Clinical and radiological characteristics of pediatric patients with COVID-19: focus on imaging findings

Purpose: CT imaging has been a detrimental tool in the diagnosis of COVID-19, but it has not been studied thoroughly in pediatric patients and its role in diagnosing COVID-19. Methods: 27 pediatric patients with COVID-19 pneumonia were included. CT examination and molecular assay tests were performed from all participants. A standard checklist was utilized to extract information, and two radiologists separately reviewed the CT images. Results: The mean age of patients was 4.7 ± 4.16 (mean ± SD) years. Seventeen patients were female, and ten were male. The most common imaging finding was ground-glass opacities followed by consolidations. Seven patients had a single area of involvement, five patients had multiple areas of involvement, and four patients had diffuse involvement. The sensitivity of CT imaging in diagnosing infections was 66.67. Also, some uncommon imaging findings were seen, such as a tree-in-bud and lung collapse. Conclusion: CT imaging shows less involvement in pediatric compared to adult patients, due to pediatric patients having a milder form of the disease. CT imaging also has a lower sensitivity in detecting abnormal lungs compared to adult patients. The most common imaging findings are ground-glass opacities and consolidations, but other non-common imaging findings also exist. © 2020, Japan Radiological Society

Diagnostic ability of 384-slice computed tomographic angiography in prediction of myocardial ischemia in patients with myocardial bridging (MB) as compared to SPECT-MPI examination

Background: During the past decade, coronary computed tomographic angiography (CCTA) has become the primary non-invasive imaging technique for the assessment of myocardial bridging (MB). Objectivs: The aim of this study was to evaluate the ability of CCTA to predict myocardial ischemia in patients with MB. Patients and Methods: A total of 32 MB patients (21 males and 11 females) participated in this study. Eleven MB parameters were measured to assess the ability of CCTA to predict MB patients with ischemia. In order to evaluate ischemia, all the patients underwent single positron emission computed tomography-myocardial perfusion imaging (SPECT-MPI) examination. Results: Ischemia was observed in 17 patients (53.1), while 15 patients (46.9) did not show signs of ischemia. Out of the 32 patients, superficial MB was observed in 15 patients while deep MB was identified in 12, and borderline was observed in five patients. All MB examined parameters were found to be significantly different between ischemic and non-ischemic patients, except for the location and tunnel artery diameter in diastole. Moreover, a cut-off value of 0.65 mm was able to discriminate ischemia with a sensitivity of 100, specificity of 93, and yield area under the receiver operating characteristic (ROC) curve (AUC) of 0.996. Also, by considering the depth cut-off value of 1.75 mm, ischemia can be distinguished with sensitivity and specificity of 100. MB length had a lower discrimination power, with a cut off value of 22.5 mm yield, 76 sensitivity, 67 specificity, and AUC = 0.810 in the diagnosis of ischemia. Conclusion: CCTA was a reliable modality with high accuracy to depict MB, identify high risk MB, and prevent unnecessary SPECT-MPI examination. © 2018, Iranian Journal of Radiology

COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings

Objectives: CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients. Methods: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases. Results: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54, 90.32, and 91.94, respectively, using ensemble (COVIDiag) classifier. Conclusions: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis. Key Points: � Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. � The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54; specificity = 90.32; and accuracy = 91.94). � The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10, 88.71, and 87.90, respectively. © 2020, European Society of Radiology

CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold

Objectives: The aim of this study was to evaluate if the analysis of sonographic parameters could predict if a thyroid nodule was hot or cold. Methods: Overall, 102 thyroid nodules, including 51 hyperfunctioning (hot) and 51 hypofunctioning (cold) nodules, were evaluated in this study. Twelve sonographic features (i.e., seven B-mode and five Doppler features) were extracted for each nodule type. The isthmus thickness, nodule volume, echogenicity, margin, internal component, microcalcification, and halo sign features were obtained in the B-mode, while the vascularity pattern, resistive index (RI), peak systolic velocity, end diastolic velocity, and peak systolic/end diastolic velocity ratio (SDR) were determined, based on Doppler ultrasounds. All significant features were incorporated in the computer-aided diagnosis (CAD) system to classify hot and cold nodules. Results: Among all sonographic features, only isthmus thickness, nodule volume, echogenicity, RI, and SDR were significantly different between hot and cold nodules. Based on these features in the training dataset, the CAD system could classify hot and cold nodules with an area under the curve (AUC) of 0.898. Also, in the test dataset, hot and cold nodules were classified with an AUC of 0.833. Conclusions: 2D sonographic features could differentiate hot and cold thyroid nodules. The CAD system showed a great potential to achieve it automatically. Key Points: � Cold nodules represent higher volume (p = 0.005), isthmus thickness (p = 0.035), RI (p = 0.020), and SDR (p = 0.044) and appear hypoechogenic (p = 0.010) in US. � Nodule volume with an AUC of 0.685 and resistive index with an AUC of 0.628 showed the highest classification potential among all B-mode and Doppler features respectively. � The proposed CAD system could distinguish hot nodules from cold ones with an AUC of 0.833 (sensitivity 90.00, specificity 70.00, accuracy 80.00, PPV 87.50, and NPV 75.00). © 2019, European Society of Radiology

The effects of gold nanoparticles characteristics and laser irradiation conditions on spatiotemporal temperature pattern of an agar phantom: A simulation and MR thermometry study

In this paper, the effects of parameters related to gold nanoparticles (type, size, and concentration) and the laser parameters on spatiotemporal temperature pattern of an agar phantom during a photothermal therapy (PTT) procedure were modeled and then experimentally verified. Eight agar phantoms loaded by gold nanoparticles were made. An agar phantom without any nanoparticles was also considered as the control. Different sizes of two types of gold nanoparticles (spherical and silica-gold core shell) at various concentrations were studied. The phantoms were irradiated by various laser powers for 5 min. The temperature changes in each phantom was firstly calculated using COMSOL Multiphysics software. Also, each phantom was irradiated by laser and MR thermometry was performed to validate the simulation results. A reasonable correlation between simulation and MR thermometry was obtained (R = 0.92). The error interval between calculations and experiments was ranged from ±3 to ±6. It was clearly evident that laser irradiation conditions and nanoparticle characteristics affected the temperature rise profile. Spherical 20 nm gold nanoparticles had better thermal efficiency and generated higher level of heat. The protocol suggested in this study may be appropriate to make a pre-clinical calculation and effect visualization for any nanoparticles-based PTT procedure before entrance into the clinics. © 2019 Elsevier Gmb

A Hybrid Multilayer Filtering Approach for Thyroid Nodule Segmentation on Ultrasound Images

Objectives: Speckle noise is the main factor that degrades ultrasound image contrast and segmentation failure. Determining an effective filter can reduce speckle noise and improve segmentation performances. The aim of this study was to define a useful filter to improve the segmentation outcome. Methods: Twelve filters, including median, hybrid median (Hmed), Fourier Butterworth, Fourier ideal, wavelet (Wlet), homomorphic Fourier Butterworth, homomorphic Fourier ideal, homomorphic wavelet (HmpWlet), frost, anisotropic diffusion, probabilistic patch-based (PPB), and homogeneous area filters, were used to find the best filter(s) to prepare thyroid nodule segmentation. A receiver operating characteristic (ROC) analysis was used for filter evaluation in the nodule segmentation process. Accordingly, 10 morphologic parameters were measured from segmented regions to find the best parameters that predict the segmentation performance. Results: The best segmentation performance was reached by using 4 hybrid filters that mainly contain contrast-limited adaptive histogram equalization, Wlet, Hmed, HmpWlet, and PPB filters. The area under the ROC curve for these filters ranged from 0.900 to 0.943 in comparison with the original image, with an area under the curve of 0.685. From 10 morphologic parameters, the area, convex area, equivalent diameter, solidity, and extent can evaluate segmentation performance. Conclusions: Hybrid filters that contain contrast-limited adaptive histogram equalization, Wlet, Hmed, HmpWlet, and PPB filters have a high potential to provide good conditions for thyroid nodule segmentation in ultrasound images. In addition to an ROC analysis, morphometry of a segmented region can be used to evaluate segmentation performances. © 2018 by the American Institute of Ultrasound in Medicin