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Pediatric high grade gliomas have undergone remarkable changes in recent time with discovery of new molecular pathways. They have been added separately in current WHO 2021 blue book. All the entities show characteristic morphology and immunohistochemistry. Methylation data correctly identifies these entities into particular group of clusters. The pediatric group high grade glioma comprises- Diffuse midline glioma, H3K27-altered; Diffuse hemispheric glioma, H3G34-mutant; Diffuse pediatric-type high-grade glioma, H3-wild type & IDH-wild type; Infant hemispheric glioma and Epithelioid glioblastoma/Grade 3 pleomorphic xanthoastrocytoma and very rare IDH-mutant astrocytoma. However it is not always feasible to perform these molecular tests where cost-effective diagnosis is a major concern. Here we discuss the major entities with their characteristic histopathology, immunohistochemistry and molecular findings that may help to reach to suggest the diagnosis and help the clinician for appropriate treatment strategies. We have also made a simple algorithmic flow chart integrated with histopathology, immunohistochemistry and molecular characteristics for better understanding., Competing Interests: Declaration of Competing Interest There are no declaration of interest from the authors., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

Rosette-forming glioneuronal tumor (RGNT) of the 4th ventricle is a newly described WHO grade I brain tumor included in recent WHO classification of CNS tumors. It is a biphasic tumor thought to originate from pluripotent progenitor cells of subependymal plate. Intra-operative diagnosis plays an important role, as complete surgical excision is the treatment of choice. We are reporting a case of RGNT in a 19 years-old young male emphasizing the intra-operative pathological pointers and their role in accurate diagnosis for the suitable surgical intervention.

The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex vivo experimentation of radiomics analysis of T1-weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on radiomics features. This retrospective study involved magnetic resonance (MR) images acquired using a 3.0-T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1-dynamic contrast-enhanced MR imaging. Statistically significant (< 0.05, independent samples T-test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1-weighted cell line images but group comparison was carried out using one-way analysis of variance. Further, a random forest (RF)-based machine learning model was designed to classify the PVE of GB and BM. Texture-based variations, especially higher nonuniformity values, were observed in the PVE of GB. No significance was observed between BM and meningioma PVE. In cell line images, the culture medium had higher nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future., (© 2022 John Wiley & Sons Ltd.)

Background and Purpose: Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T 1 perfusion MRI parameters combined under a machine learning framework., Materials and Methods: This retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T 1 perfusion and DWI data along with conventional MRI images. Analysis of T 1 perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T 1 perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T 1 perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter., Results: T 1 perfusion parameter values were significantly different between the two groups. The combination of T 1 perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error., Conclusion: The approach of using quantitative T 1 perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Glioblastoma (GB) is among the most devastative brain tumors, which usually comprises sub-regions like enhancing tumor (ET), non-enhancing tumor (NET), edema (ED), and necrosis (NEC) as described on MRI. Semi-automated algorithms to extract these tumor subpart volumes and boundaries have been demonstrated using dynamic contrast-enhanced (DCE) perfusion imaging. We aim to characterize these sub-regions derived from DCE perfusion MRI using routine 3D post-contrast-T1 (T1GD) and FLAIR images with the aid of Radiomics analysis. We also explored the possibility of separating edema from tumor sub-regions by extracting the most influential radiomics features., Methods: A total of 89 patients with histopathological confirmed IDH wild type GB were considered, who underwent the MR imaging with DCE perfusion-MRI. Perfusion and kinetic indices were computed and further used to segment tumor sub-regions. Radiomics features were extracted from FLAIR and T1GD images with PyRadiomics tool. Statistical analysis of the features was carried out using two approaches as well as machine learning (ML) models were constructed separately, i) within different tumor sub-regions and ii) ED as one category and the remaining sub-regions combined as another category. ML based predictive feature maps was also constructed., Results: Seven features found to be statistically significant to differentiate tumor sub-regions in FLAIR and T1GD images, with p-value < 0.05 and AUC values in the range of 0.72 to 0.93. However, the edema features stood out in the analysis. In the second approach, the ML model was able to categorize the ED from the rest of the tumor sub-regions in FLAIR and T1GD images with AUC of 0.95 and 0.89 respectively., Conclusion: Radiomics-based specific feature values and maps help to characterize different tumor sub-regions. However, the GLDM&#95;DependenceNonUniformity feature appears to be most specific for separating edema