Multi-parametric arterial spin labeling and diffusion-weighted imaging of paranasal sinuses masses.

PURPOSE: To evaluate arterial spin labeling (ASL) and diffusion-weighted imaging (DWI) in discrimination of benign from malignant paranasal sinus (PNS) tumors. MATERIAL AND METHODS: A prospective study was done upon 42 cases of PNS masses that underwent magnetic resonance ASL and DWI of the head. Tumor blood flow (TBF) and apparent diffusion coefficient (ADC) of the masses were calculated by two observers. The pathological diagnosis was malignant (n = 28) and benign (n = 14) cases. RESULTS: For both observers, the malignant PNS masses had significantly higher TBF (P < 0.001, 0.001) and lower ADC (P < 0.001, 0.001) than in benign masses. The ROC curve analysis of TBF, The threshed TBF was (121.45, 122.68 mL/100 g/min) used for differentiation between benign and malignant PNS masses, revealed sensitivity (92.9%, 89.3%), specificity (85.7%, 85.7%), accuracy (90.5%, 88.1%) and the AUC was 0.87 and 0.86 by both observers. the ROC curve analysis of ADC, The threshold ADC (1.215, 1.205 X10-3mm2/s) was used for differentiation between benign and malignant PNS masses, revealed sensitivity (96.4%, 89.3%), specificity (78.6%, 78.6%), accuracy of (90.5%, 85.7%) and the AUC was 0.93 and 0.92 by both observers. Combined analysis of TBF and ADC used for differentiation between benign and malignant PNS masses had revealed sensitivity (96.4%, 89.3%), specificity (92.9%, 85.7%) accuracy of (95.2%, 88.1%) and AUC. (0.995, 0.985) for both observers. CONCLUSION: Combined using of TBF and ADC have a role in differentiation malignant from benign PNS masses.

Utility of diffusion tensor imaging in differentiating benign from malignant hepatic focal lesions.

OBJECTIVES: To assess the diagnostic accuracy of diffusion tensor imaging (DTI) in the characterization of hepatic focal lesions (HFLs) and compare it to diffusion-weighted imaging (DWI). METHODS: Prospective analysis was done for 49 patients (23 male and 26 female) with 74 HFLs who underwent dynamic MRI, DWI, and DTI. Apparent diffusion coefficient (ADC) values from DWI, fractional anisotropy (FA) values, and mean diffusivity (MD) values from DTI were measured by two independent radiologists. HFLs were classified into benign and malignant HFLs; the latter were subdivided into HCC and non-HCC lesions. Binary logistic regression was performed to analyze the associations between the DTI parameters and the distinction of malignant lesions. RESULTS: The ADC, MD, and FA at cutoff values of ≤ 1.17 × 10-3 mm2/s, ≤ 1.71 × 10-3 mm2/s, and > 0.29, respectively, are excellent discriminators for differentiating malignant and benign HFLs. The mean ADC and MD values of hemangiomas were significantly higher than HCC and non-HCC malignant lesions. In contrast, the mean FA values of hemangiomas were significantly lower than those of non-HCC malignant lesions and HCCs. The ADC and MD were very good discriminators at cutoff values of > 1.03 × 10-3 mm2/s and > 1.12 × 10-3 mm2/s, respectively. The FA at a cutoff value > 0.38 is an excellent discriminator for HCC versus non-HCC malignant lesions. Only FA value > 0.38 was a statistically significant independent predictor of HCC versus non-HCC lesions among the three parameters. There was an excellent inter-observer agreement with ICC > 0.9. CONCLUSION: MD and FA of DTI are non-invasive, very good, and excellent discriminators superior to ADC measured by DWI for the differentiation of HFLs. KEY POINTS: • The ADC, MD, and FA at cutoff values of ≤ 1.17 × 10-3 mm2/s, ≤ 1.71 × 10-3 mm2/s, and > 0.29, respectively, are excellent discriminators for differentiating malignant and benign HFLs. • The mean ADC and MD values of hemangiomas were significantly higher than those of HCC and non-HCC malignant lesions. In contrast, the mean FA values of hemangiomas were significantly lower than those of non-HCC malignant lesions and HCCs, respectively. • Multivariate regression analysis revealed that only FA value > 0.38 was a statistically significant independent predictor of HCC vs. non-HCC lesions. A lesion with FA > 0.38 has 34 times higher odds of being HCC rather than non-HCC lesions.

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.

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.

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 value of the apparent diffusion coefficient value in the Liver Imaging Reporting and Data System (LI-RADS) version 2018.

