Corona Virus International Public Health Emergencies: Implications for Radiology Management.

The outbreak of 2019 novel coronavirus (2019-nCoV) pneumonia was reported in Wuhan, Hubei Province, China in December 2019 and has spread internationally. This article discusses how radiology departments can most effectively respond to this public health emergency.

Differential diagnosis of central lymphoma and high-grade glioma: dynamic contrast-enhanced histogram.

BACKGROUND: In clinical diagnosis, some central nervous system lymphomas (CNSL) are difficult to distinguish from high-grade gliomas (HGG). PURPOSE: To evaluate the diagnostic efficacy of the histogram analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in the identification of CNSL and HGG. MATERIAL AND METHODS: In all, 43 patients diagnosed with HGG (n = 28) and CNSL (n = 15) by histopathology underwent DCE-MRI scanning. Differences in histogram parameters based on DCE-MRI between HGG and CNSL were analyzed by Mann-Whitney U test. In addition, receiver operating characteristic (ROC) analysis was performed. Short-term follow-up of patients was performed using Kaplan-Meier analysis to explore the survival rates of HGG and CNSL. RESULTS: For the ROC curve analysis, we demonstrate that the 10th percentile of Ktrans (area under the curve [AUC] = 0.912, sensitivity = 86.7%, specificity = 92.9%), Kep (AUC = 0.940, sensitivity = 93.3%, specificity = 79.6%), Ve (AUC = 0.907, sensitivity = 86.7%, specificity = 89.3%), and AUC (AUC = 0.904, sensitivity = 86.7%, specificity = 92.9%) were significantly different between the CNSL and HGG groups (P < 0.001), with high diagnostic efficiency. Table 2 shows that the histogram features based on AUC maps (10th, 25th, median, 75th, 90th, and mean) were always significantly higher in the CNSL group than in the HGG group (P < 0.001). There was no significant difference in Vp or in the 75th, 90th and mean of Ktrans, Kep, and Ve between the CNSL and HGG groups (P > 0.05). CONCLUSION: A histogram analysis of DCE-MRI identified significant differences between HGG and CNSL, and this will help in the clinical differential diagnosis of these conditions.

DSC and DCE Histogram Analyses of Glioma Biomarkers, Including IDH, MGMT, and TERT, on Differentiation and Survival.

RATIONALE AND OBJECTIVES: The World Health Organization 2016 classification of central nervous system tumors added the molecular classification of gliomas and has guiding significance for the operation and prognosis of glioma patients. At present, the perfusion technique plays an important role in judging the malignant degree of glioma. To evaluate the performance of dynamic susceptibility contrast (DSC)- and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) histogram analyses in discriminating the states of molecular biomarkers and survival in glioma patients. MATERIALS AND METHODS: Forty-three glioma patients who underwent DCE- and DSC-MRI were enrolled. Relevant molecular test results, including those on isocitrate dehydrogenase (IDH), O6-methylguanine-DNA methyltransferase (MGMT) and telomere reverse transcriptase (TERT), were collected. The mean relative cerebral blood volume of DSC-MRI and histogram parameters derived from DCE-MRI (volume transfer coefficient (Ktrans), fractional volume of the extravascular extracellular space (Ve), fractional blood plasma volume (Vp), rate constant between the extravascular extracellular space and blood plasma (Kep) and area under the curve (AUC)) were calculated. Differences in each parameter between gliomas with different expression states (IDH, MGMT, and TERT) were evaluated. The diagnostic efficiency of each parameter was analyzed. The overall survival of all patients was assessed. RESULTS: The 10th percentile AUC (AUC = 0.830, sensitivity = 0.78, specificity = 0.80), the 90th percentile Ve (AUC = 0.816, sensitivity = 0.84, specificity = 0.79), and the mean Kep (AUC = 0.818, sensitivity = 0.76, specificity = 0.78) provided the highest differential efficiency for IDH, MGMT, and TERT, respectively. Kaplan-Meier curves showed a significant difference between subjects with a 10th percentile AUC higher or lower than 0.028 (log-rank = 7.535; p = 0.006) for IDH and between subjects with different 90th percentile Ve values (log-rank = 6.532; p = 0.011) for MGMT. CONCLUSION: Histogram DCE-MRI demonstrates good diagnostic performance in identifying different molecular types and for the prognostic assessment of glioma.