from the remaining tumor sub-regions using conventional FLAIR images. This may be of value in the segmentation of edema from tumors using conventional MRI in the future., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Purpose: Primary objective of this study was to retrospectively evaluate the potential of a range of qualitative and quantitative multiparametric features assessed on T 2 , post-contrast T 1 , DWI, DCE-MRI, and susceptibility-weighted-imaging (SWI) in differentiating evenly sampled cohort of primary-central-nervous-system-lymphoma (PCNSL) vs glioblastoma (GB) with pathological validation., Methods: The study included MRI-data of histopathologically confirmed ninety-five GB and PCNSL patients scanned at 3.0 T MRI. A total of six qualitative features (three from T 2 and post-contrast T 1 , three from SWI: thin-linear-uninterrupted-intra-tumoral-vasculature, broken-intra-tumoral-microvasculature, hemorrhage) were analyzed by three independent radiologists. Ten quantitative features from DWI and DCE-MRI were computed using in-house-developed algorithms. For qualitative features, Cohen's Kappa-interrater-variability-analysis was performed. Z-test and independent t-tests were performed to find significant qualitative and quantitative features respectively. Logistic-regression (LR) classifiers were implemented for evaluating performance of individual and various combinations of features in differentiating PCNSL vs GB. Performance evaluation was done via ROC-analysis. Pathological validation was performed to verify disintegration of vessel walls in GB and rim of viable neoplastic lymphoid cells with angiocentric-pattern in PCNSL., Results: Three qualitative SWI features and four quantitative DCE-MRI features (rCBVcorr, K ep , V e , and necrosis-volume-percentage) were significantly different (p < 0.05) between PCNSL and GB. Best diagnostic performance was observed with LR classifier using SWI features (AUC-0.99). The inclusion of quantitative features with SWI feature did not improve the differentiation accuracy., Conclusions: The combination of three qualitative SWI features using LR provided the highest accuracy in differentiating PCNSL and GB. Thin-linear-uninterrupted-intra-tumoral-vasculature in PCNSL and broken-intra-tumoral-microvasculature with hemorrhage in GB are the major contributors to the differentiation., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2-FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics-based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1-contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 ± 42 days before recurrence using machine learning. T1CE MRI first-order and gray-level co-occurrence matrix features are crucial in detecting local recurrence, while multimodal gray-level difference matrix and first-order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics-based phenotypic changes that may potentially serve as MR-based biomarkers for customized therapeutic intervention., (© 2021 John Wiley & Sons, Ltd.)

Rationale and Objectives: To comprehensively evaluate robustness and variations of DCE-MRI derived generalized-tracer-kinetic-model (GTKM) parameters in healthy and tumor tissues and impact of normalization in mitigating these variations on application to glioma., Materials (patients) and Methods: A retrospective study included pre-operative 31 high-grade-glioma(HGG), 22 low-grade-glioma(LGG) and 33 follow-up data from 10 patients a prospective study with 4 HGG subjects. Voxel-wise GTKM was fitted to DCE-MRI data to estimate K trans , v e , v b . Simulations were used to evaluate noise sensitivity. Variation of parameters with-respect-to arterial-input-function (AIF) variation and data length were studied. Normalization of parameters with-respect-to mean values in gray-matter (GM) and white-matter (WM) regions (GM-Type-2, WM-Type-2) and mean curves (GM-Type-1, WM-Type-1) were also evaluated. Co-efficient-of-variation(CoV), relative-percentage-error (RPE), Box-Whisker plots, bar graphs and t-test were used for comparison., Results: GTKM was fitted well in all tissue regions. K trans and v e in contrast-enhancing (CE) has shown improved noise sensitivity in longer data. v b was reliable in all tissues. Mean AIF and C(t) peaks showed ~38% and ~35% variations. During simulation, normalizations have mitigated variations due to changes in AIF amplitude in K trans and v b . . v e was less sensitive to normalizations. CoV of K trans and v b has reduced ~70% after GM-Type-1 normalization and ~80% after GM-Type-2 normalization, respectively. GM-Type-1 (p = 0.003) and GM-Type-2 (p = 0.006) normalizations have significantly improved differentiation of HGG and LGG using K trans ., Conclusion: K trans and v b can be reliably estimated in normal-appearing brain tissues and can be used for normalization of corresponding parameters in tumor tissues for mitigating inter-subject variability due to errors in AIF. Normalized K trans and v b provided improved differentiation of HGG and LGG., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: This retrospective study was performed on a 3T MRI to determine the unique conventional MR imaging and T1-weighted DCE-MRI features of oligodendroglioma and astrocytoma and investigate the utility of machine learning algorithms in their differentiation., Methods: Histologically confirmed, 81 treatment-naïve patients were classified into two groups as per WHO 2016 classification: oligodendroglioma (n = 16; grade II, n = 25; grade III) and astrocytoma (n = 10; grade II, n = 30; grade III). The differences in tumor morphology characteristics were evaluated using Z-test. T1-weighted