PURPOSE: To assess role of the apparent diffusion coefficient (ADC) in the Liver Imaging Reporting and Data System (LI-RADS) version 2018 for the prediction of hepatocellular carcinoma (HCC). MATERIAL AND METHODS: Retrospective analysis of 137 hepatic focal lesions in 108 patients at risk of HCC, who underwent magnetic resonance imaging of the liver. Hepatic focal lesions were classified according to LI-RADS-v2018, and ADC of hepatic lesions was calculated by 2 independent blinded reviewers. RESULTS: The mean ADC of LR-1 and LR-2 were 2.11 ± 0.47 and 2.08 ± 0.47 × 10-3 mm2/s, LR-3 were 1.28 ± 0.12 and 1.36 ± 0.16 × 10-3 mm2/s, LR-4, LR-5 and LR-TIV were 1.07 ± 0.08 and 1.08 ± 0.12 × 10-3 mm2/s and LR-M were 1.02 ± 0.09 and 1.00 ± 0.09 × 10-3 mm2/s by both observers, respectively. There was excellent agreement of both readings for LR-1 and LR-2 (r = 0.988), LR-3 (r = 0.965), LR-4, LR-5 and LR-TIV (r = 0.889) and LR-M (r = 0.883). There was excellent correlation between ADC and LI-RADS-v2018 (r = -0.849 and -0.846). The cut-off ADC used to differentiate LR-3 from LR-4, LR-5, and LR-TIV were ≤ 1.21 and ≤ 1.23 × 10-3 mm2/s with AUC of 0.948 and 0.926. CONCLUSIONS: Inclusion of ADC to LI-RADS-v2018 improves differentiation variable LI-RADS categories and can helps in the prediction of HCC.

Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning.

PURPOSE: To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification. PATIENTS AND METHODS: In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases. To quantify the nodules' morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests. RESULTS: The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and accuracy of 92.9 % $92.9\%$ (confidence interval [CI]: 78.9 % -- 99.5 % $78.9\%\text{--}99.5\%$ ), 95.8 % $95.8\%$ (CI: 87.4 % -- 99.7 % $87.4\%\text{--}99.7\%$ ), 93 % $93\%$ (CI: 80.7 % -- 99.5 % $80.7\%\text{--}99.5\%$ ), 96 % $96\%$ (CI: 88.8 % -- 99.7 % $88.8\%\text{--}99.7\%$ ), 92.8 % $92.8\%$ (CI: 83.5 % -- 98.5 % $83.5\%\text{--}98.5\%$ ), and 95.5 % $95.5\%$ (CI: 88.8 % -- 99.2 % $88.8\%\text{--}99.2\%$ ), respectively, using the LOSO cross-validation approach. CONCLUSION: The results demonstrated in this paper show the promise that integrating the functional features with morphology as well as texture features by using the current state-of-the-art machine learning approaches will be extremely useful for identifying thyroid nodules as well as diagnosing their malignancy.

Diffusion Tensor Imaging of Auditory Pathway in Patients With Crigler-Najjar Syndrome Type I: Correlation With Auditory Brainstem Response.

AIM: To evaluate the role of diffusion tensor imaging of the auditory pathway in patients with Crigler Najjar syndrome type I and its relation to auditory brainstem response. METHODS: Prospective study was done including 12 patients with Crigler Najjar syndrome type I and 10 age- and sex-matched controls that underwent diffusion tensor imaging of brain. Mean diffusivity and fractional anisotropy at 4 regions of the brain and brainstem on each side were measured and correlated with the results of auditory brainstem response for patients. RESULTS: There was significantly higher mean diffusivity of cochlear nucleus, superior olivary nucleus, inferior colliculus, and auditory cortex of patients versus controls on both sides for all regions (P = .001). The fractional anisotropy of cochlear nucleus, superior olivary nucleus, inferior colliculus, and auditory cortex of patients versus controls was significantly lower, with P values of, respectively, .001, .001, .003, and .001 on the right side and .001, .001, .003, and .001 on left side, respectively. Also, a negative correlation was found between the maximum bilirubin level and fractional anisotropy of the left superior olivary nucleus and inferior colliculus of both sides. A positive correlation was found between the mean diffusivity and auditory brainstem response wave latency of the right inferior colliculus and left cochlear nucleus. The fractional anisotropy and auditory brainstem response wave latency of the right superior olivary nucleus, left cochlear nucleus, and inferior colliculus of both sides were negatively correlated. CONCLUSION: Diffusion tensor imaging can detect microstructural changes in the auditory pathway in Crigler Najjar syndrome type I that can be correlated with auditory brainstem response.

