Inter-observer agreement of the Coronary Artery Disease Reporting and Data System (CAD-RADS^{TM}) in patients with stable chest pain

Purpose: To assess inter-observer variability of the Coronary Artery Disease - Reporting and Data System (CAD-RADS) for classifying the degree of coronary artery stenosis in patients with stable chest pain. Material and methods: A prospective study was conducted upon 96 patients with coronary artery disease, who underwent coronary computed tomography angiography (CTA). The images were classified using the CAD-RAD system according to the degree of stenosis, the presence of a modifier: graft (G), stent (S), vulnerable plaque (V), or non-diagnostic (n) and the associated coronary anomalies, and non-coronary cardiac and extra-cardiac findings. Image analysis was performed by two reviewers. Inter-observer agreement was assessed. Results: There was excellent inter-observer agreement for CAD-RADS (k = 0.862), at 88.5%. There was excellent agreement for CAD-RADS 0 (k = 1.0), CAD-RADS 1 (k = 0.92), CAD-RADS 3 (k = 0.808), CAD-RADS 4 (k = 0.826), and CAD-RADS 5 (k = 0.833) and good agreement for CAD-RADS 2 (k = 0.76). There was excellent agreement for modifier G (k = 1.0) and modifier S (k = 1.0), good agreement for modifier N (k = 0.79), and moderate agreement for modifier V (k = 0.59). There was excellent agreement for associated coronary artery anomalies (k = 0.845), non-coronary cardiac findings (k = 0.857), and extra-cardiac findings (k = 0.81). Conclusions: There is inter-observer agreement of CAD-RADS in categorising the degree of coronary arteries stenosis, and the modifier of the system and associated cardiac and extra-cardiac findings

Inter-observer agreement of whole-body computed tomography in staging and response assessment in lymphoma : the lugano classification

Background: to assess inter-observer agreement of whole-body computed tomography (WBCT) in staging and response assessment in lymphoma according to the Lugano classification. Material/Methods: Retrospective analysis was conducted of 115 consecutive patients with lymphomas (45 females, 70 males; mean age of 46 years). Patients underwent WBCT with a 64 multi-detector CT device for staging and response assessment after a complete course of chemotherapy. Image analysis was performed by 2 reviewers according to the Lugano classification for staging and response assessment. Results: The overall inter-observer agreement of WBCT in staging of lymphoma was excellent (k=0.90, percent agreement=94.9%). There was an excellent inter-observer agreement for stage I (k=0.93, percent agreement=96.4%), stage II (k=0.90, percent agreement=94.8%), stage III (k=0.89, percent agreement=94.6%) and stage IV (k=0.88, percent agreement=94%). The overall inter-observer agreement in response assessment after a completer course of treatment was excellent (k=0.91, percent agreement=95.8%). There was an excellent inter-observer agreement in progressive disease (k=0.94, percent agreement=97.1%), stable disease (k=0.90, percent agreement=95%), partial response (k=0.96, percent agreement=98.1%) and complete response (k=0.87, Percent agreement=93.3%). Conclusions: We concluded that WBCT is a reliable and reproducible imaging modality for staging and treatment assessment in lymphoma according to the Lugano classification

Role of diffusion-weighted magnetic resonance (MR) imaging in differentiation between graves' disease and painless thyroiditis

Background: To assess the role of diffusion-weighted MR imaging in differentiation between Graves' disease and painless thyroiditis. Material/Methods: A prospective study was conducted among 37 consecutive patients with untreated thyrotoxicosis (25 female and 12 male; mean age of 44 years) and 15 age- and sex-matched controls. Diffusion-weighted MR imaging of the thyroid gland was performed in patients and controls. The apparent diffusion coefficient (ADC) value of the thyroid gland was calculated and correlated with Tc-99m uptake and thyroid function tests of the patients. Results: There was a significant difference in the ADC value of the thyroid gland between patients and the control group (P=0.001). The mean ADC value of the thyroid gland in Graves' disease was 2.03±0.28×10-3 mm2/sec, and in patients with painless thyroiditis 1.46±0.22×10-3 mm2/sec, respectively. There was a significant difference in the ADC values between Graves' disease and painless thyroiditis (P=0.001). When the ADC value of 1.45×10-3 mm2/sec was used as a threshold value for differentiating Graves' disease from painless thyroiditis, the best result was obtained with area under the curve of 0.934, accuracy of 83.8%, sensitivity of 95.8%, and specificity of 61.5%. The mean ADC value of the thyroid gland in patients positively correlated with serum TRAb and Tc-99m uptake (r=0.57, P=0.001 and r=0.74, P=0.001, respectively). Conclusions: We concluded that ADC values of the thyroid gland can be used to differentiate Graves' disease from painless thyroiditis in patients with untreated thyrotoxicosis

Magnetic resonance spectroscopy of the frontal region in patients with metabolic syndrome : correlation with anthropometric measurement