DCE-MRI data were analyzed using an in-house built MATLAB program. The mean 90th percentile of relative cerebral blood flow, relative cerebral blood volume corrected, volume transfer rate from plasma to extracellular extravascular space, and extravascular extracellular space volume values were evaluated using independent Student's t test. Support vector machine (SVM) classifier was constructed to differentiate two groups across grade II, grade III, and grade II+III based on statistically significant features., Results: Z-test signified only calcification among conventional MR features to categorize oligodendroglioma and astrocytoma across grade III and grade II+III tumors. No statistical significance was found in the perfusion parameters between two groups and its subtypes. SVM trained on calcification also provided moderate accuracy to differentiate oligodendroglioma from astrocytoma., Conclusion: We conclude that conventional MR features except calcification and the quantitative T1-weighted DCE-MRI parameters fail to discriminate between oligodendroglioma and astrocytoma. The SVM could not further aid in their differentiation. The study also suggests that the presence of more than 50% T2-FLAIR mismatch may be considered as a more conclusive sign for differentiation of IDH mutant astrocytoma., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

Chemical exchange saturation transfer (CEST) MRI is a non-invasive molecular imaging technique with potential applications in pre-clinical and clinical studies. Applications of amide proton transfer-weighted (APT-w), glutamate-weighted (Glu-w) and creatine-weighted (Cr-w) CEST, among others, have been reported. In general, CEST data are acquired at multiple offset-frequencies. In reported studies, different offset-frequency step sizes and interpolation methods have been used during B 0 inhomogeneity correction of data. The objective of the current study was to evaluate the effects of different step sizes and interpolation methods on CEST value computation. In the current study, simulation (Glu-w, Cr-w and APT-w) and experimental data from the brain were used. Experimental CEST data (Glu-w) were acquired from human volunteers at 7 T and brain tumor patients (APT-w) at 3 T. During B 0 inhomogeneity correction, different interpolation methods (polynomial [degree-1, 2 and 3], cubic-Hermite, cubic-spline and smoothing-spline) were compared. CEST values were computed using asymmetry analysis. The effects of different step sizes and interpolation methods were evaluated using coefficient of variation (CV), normalized mean square error (nMSE) and coefficient of correlation parameters. Additionally, an optimum interpolation method for APT-w values was selected based upon fitting accuracy, T-test, receiver operating characteristic analysis, and its diagnostic performance in differentiating low-grade and high-grade tumors. CV and nMSE increase with an increase in step size irrespective of the interpolation method (except for cubic-Hermite and cubic-spline). The nMSE of Cr-w and Glu-w CEST values were least for polynomial (degree-2 and 3). The quality of Glu-w CEST maps became coarse with the increase in step size. There was a significant difference (P < .05) between low-grade and high-grade tumors using polynomial interpolation (degree-1, 2 and 3); however, linear interpolation outperforms other methods for APT-w data, providing the highest sensitivity and specificity. In conclusion, depending upon the saturation parameters and field strength, optimization of step size and interpolation should be carried out for different CEST metabolites/molecules. Glu-w, Cr-w and APT-w CEST data should be acquired with a step size of between 0.2 and 0.3 ppm. For B 0 inhomogeneity correction, polynomial (degree-2) should be used for Glu-w and Cr-w CEST data at 7 T and linear interpolation should be used for APT-w data at 3 T for a limited frequency range., (© 2021 John Wiley & Sons, Ltd.)

Purpose: T1-contrast and T2-flair images of magnetic resonance imaging (MRI) are commonly fused with computed tomography (CT) and used for delineation of postoperative residual tumor and bed after surgery in patients with glioblastoma multiforme (GBM). Our prospective study was aimed to see the feasibility of incorporating perfusion MRI in delineation of brain tumor for radiotherapy planning and its implication on treatment volumes., Methods: Twenty-four patients with histopathologically proven GBM were included in the study. All patients underwent radiotherapy planning with a contrast CT scan. In addition to radiotherapy (RT) planning protocol, T1-perfusion MRI was also done in all patients in the same sitting. Perfusion imaging was processed on the in-house-developed JAVA-based software. The images of CT and MRI were sent to the iPlan planning system (Brainlab AG, GmbH) using a Digital Imaging and Communications in Medicine - Radiation Therapy (DICOM-RT) protocol. A structure of gross tumor volume (GTV)-perfusion (GTV-P) was delineated based only on the MRI perfusion images. Subsequently, GTV-P and GTV were fused together to make GTV-summated (GTV-S). Using existing guidelines, GTV-S was expanded to form clinical target volume-summated (CTV-S) and planning target volume-summated (PTV-S). The increment in each of the summated volumes as compared to baseline volume was noted. The common overlap volume (GTVO) between GTV and GTV-P was calculated using intersection theory (GTV n GTV-P = GTVO [Overlap])., Results: Mean ± standard deviation (cc) for GTV, GTV-P, and GTVO was 46.3 ± 33.4 cc (range: 5.2 cc-108.0 cc), 26.0 ± 26.2 (range: 6.6 cc-10.3.0 cc), and 17.5 ± 22.3 cc (range: 