Histogram Analysis Based on Neurite Orientation Dispersion and Density MR Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements.

BACKGROUND: Preoperative differentiation of glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) contributes to guide neurosurgical decision-making. PURPOSE: To explore the value of histogram analysis based on neurite orientation dispersion and density imaging (NODDI) in differentiating between GBM and SBM and comparison of the diagnostic performance of two region of interest (ROI) placements. STUDY TYPE: Retrospective. POPULATION: In all, 109 patients with GBM (n = 57) or SBM (n = 52) were enrolled. FIELD STRENGTH/SEQUENCE: A 3.0 T scanners. T2 -dark-fluid sequence, contrast-enhanced T1 magnetization-prepared rapid gradient echo sequence, and NODDI. ASSESSMENT: ROIs were placed on the peritumoral edema area (ROI1) and whole tumor area (ROI2, included the cystic, necrotic, and hemorrhagic areas). Histogram parameters of each isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and orientation dispersion index (ODI) from NODDI images for two ROIs were calculated, respectively. STATISTICAL TESTS: Mann-Whitney U test, independent t-test, chi-square test, multivariate logistic regression analysis, DeLong's test. RESULTS: For the ROI1 and ROI2, the ICVFmin and ODImean obtained the highest area under curve (AUC, AUC = 0.741 and 0.750, respectively) compared to other single parameters, and the AUC of the multivariate logistic regression model was 0.851 and 0.942, respectively. DeLong's test revealed significant difference in diagnostic performance between optimal single parameter and multivariate logistic regression model within the same ROI, and the multivariate logistic regression models between two different ROIs. DATA CONCLUSION: The performance of multivariate logistic regression model is superior to optimal single parameter in both ROIs based on NODDI histogram analysis to distinguish SBM from GBM, and the ROI placed on the whole tumor area exhibited better diagnostic performance. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Multiple diffusion metrics in differentiating solid glioma from brain inflammation.

Background and purpose: The differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models. Materials and methods: Participants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm2) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated. Results: 57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758). Conclusion: Multiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram.

Objectives: We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs). Materials and Methods: This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong's test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models. Results: The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967). Conclusions: Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.

More Than Just Statics: Temporal Dynamic Changes in Inter- and Intrahemispheric Functional Connectivity in First-Episode, Drug-Naive Patients With Major Depressive Disorder.

Several functional magnetic resonance imaging (fMRI) studies have demonstrated abnormalities in static intra- and interhemispheric functional connectivity among diverse brain regions in patients with major depressive disorder (MDD). However, the dynamic changes in intra- and interhemispheric functional connectivity patterns in patients with MDD remain unclear. Fifty-eight first-episode, drug-naive patients with MDD and 48 age-, sex-, and education level-matched healthy controls (HCs) underwent resting-state fMRI. Whole-brain functional connectivity, analyzed using the functional connectivity density (FCD) approach, was decomposed into ipsilateral and contralateral functional connectivity. We computed the intra- and interhemispheric dynamic FCD (dFCD) using a sliding window analysis to capture the dynamic patterns of functional connectivity. The temporal variability in functional connectivity was quantified as the variance of the dFCD over time. In addition, intra- and interhemispheric static FCD (sFCD) patterns were calculated. Associations between the dFCD variance and sFCD in abnormal brain regions and the severity of depressive symptoms were analyzed. Compared to HCs, patients with MDD showed lower interhemispheric dFCD variability in the inferior/middle frontal gyrus and decreased sFCD in the medial prefrontal cortex/anterior cingulate cortex and posterior cingulate cortex/precuneus in both intra- and interhemispheric comparisons. No significant correlations were found between any abnormal dFCD variance or sFCD at the intra- and interhemispheric levels and the severity of depressive symptoms. Our results suggest intra- and interhemispheric functional connectivity alterations in the dorsolateral prefrontal cortex (DLPFC) and default mode network regions involved in cognition, execution and emotion. Furthermore, our study emphasizes the essential role of altered interhemispheric communication dynamics in the DLPFC in patients with MDD. These findings contribute to our understanding of the pathophysiology of MDD.

Histogram analysis based on diffusion kurtosis imaging: Differentiating glioblastoma multiforme from single brain metastasis and comparing the diagnostic performance of two region of interest placements.

