Diagnostic performance of a new multicontrast one-minute full brain exam (EPIMix) in neuroradiology: A prospective study.

BACKGROUND: Clinical MRI protocols are time-consuming; hence, new faster techniques are needed. One new fast multicontrast MRI technique, called echo planar image mix (EPIMix) (including contrasts T1 -FLAIR, T2 -weighted, diffusion-weighted images [DWI], apparent diffusion coefficient [ADC], T2 *-weighted, and T2 -FLAIR images) needs to be tested. PURPOSE: To assess if EPIMix has comparable diagnostic performance as routine clinical brain MRI. STUDY TYPE: Prospective. POPULATION: A consecutive series of 103 patients' brain MRI (January 2018 to May 2018). FIELD STRENGTH/SEQUENCE: 1.5 T or 3T. EPIMix and routine clinical protocol (clinical MRI included all or some of the contrasts T1 -FLAIR, T2 -weighted, DWI, T2 *-weighted, T2 -FLAIR, 3D-FSE). ASSESSMENT: Two neuroradiologists assessed EPIMix and clinical scans and categorized the images as abnormal or normal and described diagnosis, artifacts, diagnostic confidence image quality, and comparison of imaging time. STATISTICAL TESTS: Pivot tables with diagnostic performance calculated by receiver operating characteristics (ROC) and the area under curve (AUC). Disease categorization and image quality measures were evaluated. The study protocol is published at ClinicalTrials.gov NCT03338270. RESULTS: After exclusion of 21 patients, 82 patients had a routine clinical MRI with comparable contrasts to EPIMix and were evaluated. The diagnostic performance to categorize a full brain MRI investigation as abnormal or normal was comparable between EPIMix (AUC 0.99 (95% confidence interval [CI] 0.97-1.00) and 0.99 (95% CI 0.97-1.00)) and routine clinical MRI (n = 82). Sensitivity was 95% (95% CI 88-95) and 93% (95% CI 86-98), and specificity 100% (95% CI 97-100) and 100% (95% CI 90-100). Disease categorization was congruent between EPIMix and clinical routine MRI in 90% (reader 2) and 93% (reader 1). Image quality was generally rated lower for EPIMix (P < 0.001). Imaging time was 78 seconds for EPIMix and for the same contrasts 12 minutes 29 seconds for conventional 3T MRI. DATA CONCLUSION: EPIMix has comparable diagnostic performance (disease identification and categorization) for most patients investigated in clinical routine. Level of Evidence 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1824-1833.

Diagnostic accuracy of 11C-methionine PET in detecting neuropathologically confirmed recurrent brain tumor after radiation therapy.

OBJECTIVE: This study aims to determine the diagnostic test accuracy (DTA) of 11C-methionine (MET) PET in the discrimination between recurrent tumor and radiation-induced injury in neuropathologically confirmed cases. METHODS: A retrospective cohort of 30 patients with previously irradiated intracranial tumors (23 gliomas, 6 metastases, and 1 meningioma) was included. All patients underwent a preoperative MET PET and postoperative neuropathological analysis. Maximum and mean standardized uptake values (SUV) were obtained in the lesion, in the contralateral mirror region, and in the contralateral frontal cortex. Lesion-to-background SUV ratios (SUR mirror and SUR cortex) were then calculated. The Mann-Whitney U test was used to evaluate differences in SUV ratios between confirmed recurrent tumor and radiation injury. DTA was determined through receiver operating characteristic (ROC) analysis. RESULTS: Twenty-one patients had recurrent tumor and nine had radiation injury. The area under the ROC curve (AUC) was 0.89 for SURmaxmirror and 0.88 for SURmaxcortex. The mean (SD) of SURmaxmirror was 2.37 (0.58) in tumor recurrence and 1.57 (0.40) in radiation necrosis (P ≤ 0.001). The corresponding values for SURmaxcortex were 2.13 (0.50) and 1.45 (0.37) (P = 0.001). Clinically relevant cutoffs were SURmaxmirror ≥ 1.99 giving a specificity of 100% for tumor recurrence with a sensitivity of 76% and SURmaxcortex ≥ 1.58 giving a sensitivity and specificity of 90 and 78%, respectively. CONCLUSIONS: Based on neuropathologically confirmed cases, the DTA of SURmaxmirror and SURmaxcortex from 11C-methionine PET was high when discriminating recurrent tumor from radiation injury.

Diffusion Kurtosis Imaging of Gliomas Grades II and III - A Study of Perilesional Tumor Infiltration, Tumor Grades and Subtypes at Clinical Presentation.

