CEST MRI provides amide/amine surrogate biomarkers for treatment-naïve glioma sub-typing.

PURPOSE: Accurate glioma classification affects patient management and is challenging on non- or low-enhancing gliomas. This study investigated the clinical value of different chemical exchange saturation transfer (CEST) metrics for glioma classification and assessed the diagnostic effect of the presence of abundant fluid in glioma subpopulations. METHODS: Forty-five treatment-naïve glioma patients with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status received CEST MRI (B1rms = 2µT, Tsat = 3.5 s) at 3 T. Magnetization transfer ratio asymmetry and CEST metrics (amides: offset range 3-4 ppm, amines: 1.5-2.5 ppm, amide/amine ratio) were calculated with two models: 'asymmetry-based' (AB) and 'fluid-suppressed' (FS). The presence of T2/FLAIR mismatch was noted. RESULTS: IDH-wild type had higher amide/amine ratio than IDH-mutant_1p/19qcodel (p < 0.022). Amide/amine ratio and amine levels differentiated IDH-wild type from IDH-mutant (p < 0.0045) and from IDH-mutant_1p/19qret (p < 0.021). IDH-mutant_1p/19qret had higher amides and amines than IDH-mutant_1p/19qcodel (p < 0.035). IDH-mutant_1p/19qret with AB/FS mismatch had higher amines than IDH-mutant_1p/19qret without AB/FS mismatch ( < 0.016). In IDH-mutant_1p/19qret, the presence of AB/FS mismatch was closely related to the presence of T2/FLAIR mismatch (p = 0.014). CONCLUSIONS: CEST-derived biomarkers for amides, amines, and their ratio can help with histomolecular staging in gliomas without intense contrast enhancement. T2/FLAIR mismatch is reflected in the presence of AB/FS CEST mismatch. The AB/FS CEST mismatch identifies glioma subgroups that may have prognostic and clinical relevance.

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.

BACKGROUND: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. METHODS: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. RESULTS: Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1). CONCLUSIONS: Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.

The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis.

PURPOSE: We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas. METHODS: A review protocol was developed according to the (PRISMA-P) checklist, registered in the international prospective register of systematic reviews (PROSPERO) and published. A literature search in 4 databases was performed using the keywords 'glioma' and 'diffusional kurtosis'. After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool and data extraction was done. Reported sensitivities and specificities were used to construct 2 × 2 tables and paired forest plots using the Review Manager (RevMan®) software. A random-effect model was pursued using the hierarchical summary receiver operator characteristics. RESULTS: A total of 216 hits were retrieved. Considering duplicates and inclusion criteria, 23 articles were eligible for full-text reading. Ultimately, 19 studies were eligible for final inclusion. The quality assessment revealed 9 studies with low risk of bias in the 4 domains. Using a bivariate random-effect model for data synthesis, summary ROC curve showed a pooled area under the curve (AUC) of 0.92 and estimated sensitivity of 0.87 (95% CI 0.78-0.92) in high-/low-grade gliomas' differentiation. A mean difference in mean kurtosis (MK) value between HGG and LGG of 0.22 (95% CI 0.25-0.19) was illustrated (p value = 0.0014) with moderate heterogeneity (I2 = 73.8%). CONCLUSION: DKI shows good diagnostic accuracy in the differentiation of high- and low-grade gliomas further supporting its potential role in clinical practice. Further exploration of DKI in differentiating IDH status and in characterising non-glioma CNS tumours is however needed.

Facet Inclinations and Interfacet Angle of the Distal Radius on Posteroanterior Radiographs.

PURPOSE: To determine the lunate facet inclination (LFI), scaphoid facet inclination (SFI), and interfacet angle (IFA) of the distal radius on posteroanterior (PA) radiographs; evaluate the reliability of the IFA measurements; and define normative reference values for all 3 parameters. METHODS: The IFA was defined as the angle between the lines tangential to the scaphoid and the lunate facets. The reliability of the IFA measurements was investigated using 2 serial measurements made by 3 observers. Three parameters (the IFA, LFI, and SFI) were measured on PA wrist radiographs of 400 normal Caucasians. Between-side and -sex differences among the 3 parameters were analyzed statistically. RESULTS: The inter- and intraobserver reliability of the IFA measurements was excellent. The mean values were as follows: IFA, 20°; LFI, 14°; and SFI, 34°. Although no statistically significant difference was found between the right and the left wrists, sex-based analyses revealed significant differences between the wrists of women and men. Based on the standard distribution of IFAs, 3 groups of distal radii were defined as follows: slightly, moderately, and steeply angled. CONCLUSIONS: The LFI, SFI, and IFA are easily measured radiographic parameters of the distal radius. Although a moderate correlation was evident between the IFA and the LFI, the IFA is a novel parameter to evaluate the carpal articular shape of the distal radius. The IFA measurement on PA radiographs is reliable. CLINICAL RELEVANCE: The LFI has been accepted as a parameter for Madelung deformity and radiocarpal force transmission. The IFA may be considered as a parameter to evaluate radiocarpal coronal stability that could potentially be affected by changes in bifacet curvature.

Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis.

BACKGROUND: T1-weighted dynamic contrast-enhanced (DCE) perfusion magnetic resonance imaging (MRI) has been broadly utilized in the evaluation of brain tumors. We aimed at assessing the diagnostic accuracy of DCE-MRI in discriminating between low-grade gliomas (LGGs) and high-grade gliomas (HGGs), between tumor recurrence and treatment-related changes, and between primary central nervous system lymphomas (PCNSLs) and HGGs. METHODS: We performed this study based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy Studies criteria. We systematically surveyed studies evaluating the diagnostic accuracy of DCE-MRI for the aforementioned entities. Meta-analysis was conducted with the use of a random effects model. RESULTS: Twenty-seven studies were included after screening of 2945 possible entries. We categorized the eligible studies into three groups: those utilizing DCE-MRI to differentiate between HGGs and LGGs (14 studies, 546 patients), between recurrence and treatment-related changes (9 studies, 298 patients) and between PCNSLs and HGGs (5 studies, 224 patients). The pooled sensitivity, specificity, and area under the curve for differentiating HGGs from LGGs were 0.93, 0.90, and 0.96, for differentiating tumor relapse from treatment-related changes were 0.88, 0.86, and 0.89, and for differentiating PCNSLs from HGGs were 0.78, 0.81, and 0.86, respectively. CONCLUSIONS: Dynamic contrast-enhanced-Magnetic resonance imaging is a promising noninvasive imaging method that has moderate or high accuracy in stratifying gliomas. DCE-MRI shows high diagnostic accuracy in discriminating between HGGs and their low-grade counterparts, and moderate diagnostic accuracy in discriminating recurrent lesions and treatment-related changes as well as PCNSLs and HGGs.

Role of diffusional kurtosis imaging in grading of brain gliomas: a protocol for systematic review and meta-analysis.

INTRODUCTION: Central nervous system (CNS) gliomas are the most common primary intra-axial brain tumours and pose variable treatment response according to their grade, therefore, precise staging is mandatory. Histopathological analysis of surgical tumour samples is still deemed as the state-of-the-art staging technique for gliomas due to the moderate specificity of the available non-invasive imaging modalities. A recently evolved analysis of the tissue water diffusion properties, known as diffusional kurtosis imaging (DKI), is a dimensionless metric, which quantifies water molecules' degree of non-Gaussian diffusion, hence reflects tissue microenvironment's complexity by means of non-invasive diffusion-weighted MRI acquisitions. The objective of this systematic review and meta-analysis is to explore the performance of DKI in the presurgical grading of gliomas, both regarding the differentiation between high-grade and low-grade gliomas as well as the discrimination between gliomas and other intra-axial brain tumours. METHODS AND ANALYSIS: We will search PubMed, Medline via Ovid, Embase and Scopus in July 2018 for research studies published between January 1990 and June 2018 with no language restrictions, which have reported on the performance of DKI in diagnosing CNS gliomas. Robust inclusion/exclusion criteria will be applied for selection of eligible articles. Two authors will separately perform quality assessment according to the quality assessment of diagnostic accuracy studies-2 tool. Data will be extracted in a predesigned spreadsheet. A meta-analysis will be held using a random-effects model if substantial statistical heterogeneity is expected. The heterogeneity of studies will be evaluated, and sensitivity analyses will be conducted according to individual study quality. ETHICS AND DISSEMINATION: This work will be based on published studies; hence, it does not require institutional review board approval or ethics clearance. The results will be published in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42018099192.

Use of Diffusion Weighted Imaging in Differentiating Between Maligant and Benign Meningiomas. A Multicenter Analysis.

