Interobserver agreement of interstitial lung fibrosis Reporting and Data System (ILF-RADS) at high-resolution CT.

PURPOSE: To assess the interobserver agreement of interstitial lung fibrosis Reporting and Data System (ILF-RADS) in interpretation and categorization of interstitial lung disease (ILD) at high-resolution CT (HRCT). METHODS: Retrospective analysis was performed on 65 consecutive patients (36 male and 29 female), median age 53 years, who were referred to the Radiology Department, Mansoura University, in the period from July 2016 to February 2020. They were expected clinically to have diffuse lung disease and underwent HRCT of the chest. Patients had some investigations like serology, and when required surgical lung biopsy. Image analysis was done by two independent and blinded readers for the pulmonary and extra-pulmonary finding of ILF-RADS. The pulmonary findings were 13 items and extrapulmonary findings were 5 items. The score was 5 types according to ILF-RADS: ILF-RADS 0 (incomplete assessment), ILF-RADS 1 (typical UIP), ILF-RADS 2 (probable UIP), ILF-RADS 3 (indeterminate UIP), ILF-RADS 4 (CT features most consistent with non-UIP diagnosis). RESULTS: There was an excellent interobserver agreement of both reviewers for overall ILF-RADS (K = 0.88, P = 0.001) with 95.4% agreement. There was an excellent interobserver agreement for overall pulmonary findings (K = 0.901, 95% CI = 0.877-0.926, P = 0.001), excellent interobserver agreement for seven items including lung volume, traction bronchiectasis, nodules, cysts, consolidation, emphysema, and complications and moderate interobserver agreement for six items including reticulations, honeycomb, ground glass, mosaic attenuation, and axial and zonal distribution. There was excellent interobserver agreement for overall extra-pulmonary findings (K = 0.902, 95% CI = 0.852-0.952, P = 0.001), excellent interobserver agreement for four items including mediastinum, pleura, visible abdomen, and soft tissue and bone and moderate interobserver agreement for trachea and main bronchi. There was excellent interobserver agreement for ILF-RADS score: ILF-RADS 1 (K = 0.84, P = 0.001), ILF-RADS 3 (K = 0.881, P = 0.001), and ILF-RADS 4 (K = 0.878, 95% CI = 0.743-1.0, P = 0.001) and moderate interobserver agreement for ILF-RADS 2 (K = 0.784, P = 0.001). CONCLUSION: ILF-RADS is a reliable reporting system which can be routinely performed for standard interpretation of ILD.

Interstitial Lung Fibrosis Imaging Reporting and Data System: What Radiologist Wants to Know?

The aim of this work is to review interstitial lung fibrosis Imaging Reporting and Data System (ILF-RADS) that was designed for reporting of interstitial lung fibrosis (ILF). Findings include pulmonary and extrapulmonary findings and is subsequently designed into 4 categories. Pulmonary findings included lung volume, reticulations, traction bronchiectasis, honeycomb, nodules, cysts, ground glass, consolidation, mosaic attenuation and emphysema, and distribution of pulmonary lesions; axial (central, peripheral and diffuse), and zonal distribution (upper, middle, and lower zones). Complications in the form of acute infection, acute exacerbation, and malignancy were also assessed. Extrapulmonary findings included mediastinal, pleural, tracheal, and bone or soft tissue lesions. The lexicon of usual interstitial pneumonia (UIP) was classified into 4 categories designated as belonging in 1 of 4 categories. Lexicon of ILF-RADS-1 (typical UIP), ILF-RADS-2 (possible UIP), ILF-RADS-3 (indeterminate for UIP), and ILF-RADS-4 (inconsistent with UIP).

Role of diffusion-weighted magnetic resonance imaging in the differentiation of benign and malignant pulmonary lesions.