BACKGROUND: Diffusion kurtosis imaging (DKI) allows for assessment of diffusion influenced by microcellular structures. We analyzed DKI in suspected low-grade gliomas prior to histopathological diagnosis. The aim was to investigate if diffusion parameters in the perilesional normal-appearing white matter (NAWM) differed from contralesional white matter, and to investigate differences between glioma malignancy grades II and III and glioma subtypes (astrocytomas and oligodendrogliomas). PATIENTS AND METHODS: Forty-eight patients with suspected low-grade glioma were prospectively recruited to this institutional review board-approved study and investigated with preoperative DKI at 3T after written informed consent. Patients with histologically proven glioma grades II or III were further analyzed (n=35). Regions of interest (ROIs) were delineated on T2FLAIR images and co-registered to diffusion MRI parameter maps. Mean DKI data were compared between perilesional and contralesional NAWM (student's t-test for dependent samples, Wilcoxon matched pairs test). Histogram DKI data were compared between glioma types and glioma grades (multiple comparisons of mean ranks for all groups). The discriminating potential for DKI in assessing glioma type and grade was assessed with receiver operating characteristics (ROC) curves. RESULTS: There were significant differences in all mean DKI variables between perilesional and contralesional NAWM (p=<0.000), except for axial kurtosis (p=0.099). Forty-four histogram variables differed significantly between glioma grades II (n=23) and III (n=12) (p=0.003-0.048) and 10 variables differed significantly between ACs (n=18) and ODs (n=17) (p=0.011-0.050). ROC curves of the best discriminating variables had an area under the curve (AUC) of 0.657-0.815. CONCLUSIONS: Mean DKI variables in perilesional NAWM differ significantly from contralesional NAWM, suggesting altered microstructure by tumor infiltration not depicted on morphological MRI. Histogram analysis of DKI data identifies differences between glioma grades and subtypes.

Discrimination between Glioma Grades II and III Using Dynamic Susceptibility Perfusion MRI: A Meta-Analysis.

BACKGROUND: DSC perfusion has been evaluated in the discrimination between low-grade and high-grade glioma but the diagnostic potential to discriminate beween glioma grades II and III remains unclear. PURPOSE: Our aim was to evaluate the diagnostic accuracy of relative maximal CBV from DSC perfusion MR imaging to discriminate glioma grades II and III. DATA SOURCES: A systematic literature search was performed in PubMed/MEDLINE, Embase, Web of Science, and ClinicalTrials.gov. STUDY SELECTION: Eligible studies reported on patients evaluated with relative maximal CBV derived from DSC with a confirmed neuropathologic diagnosis of glioma World Health Organization grades II and III. Studies reporting on mean or individual patient data were considered for inclusion. DATA ANALYSIS: Data were analyzed by using inverse variance with the random-effects model and receiver operating characteristic curves describing optimal cutoffs and areas under the curve. Bivariate diagnostic random-effects meta-analysis was used to calculate diagnostic accuracy. DATA SYNTHESIS: Twenty-eight studies evaluating 727 individuals were included in the meta-analysis. Individual data were available from 10 studies comprising 190 individuals. The mean difference of relative maximal CBV between glioma grades II and III (n = 727) was 1.76 (95% CI, 1.27-2.24; P < .001). Individual patient data (n = 190) had an area under the curve of 0.77 for discriminating glioma grades II and III at an optimal cutoff of 2.02. When we analyzed astrocytomas separately, the area under the curve increased to 0.86 but decreased to 0.61 when we analyzed oligodendrogliomas. LIMITATIONS: A substantial heterogeneity was found among included studies. CONCLUSIONS: Glioma grade III had higher relative maximal CBV compared with glioma grade II. A high diagnostic accuracy was found for all patients and astrocytomas; however, the diagnostic accuracy was substantially reduced when discriminating oligodendroglioma grades II and III.

Preoperative Quantitative MR Tractography Compared with Visual Tract Evaluation in Patients with Neuropathologically Confirmed Gliomas Grades II and III: A Prospective Cohort Study.

Background and Purpose. Low-grade gliomas show infiltrative growth in white matter tracts. Diffusion tensor tractography can noninvasively assess white matter tracts. The aim was to preoperatively assess tumor growth in white matter tracts using quantitative MR tractography (3T). The hypothesis was that suspected infiltrated tracts would have altered diffusional properties in infiltrated tract segments compared to noninfiltrated tracts. Materials and Methods. Forty-eight patients with suspected low-grade glioma were included after written informed consent and underwent preoperative diffusion tensor imaging in this prospective review-board approved study. Major white matter tracts in both hemispheres were tracked, segmented, and visually assessed for tumor involvement in thirty-four patients with gliomas grade II or III (astrocytomas or oligodendrogliomas) on postoperative neuropathological evaluation. Relative fractional anisotropy (rFA) and mean diffusivity (rMD) in tract segments were calculated and compared with visual evaluation and neuropathological diagnosis. Results. Tract segment infiltration on visual evaluation was associated with a lower rFA and high rMD in a majority of evaluated tract segments (89% and 78%, resp.). Grade II and grade III gliomas had similar infiltrating behavior. Conclusion. Quantitative MR tractography corresponds to visual evaluation of suspected tract infiltration. It may be useful for an objective preoperative evaluation of tract segment involvement.