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Hepatopulmonary syndrome has low prevalence of pulmonary vascular abnormalities on chest computed tomography.

ABSTRACT: PURPOSE:Hepatopulmonary syndrome (HPS) is defined as an arterial oxygenation defect induced by intrapulmonary vascular dilatations associated with hepatic disease. This study aimed to assess the prevalence of type 1 and 2 pulmonary vascular abnormalities on chest computed tomography (CT) in patients with cirrhosis and HPS and to characterize intra- and interobserver reliability. MATERIALS AND METHODS:Two thoracic radiologists retrospectively evaluated chest CT scans from 38 cirrhosis patients with HPS. They classified the pulmonary vascular abnormalities as type 1 (multiple dilated distal pulmonary arteries), type 2(nodular dilatation or individual pulmonary arterial malformation), or absence of abnormality. Furthermore, they measured the diameters of the central pulmonary arteries and subsegmental pulmonary arteries and bronchi. We analyzed the prevalence, intraobserver reliability, and interobserver reliability of abnormal CT findings related to HPS, and the correlation of these findings with partial arterial oxygen pressure (PaO2). RESULTS:The overall prevalence of pulmonary vascular abnormalities was 28.9% (95% confidence intervals: 15.4%, 45.9%). Moreover, 26.3% of patients had type 1 abnormality (13.4%, 43.1%) and 2.6% of patients had type 2 abnormality (0.0%, 13.8%). The intraobserver reliability kappa value was 0.666 (0.40, 0.91) and the interobserver kappa value was 0.443 (0.12, 0.77). There was no correlation between pulmonary vascular abnormalities on CT and PaO2 values. CONCLUSIONS:The prevalence of pulmonary vascular abnormalities on chest CT of patients with cirrhosis and HPS is low and not correlated with PaO2. These findings question the usefulness of chest CT for the evaluation of patients with cirrhosis and HPS.

SUBMITTER: Folador L

PROVIDER: S-EPMC6799931 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeTo compare the diagnostic performance and interobserver agreement of three reporting systems for computed tomography findings in coronavirus disease 2019 (COVID-19), namely the COVID-19 Reporting and Data System (CO-RADS), COVID-19 Imaging Reporting and Data System (COVID-RADS), and Radiological Society of North America (RSNA) expert consensus statement, in a low COVID-19 prevalence area.MethodThis institutional review board approval single-institutional retrospective study included 154 hospitalized patients between April 1 and May 21, 2020; 26 (16.9 %; 63.2 ± 14.1 years, 21 men) and 128 (65.7 ± 16.4 years, 87 men) patients were diagnosed with and without COVID-19 according to reverse transcription-polymerase chain reaction results, respectively. Written informed consent was waived due to the retrospective nature of the study. Six radiologists independently classified chest computed tomography images according to each reporting system. The area under receiver operating characteristic curves, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and interobserver agreements were calculated and compared across the systems using paired t-test and kappa analysis.ResultsMean area under receiver operating characteristic curves were as follows: CO-RADS, 0.89 (95 % confidence interval [CI], 0.87-0.90); COVID-RADS, 0.78 (0.75-0.80); and RSNA expert consensus statement, 0.88 (0.86-0.90). Average kappa values across observers were 0.52 (95 % CI: 0.45-0.60), 0.51 (0.41-0.61), and 0.57 (0.49-0.64) for CO-RADS, COVID-RADS, and RSNA expert consensus statement, respectively. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were the highest at 0.71, 0.53, 0.72, 0.96, and 0.56 in the CO-RADS; 0.56, 0.31, 0.54, 0.95, and 0.35 in the COVID-RADS; 0.83, 0.49, 0.61, 0.96, and 0.55 in the RSNA expert consensus statement, respectively.ConclusionsThe CO-RADS exhibited the highest specificity, positive predictive value, which are especially important in a low-prevalence population, while maintaining high accuracy and negative predictive value, demonstrating the best performance in a low-prevalence population.

Project description:ObjectiveWe aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images.Materials and methodsThin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31-89 years) between January 2017 and May 2017 were included in the study, of which 150 cases had extensive lung parenchymal disease involving more than 40% of the parenchymal area. Parenchymal diseases included interstitial lung disease (ILD), emphysema, nontuberculous mycobacterial lung disease, tuberculous destroyed lung, pneumonia, lung cancer, and other diseases. Five experienced radiologists manually drew the margin of the lungs, slice by slice, on CT images. The dataset used to develop the network consisted of 157 cases for training, 20 cases for development, and 26 cases for internal validation. Two-dimensional (2D) U-Net and three-dimensional (3D) U-Net models were used for the task. The network was trained to segment the lung parenchyma as a whole and segment the right and left lung separately. The University Hospitals of Geneva ILD dataset, which contained high-resolution CT images of ILD, was used for external validation.ResultsThe Dice similarity coefficients for internal validation were 99.6 ± 0.3% (2D U-Net whole lung model), 99.5 ± 0.3% (2D U-Net separate lung model), 99.4 ± 0.5% (3D U-Net whole lung model), and 99.4 ± 0.5% (3D U-Net separate lung model). The Dice similarity coefficients for the external validation dataset were 98.4 ± 1.0% (2D U-Net whole lung model) and 98.4 ± 1.0% (2D U-Net separate lung model). In 31 cases, where the extent of ILD was larger than 75% of the lung parenchymal area, the Dice similarity coefficients were 97.9 ± 1.3% (2D U-Net whole lung model) and 98.0 ± 1.2% (2D U-Net separate lung model).ConclusionThe deep neural network achieved excellent performance in automatically delineating the boundaries of lung parenchyma with extensive pathological conditions on non-contrast chest CT images.

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Hepatopulmonary syndrome has low prevalence of pulmonary vascular abnormalities on chest computed tomography.

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