BACKGROUND: Meningioma is the most frequent intracranial tumor and is often an incidental finding on imaging. Some imaging-based scores were suggested for differentiating low- and high-grade meningiomas. The purpose of this work was to compare diffusion-weighted imaging findings of different meningiomas in a large multicenter study by using apparent diffusion coefficient (ADC) values for predicting tumor grade and proliferation potential. METHODS: Data from 7 radiologic departments were acquired retrospectively. Overall, 389 patients were collected. All meningiomas were investigated by magnetic resonance imaging (1.5-T scanner) by using diffusion-weighted imaging (b values of 0 and 1000 s/mm(2)). The comparison of ADC values was performed by Mann-Whitney U test. RESULTS: World Health Organization grade I was diagnosed in 271 cases (69.7%), grade II in 103 (26.5%), and grade III in 15 patients (3.9%). Grade I meningiomas showed statistically significant higher ADC values (1.05 ± 0.39 × 10(-3) mm(2)s(-1)) in comparison with grade II (0.77 ± 0.15 × 10(-3) mm(2)s(-1); P = 0.001) and grade III tumors (0.79 ± 0.21 × 10(-3) mm(2)s(-1); P = 0.01). An ADC value of <0.85 × 10(-3) mm(2)s(-1) was determined as the threshold in differentiating between grade I and grade II/III meningiomas (sensitivity, 72.9%; specificity, 73.1%; accuracy, 73.0%). Ki67 was associated with ADC (r = -0.63, P < 0.001). The optimal threshold for the ADC was (less than) 0.85 × 10(-3) mm(2)s(-1) for detecting tumors with high proliferation potential (Ki67 ≥5%). CONCLUSIONS: The estimated threshold ADC value of 0.85 can differentiate grade I meningioma from grade II and III tumors. The same ADC value is helpful for detecting tumors with high proliferation potential.

CT assessment of asymptomatic hip joints for the background of femoroacetabular impingement morphology.

PURPOSE: The purposes of this study were to assess the presence of cam and pincer morphology in asymptomatic individuals with a negative femoroacetabular impingement test, and to determine and compare the ranges of alpha angle using two measurement methods. MATERIALS AND METHODS: In total, 68 consecutive patients who underwent abdominopelvic computed tomography (CT) for reasons other than hip problems were the patient population. Patients who had a positive femoroacetabular impingement test were excluded. Alpha angle measurements from axial oblique (AN) and radial reformat-based images (AR) from the anterior through the superior portion of the femoral head-neck junction, as well as femoral head-neck offset, center-edge angle, acetabular version angle measurements, and acetabular crossover sign assessment, were made. RESULTS: Overall prevalences of cam (increased alpha angle, decreased femoral head-neck offset) and pincer morphology (increased center-edge angle, decreased acetabular version) were 20.0%, 26.8%, 25.8%, and 10.2% of the hips, respectively. The mean AR ranged from 41.64° ± 4.23° to 48.13° ± 4.63°, whereas AN was 41.10° ± 4.44°. The values of AR were higher than AN, and the difference was statistically significant (P <0.001). The highest AR values were measured on images from the anterosuperior section of femoral head-neck junction. CONCLUSION: In asymptomatic subjects, higher alpha angle values were obtained from radial reformatted images, specifically from the anterosuperior portion of the femoral head-neck junction compared with the axial oblique CT images. Other measurements used for the assessment of cam and pincer morphology can also be beyond the ranges that are considered normal in the general population.

Tract-based spatial statistics of diffusion tensor imaging in hereditary spastic paraplegia with thin corpus callosum reveals widespread white matter changes.

PURPOSE: We aimed to investigate white matter diffusivity abnormalities in hereditary spastic paraplegia with thin corpus callosum (HSP-TCC) patients in relation with electrophysiological findings. MATERIALS AND METHODS: Brain magnetic resonance imaging (MRI) and diffusion tensor imaging were performed on four HSP-TCC patients and 15 age-matched healthy subjects. Voxel-wise statistical analysis of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity maps were carried out using tract-based spatial statistics, and significantly affected voxels were labeled using a human white matter atlas. Conventional nerve conduction studies, cortical and spinal-root motor evoked potentials, and somatosensory evoked potentials were examined in three patients. RESULTS: On MRI, all patients had a thin corpus callosum with mild T2 hyperintensity in the periventricular white matter. Compared to control subjects, we detected widespread significant decreases in fractional anisotropy, and increases in axial diffusivity, radial diffusivity, and mean diffusivity in structures including in the corpus callosum, motor, and non-motor white matter tracts in HSP-TCC patients. Several different regions showed significant reduction in axial diffusivity. Electrophysiological studies revealed prolonged central motor conduction times and reduced cortical motor evoked potentials and somatosensory evoked potentials amplitudes in all patients. One patient had low sural sensory nerve action potential suggestive of axonal neuropathy. CONCLUSION: Tract-based spatial statistics of diffusion tensor imaging revealed a more widespread involvement of white matter in HSP-TCC patients than has previously been detected by conventional MRI. This may explain the broad spectrum of electrophysiological and neurological abnormalities that complicate hereditary spastic paraplegia in these patients.