Purpose: to demonstrate 1H-MR spectroscopy of the frontal region in patients with metabolic syndrome and to correlate the metabolic ratios with anthropometric measurement. Material and methods: A prospective study was conducted upon 20 patients with metabolic syndrome (10 male, 10 female; mean age 52 years) and 20 age- and sex-matched volunteers. Patients were mild-moderate (n = 14) and marked and morbid obesity (n = 6). Patients and volunteers underwent 1H-MR spectroscopy of the frontal region. The Ch/Cr and NAA/Cr ratio were calculated and correlated with anthropometric measurement. Results: The Cho/Cr and NAA/Cr of patients with Mets (1.03 ± 0.08 and 1.62 ± 0.08) were significantly different (p = 0.001) to those of volunteers (0.78 ± 0 and 1.71 ± 0.61, respectively). The Cho/Cr and NAA/Cr cutoffs used to differentiate patients from volunteers were 0.89 and 1.77 with areas under the curve of 0.992 and 0.867 and accuracy of 97% and 93%, respectively. There was a significant difference in Cho/Cr and NAA/Cr between patients with marked-morbid obesity and moderate-mild obesity (p = 0.001 respectively). Conclusions: We concluded that NAA/Cr and Cho/Cr ratios of the frontal region can differentiate patients with metabolic syndrome from volunteers and are well correlated with the anthropometric measurement

Computed tomography assessment of hepatic metastases of breast cancer with revised response evaluation criteria in solid tumors (RECIST) criteria (version 1.1) : inter-observer agreement

Background: To assess inter-observer agreement of revised RECIST criteria (version 1.1) for computed tomography assessment of hepatic metastases of breast cancer. Material/Methods: A prospective study was conducted in 28 female patients with breast cancer and with at least one measurable metastatic lesion in the liver that was treated with 3 cycles of anthracycline-based chemotherapy. All patients underwent computed tomography of the abdomen with 64-row multi-detector CT at baseline and after 3 cycles of chemotherapy for response assessment. Image analysis was performed by 2 observers, based on the RECIST criteria (version 1.1). Results: Computed tomography revealed partial response of hepatic metastases in 7 patients (25%) by one observer and in 10 patients (35.7%) by the other observer, with good inter-observer agreement (k=0.75, percent agreement of 89.29%). Stable disease was detected in 19 patients (67.8%) by one observer and in 16 patients (57.1%) by the other observer, with good agreement (k=0.774, percent agreement of 89.29%). Progressive disease was detected in 2 patients (7.2%) by both observers, with perfect agreement (k=1, percent agreement of 100%). The overall inter-observer agreement in the CT-based response assessment of hepatic metastasis between the two observers was good (k=0.793, percent agreement of 89.29%). Conclusions: We concluded that computed tomography is a reliable and reproducible imaging modality for response assessment of hepatic metastases of breast cancer according to the RECIST criteria (version 1.1)

Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas

Purpose: To assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas. Material and methods: A prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas. Results: There was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10-3 mm2/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%. Conclusion: TBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas

COVID-19 and myocarditis: a brief review

Cardiovascular complications (especially myocarditis) related to COVID-19 viral infection are not well understood, nor do they possess a well recognized diagnostic protocol as most of our information regarding this issue was derived from case reports. In this article we extract data from all published case reports in the second half of 2020 to summarize the theories of pathogenesis and explore the value of each diagnostic test including clinical, lab, ECG, ECHO, cardiac MRI and endomyocardial biopsy. These tests provide information that explain the mechanism of development of myocarditis that further paves the way for better management

The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients

Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs energy markers were extracted from each CT scan by tuning the MGRF parameters on each lesion separately. The latter were healthy/mild, moderate, and severe lesions. To represent these markers more reliably, a Cumulative Distribution Function (CDF) was generated, then statistical markers were extracted from it, namely, 10th through 90th CDF percentiles with 10% increments. Subsequently, the three extracted markers were combined together and fed into a backpropagation neural network to make the diagnosis. The developed system was assessed on 76 COVID-19-infected patients using two metrics, namely, accuracy and Kappa. In this paper, the proposed system was trained and tested by three approaches. In the first approach, the MGRF model was trained and tested on the lungs. This approach achieved 95.83% accuracy and 93.39% kappa. In the second approach, we trained the MGRF model on the lesions and tested it on the lungs. This approach achieved 91.67% accuracy and 86.67% kappa. Finally, we trained and tested the MGRF model on lesions. It achieved 100% accuracy and 100% kappa. The results reported in this paper show the ability of the developed system to accurately grade COVID-19 lesions compared to other machine learning classifiers, such as k-Nearest Neighbor (KNN), decision tree, naïve Bayes, and random forest

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

Imaging appearance of bone tumors of the maxillofacial region

This paper reviews the imaging appearance of benign and malignant bone tumors of the maxillofacial region. A benign bone tumor commonly appears as a well circumscribed lesion. The matrix of the tumor may be calcified or sclerotic. Malignancies often display aggressive characteristics such as cortical breakthrough, bone destruction, a permeative pattern and associated soft-tissue masses. Computed tomography scan is an excellent imaging modality for accurate localization of the lesion, characterization of the tumor matrix and detection of associated osseous changes such as bone remodeling, destruction or periosteal reaction. Magnetic resonance imaging is of limited value in the evaluation of maxillofacial bone tumors