10.0 cc-92 cc), respectively. Median volume (cc) for GTV, GTV-P, and GTVO was 40.8 cc, 17.2 cc, and 8.0 cc, respectively. Mean absolute and relative increments from GTV to that of GTV-S were 8.5 ± 8.2 cc and 27.2 ± 30.9%, respectively. Average CTV volume (cc) was 230.4 ± 115.3 (range: 80.8 cc-442.0 cc). Mean and median CTV-S volumes were 262.0 ± 126.3 cc (range: 80.8 cc-483.0 cc) and 221.0 cc, respectively. The increment in the mean CTV volume (with respect to CTV created from GTV-S) was 15.2 ± 15.9%. Mean and median PTV volumes created on the summated CTV were 287.1 ± 134.0 cc (range: 118.9 cc-576.0 cc) and 258.0 cc, respectively. Absolute and relative increments in PTV volume, while incorporating the perfusion volume, were 31.3 ± 28.9 cc and 12.5 ± 13.3%, respectively. Out of the total of 24 patients, perfusion scanning did not do any increment in GTV in five patients., Conclusions: Our study is the first to present the feasibility and the outcome of contouring on perfusion imaging and its overlay on regular MRI images. The implications of this on long-term outcome and control rates of glioblastoma patients need to be seen in future studies., Competing Interests: None

Purpose: To evaluate the efficacy of optimized T1-Perfusion MRI protocol (protocol-2) with whole brain coverage and improved spatial resolution using Compressed-SENSE (CSENSE) to differentiate high-grade-glioma (HGG) and low-grade-glioma (LGG) and to compare it with the conventional protocol (protocol-1) with partial brain coverage used in our center., Methods: This study included MRI data from 5 healthy volunteers, a phantom and 126 brain tumor patients. Current study had two parts: To analyze the effect of CSENSE on 3D-T1-weighted (W) fast-field-echo (FFE) images, T1-W, dual-PDT2-W turbo-spin-echo images and T1 maps, and to evaluate the performance of high resolution T1-Perfusion MRI protocol with whole brain coverage optimized using CSENSE. Coefficient-of-Variation (COV), Relative-Percentage-Error (RPE), Normalized-Mean-Squared-Error (NMSE) and qualitative scoring were used for the former study. Tracer-kinetic (K trans ,v e ,v p ) and hemodynamic (rCBV,rCBF) parameters computed from both protocols were used to differentiate LGG and HGG., Results: The image quality of all structural images was found to be of diagnostic quality till R = 4. NMSE in healthy T1-W-FFE images and COV in phantom images increased with-respect-to R and images provided optimum quality till R = 4. Structural images and maps exhibited artefacts from R = 6. All parameters in tumor tissue and hemodynamic parameters in healthy gray matter tissue computed from both protocols were not significantly different. Parameters computed from protocol-2 performed better in terms of glioma grading. For both protocols, rCBF performed least (AUC = 0.759 and 0.851) and combination of all parameters performed best (AUC = 0.890 and 0.964)., Conclusion: CSENSE (R = 4) can be used to improve the resolution and brain coverage for T1-Perfusion analysis used to differentiate gliomas., Competing Interests: Declaration of competing interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: To evaluate the role of amide proton transfer-weighted (APT-w) magnetic resonance imaging (MRI) in differentiating neoplastic and infective mass lesions using different contrast normalizations, region of interest (ROI) selection, and histogram analysis., Procedures: Retrospective study included 32 treatment-naive patients having intracranial mass lesions (ICMLs): low-grade glioma (LGG) = 14, high-grade glioma (HGG) = 10, and infective mass lesions = 8. APT-w MRI images were acquired along with conventional MRI images at 3 T. APT-w contrast, corrected for B 0 -field inhomogeneity, was computed and optimized with respect to different types of normalizations. Different ROIs on lesion region were selected followed by ROI analysis and histogram analysis. Statistical analysis was performed using Shapiro-Wilk's test, t tests, ANOVA with Tukey's post hoc test, and receiver operation characteristic (ROC) analysis., Results: ICMLs showed significantly (p < 0.01) higher APT-w contrast in lesion compared with contralateral side. There was a substantial overlap between mean APT-w contrast of neoplastic and infective mass lesions as well as among different groups of ICMLs irrespective of ROI selection and normalizations. APT-w contrast (using type 4 normalization: normalized with reference signal at negative offset frequency and APT-w contrast in normal-appearing white matter) reduced variability of APT-w contrast across different subjects, and overlap was less compared with other types of normalizations. There was a significant difference (p < 0.05) between neoplastic and infective mass lesions using t test for different histogram parameters of type 4 normalized APT-w contrast. ANOVA with post hoc showed significant difference (p < 0.05) for different histogram parameters of APT-w contrast (Type 4 normalization) between LGG and HGG, LGG, and infective mass lesion. Histogram parameters such as standard deviation, mean of top percentiles, and median provided improved differentiation between neoplastic and infective mass lesions compared with mean APT-w contrast. A greater number of histogram parameters of type 4 normalized APT-w contrast corresponding to active lesion region can significantly differentiate between ICMLs than other types of normalizations and ROIs., Conclusions: APT-w contrast using type 4 normalization and active lesion region (ROI-2) should be used for studying APT. APT-MRI should be combined with other MRI techniques to further improve the differential diagnosis of ICMLs.