Posttreatment Magnetic Resonance Imaging Surveillance of Head and Neck Cancers.

Treatment strategies and recommended surveillance imaging differ for head and neck cancers depending on subsite and neoplasm type, and pose confusion for referring physicians and interpreting radiologists. The superior soft tissue resolution offered by magnetic resonance imaging is most useful in the surveillance of cancers with high propensities for intraorbital, intracranial, or perineural disease spread, which most commonly include those arising from the sinonasal cavities, nasopharynx, orbits, salivary glands, and the skin. This article discusses recommended surveillance protocoling and reviews treatment approaches, common posttreatment changes, and pearls for identifying disease recurrence in a subsite-based approach.

Role of MR Imaging in Head and Neck Squamous Cell Carcinoma.

Routine and advanced MR imaging sequences are used for locoregional spread, nodal, and distant staging of head and neck squamous cell carcinoma, aids treatment planning, predicts treatment response, differentiates recurrence for postradiation changes, and monitors patients after chemoradiotherapy.

Preimaging and Postimaging of Graft and Flap in Head and Neck Reconstruction.

Head and neck reconstructive surgical techniques are complex; now the microvascular free tissue transfer is the most frequently used. The postreconstruction imaging interpretation is challenging due to the altered anatomy and flap variability. We aim to improve radiologists' knowledge with diverse methods of flap reconstruction for an accurate appreciation of their expected cross-sectional imaging appearance and early detection of tumor recurrence and other complication.

MR Imaging of Salivary Gland Tumors.

Neoplasms of the salivary glands are characterized by their marked histologic diversity giving them nonspecific imaging findings. MR imaging is the best imaging modality to evaluate salivary gland tumors. Multiparametric MR imaging combines conventional imaging features, diffusion-weighted imaging, and perfusion imaging to help distinguish benign and low-grade neoplasms from malignant tumors; however, a biopsy is often needed to establish a definitive histopathologic diagnosis. An awareness of potential imaging pitfalls is important to prevent mistakes in salivary neoplasm imaging.

Update on MR Imaging of Soft Tissue Tumors of Head and Neck.

This article reviews soft tissue tumors of the head and neck following the 2020 revision of WHO Classification of Soft Tissue and Bone Tumours. Common soft tissue tumors in the head and neck and tumors are discussed, along with newly added entities to the classification system. Salient clinical and imaging features that may allow for improved diagnostic accuracy or to narrow the imaging differential diagnosis are covered. Advanced imaging techniques are discussed, with a focus on diffusion-weighted and dynamic contrast imaging and their potential to help characterize soft tissue tumors and aid in distinguishing malignant from benign tumors.

MR Imaging of Vascular Malformations and Tumors of Head and Neck.

Soft tissue vascular anomalies show a wide heterogeneity of clinical manifestations and imaging features. MR imaging has an important role in the diagnosis and management of vascular lesions of the head and neck. MR angiography is mandatory in cases of arteriovenous and combined malformations to assess the high-flow nature/component of the lesions and plan therapy. Infantile hemangiomas can be differentiated from congenital hemangiomas by clinical course. Reactive vascular tumors have nonspecific features similar to infantile hemangiomas. Locally malignant and malignant vascular tumors have irregular borders, infiltration of different tissue planes, and lower apparent diffusion coefficient values than benign vascular tumors.

MR imaging of Oral Cavity and Oropharyngeal Cancer.

MR imaging is the modality of choice in the evaluation of oral cavity and oropharyngeal cancer. Routine postcontrast MR imaging is important for the accurate localization and characterization of the locoregional extension of oral cavity and oropharyngeal cancers. The anatomy of the oral cavity and oropharynx is complex; accurate interpretation is vital for description of the extension of the masses. Understanding the new changes in the eighth edition of the American Joint Committee on Cancer staging system. MR imaging is the imaging modality of choice for detection of perineural spread.

Artificial Intelligence and Deep Learning of Head and Neck Cancer.

Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.

Magnetic Resonance Imaging of Perineural Spread of Head and Neck Cancer.

Perineural tumor spread (PNTS) is one of the important methods of tumoral spread in head and neck cancers. It consists of a complex process that entails the production of certain chemicals or the production of certain cell receptors. Histologic type and primary tumor site play an important role in PNTS. Any nerve could be affected; however, the trigeminal and facial nerves are the most involved nerves. Magnetic resonance imaging and computed tomography detect the primary and secondary signs of PNTS. Functional imaging such as diffusion-weighted imaging and hybrid imaging act as problem-solving techniques.

