Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas.

BACKGROUND: Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging. PURPOSE: To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas. MATERIAL AND METHODS: This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas. RESULTS: Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp-90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05). CONCLUSION: Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.

Diffusion-relaxation correlation spectrum imaging for predicting tumor consistency and gross total resection in patients with pituitary adenomas: a preliminary study.

OBJECTIVE: To evaluate the ability of diffusion-relaxation correlation spectrum imaging (DR-CSI) to predict the consistency and extent of resection (EOR) of pituitary adenomas (PAs). METHODS: Forty-four patients with PAs were prospectively enrolled. Tumor consistency was evaluated at surgery as either soft or hard, followed by histological assessment. In vivo DR-CSI was performed and spectra were segmented following to a peak-based strategy into four compartments, designated A (low ADC), B (mediate ADC, short T2), C (mediate ADC, long T2), and D (high ADC). The corresponding volume fractions ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) along with the ADC and T2 values were calculated and assessed using univariable analysis for discrimination between hard and soft PAs. Predictors of EOR > 95% were analyzed using logistic regression model and receiver-operating-characteristic analysis. RESULTS: Tumor consistency was classified as soft (n = 28) or hard (n = 16). Hard PAs presented higher [Formula: see text] (p = 0.001) and lower [Formula: see text] (p = 0.013) than soft PAs, while no significant difference was found in other parameters. [Formula: see text] significantly correlated with the level of collagen content (r = 0.448, p = 0.002). Knosp grade (odds ratio [OR], 0.299; 95% confidence interval [CI], 0.124-0.716; p = 0.007) and [Formula: see text] (OR, 0.834, per 1% increase; 95% CI, 0.731-0.951; p = 0.007) were independently associated with EOR > 95%. A prediction model based on these variables yielded an AUC of 0.934 (sensitivity, 90.9%; specificity, 90.9%), outperforming the Knosp grade alone (AUC, 0.785; p < 0.05). CONCLUSION: DR-CSI may serve as a promising tool to predict the consistency and EOR of PAs. CLINICAL RELEVANCE STATEMENT: DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs and may serve as a promising tool to predict the tumor consistency and extent of resection in patients with PAs. KEY POINTS: • DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs by visualizing the volume fraction and corresponding spatial distribution of four compartments ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]). • [Formula: see text] correlated with the level of collagen content and may be the best DR-CSI parameter for discrimination between hard and soft PAs. • The combination of Knosp grade and [Formula: see text] achieved an AUC of 0.934 for predicting the total or near-total resection, outperforming the Knosp grade alone (AUC, 0.785).

Temporal changes in plaque characteristics after treatment and their relationship with stroke recurrence: a quantitative study using magnetic resonance imaging.

Background: Intracranial atherosclerotic disease (ICAD) is the major cause of ischemic stroke. Despite aggressive medical therapy, around 15% of patients with ICAD experience recurrence. The aim of the present study was to evaluate the temporal changes in intracranial arteriosclerotic plaques after medical treatment based on vessel wall magnetic resonance imaging (VWMRI) and to explore their relationship with stroke recurrence. Methods: A total of 67 symptomatic patients with ICAD who underwent initial and follow-up VWMRI were recruited into this retrospective cohort study. Stroke recurrence was defined as an ipsilateral stroke symptom after the initial attack. The clinical characteristics and plaque features, including stenosis ratio (measured based on luminal diameter or area), plaque thickness, plaque burden (PB), enhancement ratio (ER), and enhancement grade, were evaluated and compared between the initial and follow-up examinations. Changes in plaque characteristics were compared between patients with or without recurrence by univariable analyses. Multivariable regression was performed to investigate imaging markers for recurrent stroke. Results: The median interval between baseline and follow-up VWMRI was 334 days. A total of 13 cases (19.4%) experienced a stroke recurrence. After treatment, significant decreases in the stenosis ratio (area), PB, and ER were observed in cases without recurrence (all P<0.05), while no significant difference in plaque features was found for cases with recurrence. Univariable analyses showed that changes in stenosis ratio (area), plaque thickness, PB, and ER were significantly different between patients with and without recurrence (all P<0.05). Multivariable regression indicated that PB change was the only significant marker associated with stroke recurrence [odds ratio (OR) =1.112 per 1% increase, 95% confidence interval (CI): 1.010 to 1.224, P=0.031]. Conclusions: Patients with arteriosclerotic plaques who benefit from medical treatment show obvious decreases in stenosis (area), PB, and ER. The progression of PB may serve as an independent marker for predicting stroke recurrence.