Background: Susceptibility weighted imaging (SWI) provides vascular information and plays an important role in improving the diagnostic accuracy of preoperative glioma grading. Intratumoral susceptibility signal intensities (ITSS) obtained from SWI has been used in glioma grading. However, the current method for estimation of ITSS is semiquantitative, manual count-dependent, and includes hemorrhage as well as vasculature., Purpose: To develop a quantitative approach that calculates the vasculature volume within tumors by filtering out the hemorrhage from ITSS using R 2 * values and connected component analysis-based segmentation algorithm; to evaluate the accuracy of the proposed ITSS vasculature volume (IVV) for differentiating various grades of glioma; and compare it with reported semiquantitative ITSS approach., Study Type: Retrospective., Subjects: Histopathologically confirmed 41 grade IV, 19 grade III, and 15 grade II glioma patients.Field Strength/Sequence: SWI (four echoes: 5.6, 11.8, 18, 24.2 msec) along with conventional MRI sequences (T 2 -weighted, T 1 -weighted, 3D-fluid-attenuated inversion recovery [FLAIR], and diffusion-weighted imaging [DWI]) at 3.0T., Assessment: R 2 * relaxation maps were calculated from multiecho SWI. The R 2 * cutoff value for hemorrhage ITSS was determined. A segmentation algorithm was designed, based on this R 2 * hemorrhage combined with connected component shape analysis, to quantify the IVV from all slices containing tumor by filtering out hemorrhages. Semiquantitative ITSS scoring as well as total ITSS volume (TIV) including hemorrhages were also calculated., Statistical Tests: One-way analysis of variance (ANOVA) and Tukey-Kramer post-hoc tests were performed to see the difference among the three grades of the tumor (II, III, and IV) in terms of semiquantitative ITSS scoring, TIV, and IVV. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the three methods individually in discriminating between grades of glioma., Results: One-way ANOVA showed that only the proposed IVV significantly differentiated different grades of gliomas having visible ITSS. ROC analysis showed that IVV provided the highest AUC for the discrimination of grade II vs. III (0.93), grade III vs. IV (0.98), and grade II vs. IV glioma (0.94). IVV also provided the highest sensitivity and specificity for differentiating grade II vs. III (87.44, 98.41), grade III vs. IV (97.15, 94.12), and grade II vs. IV (98.72, 92.31)., Data Conclusion: The proposed quantitative method segregates hemorrhage from tumor vasculature. It scores above the existing semiquantitative method in terms of ITSS estimation and grading accuracy., Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:225-233., (© 2019 International Society for Magnetic Resonance in Medicine.)

Background: Glioma grading between intermediate grades (Grade II vs. III and Grade III vs. IV) as well as multiclass grades (Grade II vs. III vs. IV) is challenging and needs to be addressed., Purpose: To develop an artificial intelligence-based methodology for glioma grading using T 1 perfusion parameters and volume of tumor components, and validate the efficacy of the methodology by grading on a cohort of glioma patients., Study Type: Retrospective., Population: The development set consisted of 53 glioma patients and validation consisted of 13 glioma patients., Field Strength/sequence: Conventional MRI images (2D T 1 -W, dual PD-T 2 -W, and 3D FLAIR) and 3D T 1 perfusion MRI data obtained at 3 T., Assessment: Enhancing and nonenhancing components of glioma were segmented out and combined to form the region of interest (ROI) for glioma grading. Prominent vessels were removed from the selected ROI. Different T 1 perfusion parameters from the ROI were combined with volume of tumor components to form the feature set for glioma grading. Optimization was carried out for selection of the statistic of the T 1 perfusion parameters and the features to be used for glioma grading using sequential feature selection and random forest-based feature selection method. An optimized support vector machine (SVM) classifier was used for glioma grading., Statistical Tests: Mean ± SD, analysis of variance (ANOVA) followed by the Tukey-Kramer test, ROC analysis., Results: Classification error for Grade II vs. III was 3.7%, for Grade III vs. IV was 5.26%, and for Grade II vs. III vs. IV was 9.43% using the proposed methodology. The mean of the values above the 90 th percentile value of T 1 perfusion parameters provided a maximum area under the curve (AUC) for intermediate grade differentiation. Random forest obtained optimal feature set provided better grading results than other methods using the SVM classifier., Data Conclusion: It was feasible to achieve low classification error for intermediate as well as multiclass glioma grading using an SVM classifier based on optimized features obtained from T 1 perfusion MRI and volumes of tumor components., Level of Evidence: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1295-1306., (© 2019 International Society for Magnetic Resonance in Medicine.)

