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- Automatic Quantification of Interstitial Lung Disease From Chest Computed Tomography in Systemic SclerosisPublication . Carvalho, Alysson Roncally S.; Guimarães, Alan R.; Sztajnbok, Flávio R.; Rodrigues, Rosana Souza; Silva, Bruno Rangel Antunes; Lopes, Agnaldo José; Zin, Walter Araujo; Almeida, Isabel; França, ManuelaBackground: Interstitial lung disease (ILD) is a common complication in patients with systemic sclerosis (SSc), and its diagnosis contributes to early treatment decisions. Purposes: To quantify ILD associated with SSc (SSc-ILD) from chest CT images using an automatic quantification method based on the computation of the weight of interstitial lung opacities. Methods: Ninety-four patients with SSc underwent CT, forced vital capacity (FVC), and carbon monoxide diffusion capacity (DLCO) tests. Seventy-three healthy individuals without radiological evidence of lung disease served as controls. After lung and airway segmentation, the ratio between the weight of interstitial opacities [densities between -500 and +50 Hounsfield units (HU)] and the total lung weight (densities between -1,000 and +50 HU) was used as an ILD indicator (ILD[%] = 100 × [LW(-500 to +50HU)/LW(-1, 000 to +50HU)]). The cutoff of normality between controls and SSc was determined with a receiver operator characteristic curve. The severity of pulmonary involvement in SSc patients was also assessed by calculating Z scores of ILD relative to the average interstitial opacities in controls. Accordingly, SSc-ILD was classified as SSc Limited-ILD (Z score < 3) and SSc Extensive-ILD (Z score ≥ 3 or FVC < 70%). Results: Seventy-eight (83%) SSc patients were classified as presenting SSc-ILD (optimal ILD threshold of 23.4%, 0.83 sensitivity, 0.92 specificity, and 0.94 area under the receiver operator characteristic curve, 95% CI from 0.89 to 0.96, 0.93 positive predictive value, and 0.81 negative predictive value, p < 0.001) and exhibited radiological attenuations compatible with interstitial pneumonia dispersed in the lung parenchyma. Thirty-six (38%) patients were classified as SSc Extensive-ILD (ILD threshold ≥ 29.6% equivalent to a Z score ≥ 3) and 42 (45%) as SSc Limited-ILD. Eighteen (50%) patients with SSc Extensive-ILD presented FVC < 70%, being only five patients classified exclusively based on FVC. SSc Extensive-ILD also presented lower DLCO (57.9 ± 17.9% vs. 73.7 ± 19.8%; p < 0.001) and total lung volume (2,916 ± 674 vs. 4,286 ± 1,136, p < 0.001) compared with SSc Limited-ILD. Conclusion: The proposed method seems to provide an alternative to identify and quantify the extension of ILD in patients with SSc, mitigating the subjectivity of semiquantitative analyzes based on visual scores.
- COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided DiagnosisPublication . Carvalho, Alysson Roncally S.; Guimarães, Alan; Werberich, Gabriel Madeira; de Castro, Stephane Nery; Pinto, Joana Sofia F.; Schmitt, Willian Rebouças; França, Manuela; Bozza, Fernando Augusto; Guimarães, Bruno Leonardo da Silva; Zin, Walter Araujo; Rodrigues, Rosana SouzaPurpose: This work aims to develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement (PI) in COVID-19 as well as the radiological patterns referred to as lung opacities in chest computer tomography (CT). Methods: One hundred thirty subjects with COVID-19 pneumonia who underwent chest CT at hospital admission were retrospectively studied (141 sets of CT scan images). Eighty-eight healthy individuals without radiological evidence of acute lung disease served as controls. Two radiologists selected up to four regions of interest (ROI) per patient (totaling 1,475 ROIs) visually regarded as well-aerated regions (472), ground-glass opacity (GGO, 413), crazy paving and linear opacities (CP/LO, 340), and consolidation (250). After balancing with 250 ROIs for each class, the density quantiles (2.5, 25, 50, 75, and 97.5%) of 1,000 ROIs were used to train (700), validate (150), and test (150 ROIs) an artificial neural network (ANN) classifier (60 neurons in a single-hidden-layer architecture). Pulmonary involvement was defined as the sum of GGO, CP/LO, and consolidation volumes divided by total lung volume (TLV), and the cutoff of normality between controls and COVID-19 patients was determined with a receiver operator characteristic (ROC) curve. The severity of pulmonary involvement in COVID-19 patients was also assessed by calculating Z scores relative to the average volume of parenchymal opacities in controls. Thus, COVID-19 cases were classified as mild (
- CT volumetry performs better than nuclear renography in predicting estimated renal function one year after living donationPublication . Almeida, Manuela; Pereira, Pedro R.; Ramos, Miguel; Nunes-Carneiro, Diogo; Mandaleno, Mariana; Silva, Filipa; Pedroso, Sofia; França, Manuela; Martins, La Salete; J, MalheiroThe evaluation of split renal function (SRF) is a critical issue in living kidney donations and can be evaluated using nuclear renography (NR) or computerized tomography (CT), with unclear comparative advantages. We conducted this retrospective study in 193 donors to examine the correlation of SRF assessed by NR and CT volumetry and compared their ability to predict remaining donor renal function at 1 year, through multiple approaches. A weak correlation between imaging techniques for evaluating the percentage of the remaining kidney volume was found in the global cohort, with an R2 = 0.15. However, the Bland-Altman plot showed an acceptable agreement (95% of the difference between techniques falling within - 8.51 to 6.11%). The predicted and observed eGFR one year after donation were calculated using the CKD-EPI, and CG/BSA equations. CT volume showed a better correlation than NR for both formulas (adjusted R2 of 0.42. and 0.61 vs 0.37 and 0.61 for CKD-EPI and CG/ BSA equations, respectively). In non-nested modeling tests, CT volumetry was significantly superior to NR for both equations. CT volumetry performed better than NR in predicting the estimated renal function of living donors at 1-year, independently from the eGFR equation.