PURPOSE: To assess the value of histogram analysis, using diffusion kurtosis imaging (DKI), in differentiating glioblastoma multiforme (GBM) from single brain metastasis (SBM) and to compare the diagnostic efficiency of different region of interest (ROI) placements. METHOD: Sixty-seven patients with histologically confirmed GBM (n = 35) and SBM (n = 32) were recruited. Two ROIs-the contrast-enhanced area and whole-tumor area-were delineated across all slices. Eleven histogram parameters of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) from both ROIs were calculated. All histogram parameter values were compared between GBM and SBM, using the Mann-Whitney U test. The accuracies of different histogram parameters were compared using the McNemar test. Receiver operating characteristic (ROC) analyses were conducted to assess the diagnostic performance. RESULTS: In the contrast-enhanced area, FA10, FA25, FA75, FA90, FAmean, FAmedian, FAmax, MDmax, MDskewness, and MKskewness were significantly higher for GBM than for SBM. FAskewness was significantly lower for GBM than for SBM. FA25 (0.815) had the highest area under the curve (AUC). In the whole-tumor area, FA10, FA25, FA75, FA90, FASD, FAmean, FAmedian, FAmax, MDmax, MDskewness, and MKskewness were significantly higher for GBM than for SBM. FAmedian (0.805) had the highest AUC. The accuracy of FA25 in the contrast-enhanced area was significantly higher than that of the FAmedian in the whole-tumor area. CONCLUSIONS: GBM and SBM can be differentiated using the DKI-based histogram analysis. Placing the ROI on the contrast-enhanced area results in better discrimination.

Whole-Tumor Histogram Analysis of Multiple Diffusion Metrics for Glioma Genotyping.

Background The isocitrate dehydrogenase (IDH) genotype and 1p/19q codeletion status are key molecular markers included in glioma pathologic diagnosis. Advanced diffusion models provide additional microstructural information. Purpose To compare the diagnostic performance of histogram features of multiple diffusion metrics in predicting glioma IDH and 1p/19q genotyping. Materials and Methods In this prospective study, participants were enrolled from December 2018 to December 2020. Diffusion-weighted imaging was performed by using a spin-echo echo-planar imaging sequence with five b values (500, 1000, 1500, 2000, and 2500 sec/mm2) in 30 directions for every b value and one b value of 0. Diffusion metrics of diffusion-tensor imaging (DTI), diffusion-kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) were calculated, and their histogram features were analyzed in regions that included the entire tumor and peritumoral edema. Comparisons between groups were performed according to IDH genotype and 1p/19q codeletion status. Logistic regression analysis was used to predict the IDH and 1p/19q genotypes. Results A total of 215 participants (115 men, 100 women; mean age, 48 years ± 13 [standard deviation]) with grade II (n = 68), grade III (n = 35), and grade IV (n = 112) glioma were included. Among the DTI, DKI, NODDI, MAP, and total diffusion models, there were no significant differences in the areas under the receiver operating characteristic curve (AUCs) for predicting IDH mutations (AUC, 0.76, 0.82, 0.78, 0.81, and 0.82, respectively; P > .05) and 1p/19q codeletion in gliomas with IDH mutations (AUC, 0.83, 0.81, 0.82, 0.83, and 0.88, respectively; P > .05). A regression model with an R2 value of 0.84 was used for the Ki-67 labeling index and histogram features of the diffusion metrics. Conclusion Whole-tumor histogram analysis of multiple diffusion metrics is a promising approach for glioma isocitrate dehydrogenase and 1p/19q genotyping, and the performance of diffusion-tensor imaging is similar to that of advanced diffusion models. Clinical trial registration no. ChiCTR2100048119 © RSNA, 2021 Online supplemental material is available for this article. An earlier incorrect version appeared online. This article was corrected on December 14, 2021.

Radiomics for the Prediction of Epilepsy in Patients With Frontal Glioma.

OBJECTIVE: This study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE. METHODS: This retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical-radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong's test was used to compare the performance of the models. A two-sided t-test and Fisher's exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p <0.05 was set as the threshold for significance. RESULTS: The classification accuracy of seven scout models, except the wavelet first-order model (0.793) and the wavelet texture model (0.784), was <0.75 in cross-validation. The clinical-radiomics model, including 17 magnetic resonance imaging-based features selected among the 1,130 radiomics features and two clinical features (patient age and tumor grade), achieved better discriminative performance for GAE prediction in both the training [AUC = 0.886, 95% confidence interval (CI) = 0.819-0.940] and testing cohorts (AUC = 0.836, 95% CI = 0.707-0.937) than the radiomics model (p = 0.008) with 82.0% and 78.2% accuracy, respectively. CONCLUSION: Radiomics analysis can non-invasively predict GAE, thus allowing adequate treatment of frontal glioma. The clinical-radiomics model may enable a more precise prediction of frontal GAE. Furthermore, age and pathology grade are important risk factors for GAE.

Mean apparent propagator-MRI: A new diffusion model which improves temporal lobe epilepsy lateralization.