Systemic administration of chemotherapeutic drugs is widely used in the treatment of cancer. However, a good understanding of drug transport barriers that influence the treatment efficacy is still lacking. In this study, a voxelized numerical model based on dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) and computational fluid dynamics (CFD) is employed to study the transport and efficacy of three different chemotherapeutic drugs, namely methotrexate, doxorubicin and cisplatin in human brain tumors. DCE-MRI data provides realistic heterogeneous vasculature of the tumor, the permeability of tissue to contrast agent, interstitial volume fraction (porosity) of the tissue and patient-specific arterial input function (AIF). The permeability of tissue to aforementioned drugs is determined by correlating it with the permeability of tissue to the contrast agent. The model is employed to simulate drug concentration in the tissue and compare the effect of heterogeneous vasculature on the distribution of the drugs in the tumor. The drug accumulation is observed to be higher in high permeability areas initially, and in higher porosity areas at later times. Furthermore, it is observed that methotrexate remains in the interstitial space of the tumor in higher concentration for a longer duration as compared to other two drugs, facilitating more tumor cell killing., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objective: To assess the inter-technique agreement of relative cerebral blood volume (rCBV) measurements obtained using T1- and T2*-perfusion MRI on 3T scanner in glioma patients., Methods: A total of 49 adult patients with gliomas underwent both on T1- and T2*-perfusion in the same scanning session, and rCBV maps were estimated using both methods. For the quantitative analysis; Two independent observers recorded the rCBV values from the tumor as well as contralateral brain tissue from both T1- and T2*-perfusion. Inter-observer and inter-technique rCBV measurement agreement were determined by using 95% Bland-Altman limits of agreement and intra-class correlation coefficient (ICC) statistics., Results: Qualitative analysis of the conventional and perfusion images showed that 16/49 (32.65%) tumors showed high susceptibility, and in these patients T2*-perfusion maps were suboptimal. Bland-Altman plots revealed an agreement between two independent observers recorded rCBV values for both T1- and T2*-perfusion. The ICC demonstrated strong agreement between rCBV values recorded by two observers for both T2* (ICC = 0.96, p = 0.040) and T1 (ICC = 0.97, p = 0.026) perfusion and similarly, good agreement was noted between rCBV estimated using two methods (ICC = 0.74, P<0.001). ROC analysis showed that rCBV estimated using T1- and T2*-perfusion methods were able to discriminate between grade-III and grade-IV tumors with AUC of 0.723 and 0.767 respectively. Comparison of AUC values of two ROC curves did not show any significant difference., Conclusions: In the current study, T1- and T2*-perfusion showed similar diagnostic performance for discrimination of grade III and grade IV gliomas; however, T1-perfusion was found to be better for the evaluation of tumors with intratumoral hemorrhage, postoperative recurrent tumors, and lesions near skull base. We conclude that T1-perfusion MRI with a single dose of contrast could be used as an alternative to T2*-perfusion to overcome the issues associated with this technique in brain tumors for reliable perfusion quantification., Competing Interests: The authors have read the journal's policy and the authors of this manuscript have the following competing interests: IS and MVC are paid employees of Philips Health Systems and Philips Health Tech Asia Pacific. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Purpose: High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T 2 -W/FLAIR images. Most studies involving differentiation between vasogenic edema and non-enhancing tumor consider radiologist-based tumor delineation as the ground truth. However, analysis by a radiologist can be subjective and there remain both inter- and intra-rater differences. The objective of the current study is to develop a methodology for differentiation between non-enhancing tumor and vasogenic edema in HGG patients based on T 1 perfusion MRI parameters, using a ground truth which is independent of a radiologist's manual delineation of the tumor., Material and Methods: This study included 9 HGG patients with pre- and post-surgery MRI data and 9 metastasis patients with pre-surgery MRI data. MRI data included conventional T 1 -W, T 2 -W, and FLAIR images and DCE-MRI dynamic images. In this study, the authors hypothesize that surgeried non-enhancing FLAIR hyperintense tissue, which was obtained using pre- and post-surgery MRI images of glioma patients, should be largely comprised of non-enhancing tumor. Hence this could be used as an alternative ground truth for the non-enhancing tumor region. Histological examination of the resected tissue was done for validation. Vasogenic edema was obtained from the non-enhancing FLAIR hyperintense region of metastasis patients, as they have a clear boundary between enhancing tumor