PURPOSE: To evaluate the role of magnetic resonance (MRI) diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) measurement of solid and cystic pulmonary masses in differentiating benign from malignant lesions. MATERIAL AND METHODS: The study included 41 patients with pulmonary masses, who underwent conventional MRI and DWI (b value 0, 500, and 1000 s/mm²) examinations with 1.5-T MRI. The diffusion signal and the mean ADC values of the solid and cystic lesions were obtained. Statistical analyses were performed with the Mann-Whitney U test (z), Pearson's chi-square test, and receiver operating characteristic (ROC) analysis. RESULTS: Thirty-three lesions were malignant, and eight lesions were benign. The malignant masses showed significantly higher signal intensity on DWI than benign masses (p = 0.006), and the mean ADC value of malignant solid lesions was significantly lower than that of benign lesions (p = 0.02). By ROC analysis, an ADC cut-off value of 1.4 × 10-3 mm2/s was considered the threshold value, and the sensitivity and specificity were 93.8% and 75%, respectively. There was no significant difference between the ADC value of the cystic parts inside the benign and the malignant lesions. CONCLUSIONS: Diffusion-weighted MRI and measurement of ADC value can significantly differentiate between solid benign and malignant pulmonary masses.

The added value of brain MR spectroscopy in children with Crigler-Najjar syndrome type-I: correlation with demographic, neurodevelopmental, and laboratory findings.

OBJECTIVE: The aim of this study is to demonstrate the role of proton magnetic resonance spectroscopy (1H-MRS) in the detection of brain microstructural changes in patients with Crigler-Najjar syndrome type-I (CNs-I), and its correlation with demographic, neurodevelopmental and laboratory findings. METHODS: Prospective study was conducted on 25 children with CNs-I and 25 age and sex-matched children, who served as control. They underwent multivoxel 1H-MRS of basal ganglion at echo time 135-144 ms. N-acetyl aspartate/Creatine (NAA/Cr) and Choline (Ch)/Cr were calculated and correlated with demographic, clinical, and laboratory findings of patients with CNs-I. RESULTS: There was a significant difference in NAA/Cr and Ch/Cr between patients and controls. The cut-off value for NAA/Cr and Ch/Cr used to differentiate patients from controls were 1.8 and 1.2 with an area under the curve (AUC) of 0.91 and 0.84 respectively. There was a significant difference in MRS ratios between patients with neurodevelopmental delay (NDD) and patients without NDD. The cut-off values for NAA/Cr and Ch/Cr used to differentiate between patients with NDD and patients without NDD were 1.47 and 0.99, with AUC of 0.87 and 0.8 respectively. The NAA/Cr and Ch/Cr were well correlated with family history (p = 0.006 and p < 0.001) respectively, consanguinity (p < 0.001 and p = 0.001), neurodevelopmental delay (p = 0.001 and p = 0.004), serum bilirubin level (r = -0.77, p < 0.001), (r = -0.49, p = 0.014), phototherapy (p < 0.001 and p = 0.32), blood transfusion (p < 0.001 and p = 0.001) respectively. CONCLUSION: 1H-MRS can be a useful tool in the detection of neurological changes in patients with CNs-I; NAA/Cr and Ch/Cr parameters are well correlated with demographic, clinical, and laboratory findings. ADVANCES IN KNOWLEDGE: Our study is the first report on using MRS in assessing neurological manifestations in CNs. 1H-MRS can be a useful tool in the detection of neurological changes in patients with CNs-I.

Pre- and post-therapeutic evaluation of liver and spleen in type I and type III Gaucher's disease using diffusion tensor imaging.

PURPOSE: To assess the role of diffusion tensor imaging in assessing liver and splenic parenchymal infiltration in Gaucher's disease (G.D.) type I and III before and after therapy. METHODS: A prospective study was conducted upon 28 consecutive patients with G.D. type I and III and 28 age and sex-matched controls. They underwent an MRI and DTI of the liver and spleen. Mean diffusivity (M.D.) and fractional anisotropy (F.A.) values of the liver and spleen were evaluated before and after treatment and compared with control. RESULTS: There was a statistically significant difference in the M.D. value of the liver and spleen between untreated patients and controls and between control and treated patients and in the M.D. value of the liver and spleen between untreated and treated patients. There is a statistically significant difference in the F.A. value of the liver and spleen between untreated patients and controls and in the F.A. value of the liver and spleen between untreated and treated patients. Hemoglobin level was positively correlated with the M.D. value of the spleen. Clinical score was negatively correlated with M.D. value of the spleen and was positively correlated with F.A. values of the liver and F.A. values of the spleen. Spleen volume was negatively correlated with M.D. values of the spleen. CONCLUSION: Significant difference in M.D. and F.A. values of liver and splenic parenchyma in p atients with type I and III G.D. and controls, and between untreated and treated patients. The M.D. and F.A. values were well correlated with some biomarkers of disease activity.

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images.

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support.