Texture Analysis of High b-Value Diffusion-Weighted Imaging for Evaluating Consistency of Pituitary Macroadenomas.

BACKGROUND: Preoperative evaluation of the consistency of pituitary macroadenomas is important for neurosurgeons to prepare the surgical plan. PURPOSE: To evaluate the diagnostic performance of texture analysis (TA) of diffusion-weighted imaging (DWI) at a standard b-value (b = 1000 s/mm2 ) and a high b-value (b = 2000 s/mm2 ) for their ability to assess the tumor consistency of pituitary macroadenomas. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: Fifty patients with histologically confirmed pituitary macroadenomas were classified as soft (n = 37) or hard (n = 13) types. FIELD STRENGTH/SEQUENCE: Coronal T2 -weighted imaging (T2 WI), Readout Segmentation of Long Variable Echo-trains (RESOLVE) DWI at b = 1000 s/mm2 and b = 2000 s/mm2 were acquired with 3.0T MRI. ASSESSMENT: The corresponding apparent diffusion coefficient (ADC) maps (ADC1000 and ADC2000 ) were registered to T2 WI. Regions of interest (ROIs) were manually drawn along the solid part of the tumor from the coregistered T2 WI-ADC images. The texture parameters from T2 WI, ADC1000 , and ADC2000 were acquired. STATISTICAL TESTS: The texture parameters were compared between the two types by using unpaired Student's t-test. Receiver operating characteristic (ROC) curves and logistic regression analyses were used to assess their diagnostic performance. RESULTS: Significant differences in TA parameters of ADC1000 and ADC2000 were observed between soft and hard types (P < 0.05 for all), whereas the TA of T2 WI resulted in no significant difference (P > 0.05 for all). TA of ADC2000 provided a superior diagnostic performance compared with that of ADC1000 (P = 0.038). A combination of mean value and entropy of ADC2000 yielded an AUC, a sensitivity, and a specificity of 0.911, 78.4% and 92.3%, respectively. DATA CONCLUSION: TA of ADC values were useful for assessing the tumor consistency of pituitary macroadenomas. ADC2000 may facilitate better type discrimination. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1507-1513.

Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading.

BACKGROUND: The application of conventional magnetic resonance imaging (MRI) in glioma grading is limited and non-specific. PURPOSE: To investigate the application values of MRI, texture analysis (TA) of dynamic contrast-enhanced MRI (DCE-MRI) and intratumoral susceptibility signal (ITSS) on susceptibility weighted imaging (SWI), alone and in combination, for glioma grading. MATERIAL AND METHODS: Fifty-two patients with pathologically confirmed gliomas who underwent DCE-MRI and SWI were enrolled in this retrospective study. Conventional MRIs were evaluated by the VASARI scoring system. TA of DCE-MRI-derived parameters and the degree of ITSS were compared between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). The diagnostic ability of each parameter and their combination for glioma grading were analyzed. RESULTS: Significant statistical differences in VASARI features were observed between LGGs and HGGs ( P < 0.05), of which the enhancement quality had the highest area under the curve (AUC) (0.873) with 93.3% sensitivity and 80% specificity. The TA of DCE-MRI derived parameters were significantly different between LGGs and HGGs ( P < 0.05), of which the uniformity of Ktrans had the highest AUC (0.917) with 93.3% sensitivity and 90% specificity. The degree of ITSS was significantly different between LGGs and HGGs ( P < 0.001). The AUC of the ITSS was 0.925 with 93.3% sensitivity and 90% specificity. The best discriminative power was obtained from a combination of enhancement quality, Ktrans- uniformity, and ITSS, resulting in 96.7% sensitivity, 100.0% specificity, and AUC of 0.993. CONCLUSION: Combining conventional MRI, TA of DCE-MRI, and ITSS on SWI may help to improve the differentiation between LGGs and HGGs.

Ectopic meningioma in a patient with neurofibromatosis Type 2: a case report and review of the literature.

Ectopic meningioma occurring in the region of parapharyngeal space is rare in clinical practice and brings great challenge in its diagnosis. This report details such a case in a 14-year-old girl with neurofibromatosis Type 2, which is a highly infrequent association. The clinical manifestations, imaging findings, and pathological manifestations are described, and the relevant literature is reviewed to highlight characteristic imaging findings of ectopic meningiomas.