and edema. DCE-MRI data analysis was performed to obtain T 1 perfusion MRI parameters. Support Vector Machine (SVM) classification was performed using T 1 perfusion MRI parameters to differentiate between non-enhancing tumor and vasogenic edema. Receiver-operating-characteristic (ROC) analysis was done on the results of the SVM classifier. For improved classification accuracy, the SVM output was post-processed via neighborhood smoothing., Results: Histology results showed that resected tissue consists largely of tumorous tissue with 7.21 ± 4.05% edema and a small amount of healthy tissue. SVM-based classification provided a misclassification error of 8.4% in differentiation between non-enhancing tumor and vasogenic edema, which was further reduced to 2.4% using neighborhood smoothing., Conclusion: The current study proposes a semiautomatic method for segmentation between non-enhancing tumor and vasogenic edema in HGG patients, based on an SVM classifier trained on an alternative ground truth to a radiologist's manual delineation of a tumor. The proposed methodology may prove to be a useful tool for pre- and post-operative evaluation of glioma patients., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Glioma grade along with patient's age and general health are used for treatment planning and prognosis., Purpose: To characterize and quantify the spontaneous blood oxygen level-dependent (BOLD) fluctuations in gliomas using measures based on T2*-weighted signal time-series and to distinguish between high- and low-grade gliomas., Study Type: Retrospective., Subjects: Twenty-one patients with high-grade and 13 patients with low-grade gliomas confirmed on histology were investigated., Field Strength/sequence: Dynamic T2*-weighted (multislice single-shot echo-planar-imaging) magnetic resonance imaging (MRI) was performed on a 3T system with an 8-element receive-only head coil to measure the BOLD fluctuations. In addition, a dynamic T 1 -weighted (3D fast field echo) dynamic contrast-enhanced (DCE) perfusion scan was performed., Assessment: Three BOLD measures were determined: the temporal shift (TS), amplitude of low frequency fluctuations (ALFF), and regional homogeneity (ReHo). DCE perfusion-based cerebral blood volume (CBV) and time-to-peak (TTP) maps were concurrently evaluated for comparison., Statistical Tests: An analysis-of-variance test was first used. When the test appeared significant, post-hoc analysis was performed using analysis-of-covariance with age as covariate. Logistic regression and receiver-operator characteristic curve analysis were also performed., Results: TS was significantly advanced in high-grade gliomas compared to the contralateral cortex (P = 0.01) and low-grade gliomas (P = 0.009). In high-grade gliomas, ALFF and CBV were significantly higher than the contralateral cortex (P = 0.041 and P = 0.008, respectively) and low-grade gliomas (P = 0.036 and P = 0.01, respectively). ReHo and TTP did not show significant differences between high- and low-grade gliomas (P = 0.46 and P = 0.42, respectively). The area-under-curve was above 0.7 only for the TS, ALFF, and CBV measures., Data Conclusion: Advanced and amplified hemodynamic fluctuations manifest in high-grade gliomas, but not in low-grade gliomas, and can be assessed using BOLD measures. Preliminary results showed that quantification of spontaneous fluctuations has potential for hemodynamic characterization of gliomas and distinguishing between high- and low-grade gliomas., Level of Evidence: 4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:1616-1625., (© 2017 International Society for Magnetic Resonance in Medicine.)

Purpose: MRI is a useful method for discriminating low- and high-grade glioma using perfusion MRI and susceptibility-weighted imaging (SWI). The purpose of this study is to evaluate the usefulness of T1-perfusion MRI and SWI in discriminating among grade II, III, and IV gliomas., Methods: T1-perfusion MRI was used to measure relative cerebral blood volume (rCBV) in 129 patients with glioma (70 grade IV, 33 grade III, and 26 grade II tumors). SWI was also used to measure the intratumoral susceptibility signal intensity (ITSS) scores for each tumor in these patients. rCBV and ITSS values were compared to seek differences between grade II vs. grade III, grade III vs. grade IV, and grade III+II vs. grade IV tumors., Results: Significant differences in rCBV values of the three grades of the tumors were noted and pairwise comparisons showed significantly higher rCBV values in grade IV tumors as compared to grade III tumors, and similarly increased rCBV was seen in the grade III tumors as compared to grade II tumors (p < 0.001). Grade IV gliomas showed significantly higher ITSS scores on SWI as compared to grade III tumors (p < 0.001) whereas insignificant difference was seen on comparing ITSS scores of grade III with grade II tumors. Combining the rCBV and ITSS resulted in significant improvement in the discrimination of grade III from grade IV tumors., Conclusion: The combination of rCBV values derived from T1-perfusion MRI and SWI derived ITSS scores improves the diagnostic accuracy for discrimination of grade III from grade IV gliomas.