The relevance of left ventricular functions to clinical and metabolic characteristics of prepubertal children with obesity.

BACKGROUND: Paediatric obesity is a worldwide health burden, with growing evidence linking obesity to myocardial function impairments. The study aims to evaluate left ventricular functions among prepubertal obese children to obesity-related clinical and metabolic parameters. METHODS: Between June 2019 and March 2020, 40 prepubertal children with obesity were recruited and compared to 40 healthy controls. Patients were assessed for body mass index z scores, waist circumference, body adiposity by bioimpedance analysis, and obesity-related laboratory tests, for example, serum chemerin. Left ventricular functions were assessed using variable echocardiographic modalities, such as M-mode, tissue Doppler, and two-dimensional speckle tracking. RESULTS: Mean patients' age was 9.25 ± 1.05 years. Left ventricular mass index, E/E', and myocardial performance index were significantly increased in obese children than controls. Although M-mode-derived ejection fraction was comparable in both groups, two-dimensional speckle tracking-derived ejection fraction, global longitudinal strain, and global circumferential strain were significantly lower in cases than controls. Left ventricular mass index displayed a positive correlation with body mass index z score (p = 0.003), fat mass index (p = 0.037), and trunk fat mass (p = 0.021). Global longitudinal strain was negatively correlated with body mass index z score (p = 0.015) and fat mass index (p = 0.016). Serum chemerin was positively correlated with myocardial performance index (p = 0.01). CONCLUSION: Alterations of left ventricular myocardial functions in prepubertal obese children could be detected using different echocardiographic modalities. Chemerin, body mass index z score, fat mass index, and trunk fat mass were correlated with subclinical left ventricular myocardial dysfunction parameters before puberty. Our results reinforce early and strict management of childhood obesity upon detection of changes in anthropometric and body adiposity indices.

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.

Diffusion Tensor Imaging in Characterization of Mediastinal Lymphadenopathy.

OBJECTIVE: To determine the efficacy of diffusion-weighted MRI (DWI) and diffusion tensor imaging (DTI) in the characterization of mediastinal lymphadenopathy and the differentiation between malignant and benign lymph nodes (LNs). METHODS: a retrospective evaluation of 58 patients with mediastinal lymphadenopathy that underwent DWI and DTI with calculation of apparent diffusion coefficient (ADC), fractional anisotropy (FA), and mean diffusivity (MD) values of LNs. Final diagnosis was made by the histopathology and proved metastatic (n = 21), lymphomatous (n = 14), granulomatous (n = 11) and reactive (n = 12) LNs. RESULTS: Malignant mediastinal LNs had remarkably lower ADC and MD; (p = 0.001) and higher FA; (p = 0.001) than in benign LNs. The threshold of ADC, MD, and FA at (1.48, 1.32 × 10-3 mm2/s), (1.31, 1.33 × 10-3 mm2/s), (0.62, 0.52) to differentiate malignant from benign LNs has AUC of (0.89, 0.94), (0.96, 0.95), (0.72, 0.82), accuracy of (87%, 86%), (89%, 86%), (70%, 72%) by both observers respectively. The threshold of ADC, MD, and FA at (1.47, 1.32 × 10-3 mm2/s), (1.31, 1.3 × 10-3 mm2/s), (0.62, 0.67) used to differentiate metastatic from reactive LNs revealed AUC of (0.90, 0.94), (0.96, 0.96), (0.73, 0.77), accuracy of (87%, 81%), (87%, 81%), (72%, 66%) by both observers respectively. The mean ADC and MD values of metastatic LNs were statistically significant (p = 0.001) and (p = 0.002, 0.02) respectively when compared with that of lymphoma. The threshold of ADC, and MD (0.94, 0.97 × 10-3 mm2/s) and (0.87, 0.91 × 10-3 mm2/s) used to differentiates metastatic from lymphomatous nodes revealed AUC of (0.90, 0.91), (0.81, 0.74), an accuracy of (85%, 91%), (71%, 71%), by both observers respectively. The inter-class correlation between two observers for all nodes for ADC, MD and FA was r= 0.931, 0.956 and 0.885 respectively. CONCLUSION: Using ADC, MD, and FA can help in the characterization of mediastinal lymphadenopathy noninvasively.