, ground-glass opacities (GGO), consolidations, interlobular septal thickening, tracheobronchial stenosis, and aneurysmal dilatation of pulmonary arteries, with or without pleural effusion. Radiological diagnosis alone is difficult since signs and symptoms of lung vessel involvement are often non-specific and might overlap with other conditions such as infections, connective tissue diseases and neoplasms. Therefore, the aim of this review is to describe the most common radiological features of lung involvement in pulmonary vasculitis so that, alongside detailed clinical history and laboratory tests, a prompt diagnosis can be performed.
RESUMO
BACKGROUND: Biparametric MRI (bpMRI) has an important role in the diagnosis of prostate cancer (PCa), by reducing the cost and duration of the procedure and adverse reactions. We assess the additional benefit of the ADC map in detecting prostate cancer (PCa). Additionally, we examine whether the ADC value correlates with the presence of clinically significant tumors (csPCa). METHODS: 104 peripheral lesions classified as PI-RADS v2.1 score 3 or 3+1 at the mpMRI underwent transperineal MRI/US fusion-guided targeted biopsy. RESULTS: The lesions were classified as PI-RADS 3 or 3+1; at histopathology, 30 were adenocarcinomas, 21 of which were classified as csPCa. The ADC threshold that maximized the Youden index in order to predict the presence of a tumor was 1103 (95% CI (990, 1243)), with a sensitivity of 0.8 and a specificity of 0.59; both values were greater than those found using the contrast medium, which were 0.5 and 0.54, respectively. Similar results were also found with csPCa, where the optimal ADC threshold was 1096 (95% CI (988, 1096)), with a sensitivity of 0.86 and specificity of 0.59, compared to 0.49 and 0.59 observed in the mpMRI. CONCLUSIONS: Our study confirms the possible use of a quantitative parameter (ADC value) in the risk stratification of csPCa, by reducing the number of biopsies and, therefore, the number of unwarranted diagnoses of PCa and the risk of overtreatment.