PURPOSE: To compare MRI volume measurements, FLAIR image intensity, Diffusion tensor imaging (DTI) and mean apparent propagator (MAP)-MRI measurements in hippocampus ipsilateral and contralateral to the epileptogenic focus for non-invasive lateralization of temporal lobe epilepsy (TLE) and also compare these DTI and MAP-MRI measurements to cognitive function. METHOD: A cohort of patients with unilateral TLE and aged-and gendered-matched controls were enrolled in this retrospective study. T1-weighted MPRAGE data for the volume, FLAIR image intensity, DTI and MAP-MRI parameters were performed for bilateral hippocampi of all subjects. The sensitivity, specificity, lateralization ratios and Cohen's d effect sizes of all MR measurements were calculated. Pearson correlation analysis was performed to compare DTI and MAP-MRI measurements to cognitive function. RESULTS: We evaluated 23 patients and 17 controls. The MAP-MRI parameter 'return to the plane probability' (RTPP) had the strongest effect size (d = -1.678, lateralization ratio = 86.36 %) for differentiating hippocampus ipsilateral to the epileptogenic focus from contralateral hippocampus when compared to all other DTI/MAP-MRI parameters, signal intensity on FLAIR and hippocampal volumes. Mean diffusivity (MD), radial diffusivity (RD), mean square displacement (MSD) were each negatively correlated to clinical measures of delayed recall (r = -0.758; r = -0.772; r = -0.684, respectively). While return to the axis probability (RTAP) return to the origin probability (RTOP) and fractional anisotropy (FA) were positively correlated (r = 0.832; r = 0.813; r = 0.717, respectively) (all P < 0.05). CONCLUSION: MAP-MRI measurements are promising radiologic biomarkers for the non-invasive lateralization of epileptogenic foci in TLE.

Differentiating skull base chordomas and invasive pituitary adenomas with conventional MRI.

Background It is difficult to distinguish between invasive pituitary adenomas (IPAs) and skull base chordomas based on tumor location and clinical manifestations. Purpose To investigate the value of the apparent diffusion coefficient (ADC), T2-weighted (T2W) imaging, and dynamic contrast enhancement (DCE) in differentiating skull base chordomas and IPAs. Material and Methods Data for 21 patients with skull base chordomas and 27 patients with IPAs involving the paranasal sinus were retrospectively reviewed, and all diagnoses were pathologically confirmed. Each patient underwent conventional 3.0 T magnetic resonance imaging (MRI), including, ADC, T2W imaging, and DCE sequences. Regions of interest were drawn in the mass and in normal white matter on ADC maps and T2W imaging. The mean ADC, normal ADC, T2W imaging signal intensity (SI), and relative T2-weighted (rT2W) imaging values were measured. DCE parameters, including types of time signal-intensity curves (TIC), enhancement peak (EP), and maximum contrast enhancement ratio (MCER), were calculated. Differences between skull base chordomas and IPAs were evaluated using the independent samples t-test. Receiver operating characteristic (ROC) curve analyses were also performed. Results When comparing IPAs and chordomas, there were significant differences in mean ADC, normal ADC, rT2W imaging values, TIC, EP, and MCER ( P < 0.01). The areas under curves in the ROC analyses for normal ADC, mean ADC, T2W imaging, rT2W imaging, TIC, EP, and MCER were 1.0, 0.996, 1.0, 0.81, 0.987, and 0.987, respectively. Conclusion ADC, T2W imaging SI, and DCE-related parameters can contribute to the differential diagnosis of skull base chordomas and IPAs.

A Study on Clinical Characteristics and Magnetic Resonance Imaging Manifestations on Systemic Rosai-Dorfman Disease.

BACKGROUND:: Rosai-Dorfman disease (RDD) is typically characterized by painless bilateral and symmetrical cervical lymphadenopathy, with associated fever and leukocytosis. The aim of the current study was to summarize the clinical features and imaging characteristics of RDD, in an effort to improve its diagnostic accuracy. METHODS: The study was analyzed from 32 patients between January 2011 and December 2017; of these, 16 patients had pathologically diagnosed RDD, eight had pathologically diagnosed meningioma, and eight pathologically diagnosed lymphoma. All patients underwent computed tomography and magnetic resonance imaging (MRI). Clinical features and imaging characteristics of RDD were analyzed retrospectively. The mean apparent diffusion coefficient (ADC) values of lesions at different sites were measured, and one-way analysis of variance and the least significant difference t-test were used to compare the differences between groups and draw receiver operating characteristic curves. The tumors were excised for biopsy and analyzed using immunohistochemistry. RESULTS:: The mean ADCs were (0.81 ± 0.10) × 10-3 mm2/s for intercranial RDD, (0.73 ± 0.05) × 10-3 mm2/s for nasopharyngeal RDD, (0.74 ± 0.11) × 10-3 mm2/s for bone RDD, and (0.71 ± 0.04) × 10-3 mm2/s for soft-tissue RDD. The optimum ADC to distinguish intracranial RDD from lymphoma was 0.79 × 10-3 mm2/s (62.5% sensitivity and 100% specificity) and to distinguish meningioma from intracranial RDD was 0.92 × 10-3 mm2/s (62.5% sensitivity and 100% specificity). Levels of C-reactive protein, erythrocyte sediment rate and D-dimer were significantly elevated (81%, 87%, and 75%, respectively). On immunohistochemistry, RDD was positive for both S-100 and CD68 proteins but negative for CD1a. CONCLUSIONS:: Conventional MRI, combined with diffusion-weighted imaging and ADC mapping, is an important diagnostic tool in evaluating RDD patients. An accurate diagnosis of RDD should consider the clinical features, imaging characteristics, and the pathological findings.