Background: Dynamic-contrast-enhanced (DCE) MRI data acquired using gradient echo based sequences is affected by errors in flip angle (FA) due to transmit B 1 inhomogeneity (B 1 inh). The purpose of the study was to evaluate the effect of B 1 inh on quantitative analysis of DCE-MRI data of human brain tumor patients and to evaluate the clinical significance of B 1 inh correction of perfusion parameters (PPs) on tumor grading., Methods: An MRI study was conducted on 35 glioma patients at 3T. The patients had histologically confirmed glioma with 23 high-grade (HG) and 12 low-grade (LG). Data for B 1 -mapping, T 1 -mapping and DCE-MRI were acquired. Relative B 1 maps (B 1rel ) were generated using the saturated-double-angle method. T 1 -maps were computed using the variable flip-angle method. Post-processing was performed for conversion of signal-intensity time (S(t)) curve to concentration-time (C(t)) curve followed by tracer kinetic analysis (K trans , Ve, Vp, Kep) and first pass analysis (CBV, CBF) using the general tracer-kinetic model. DCE-MRI data was analyzed without and with B 1 inh correction and errors in PPs were computed. Receiver-operating-characteristic (ROC) analysis was performed on HG and LG patients. Simulations were carried out to understand the effect of B 1 inhomogeneity on DCE-MRI data analysis in a systematic way. S(t) curves mimicking those in tumor tissue, were generated and FA errors were introduced followed by error analysis of PPs. Dependence of FA-based errors on the concentration of contrast agent and on the duration of DCE-MRI data was also studied. Simulations were also done to obtain K trans of glioma patients at different B 1rel values and see whether grading is affected or not., Results: Current study shows that B 1rel value higher than nominal results in an overestimation of C(t) curves as well as derived PPs and vice versa. Moreover, at same B 1rel values, errors were large for larger values of C(t). Simulation results showed that grade of patients can change because of B 1 inh., Conclusions: B 1 inh in the human brain at 3T-MRI can introduce substantial errors in PPs derived from DCE-MRI data that might affect the accuracy of tumor grading, particularly for border zone cases. These errors can be mitigated using B 1 inh correction during DCE-MRI data analysis.

Purpose: Aim of this retrospective study was to compare diagnostic accuracy of proposed automatic normalization method to quantify the relative cerebral blood volume (rCBV) with existing contra-lateral region of interest (ROI) based CBV normalization method for glioma grading using T1-weighted dynamic contrast enhanced MRI (DCE-MRI)., Material and Methods: Sixty patients with histologically confirmed gliomas were included in this study retrospectively. CBV maps were generated using T1-weighted DCE-MRI and are normalized by contralateral ROI based method (rCBV&#95;contra), unaffected white matter (rCBV&#95;WM) and unaffected gray matter (rCBV&#95;GM), the latter two of these were generated automatically. An expert radiologist with >10years of experience in DCE-MRI and a non-expert with one year experience were used independently to measure rCBVs. Cutoff values for glioma grading were decided from ROC analysis. Agreement of histology with rCBV&#95;WM, rCBV&#95;GM and rCBV&#95;contra respectively was studied using Kappa statistics and intra-class correlation coefficient (ICC)., Result: The diagnostic accuracy of glioma grading using the measured rCBV&#95;contra by expert radiologist was found to be high (sensitivity=1.00, specificity=0.96, p<0.001) compared to the non-expert user (sensitivity=0.65, specificity=0.78, p<0.001). On the other hand, both the expert and non-expert user showed similar diagnostic accuracy for automatic rCBV&#95;WM (sensitivity=0.89, specificity=0.87, p=0.001) and rCBV&#95;GM (sensitivity=0.81, specificity=0.78, p=0.001) measures. Further, it was also observed that, contralateral based method by expert user showed highest agreement with histological grading of tumor (kappa=0.96, agreement 98.33%, p<0.001), however; automatic normalization method showed same percentage of agreement for both expert and non-expert user. rCBV&#95;WM showed an agreement of 88.33% (kappa=0.76,p<0.001) with histopathological grading., Conclusion: It was inferred from this study that, in the absence of expert user, automated normalization of CBV using the proposed method could provide better diagnostic accuracy compared to the manual contralateral based approach., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background/aims: Perfusion magnetic resonance imaging (MRI) is useful for preoperative assessment of brain tumors. Dynamic susceptibility contrast perfusion MRI is commonly used for evaluation of brain tumors. Dynamic contrast-enhanced (DCE) MRI is an alternative method that has mainly been used in adult brain tumors. In this preliminary study, we report our initial experience with the DCE perfusion MRI in pediatric brain tumors., Methods: Sixty-four newly diagnosed pediatric brain tumor patients underwent DCE perfusion MRI on a 3-T scanner. Hemodynamic and kinetic parametric maps were generated and the regions with the highest values were measured on each map. Statistical differences were sought to differentiate between low-grade tumors, high-grade tumors, and medulloblastomas. The perfusion metrics of common posterior fossa tumors were also compared., Results: Relative corrected cerebral blood volume (rCBV) and fractional plasma volume measures differed significantly between high- and low-grade tumors (p < 0.05). High-grade tumors could be differentiated from low-grade tumors, with an rCBV cutoff value of 2.41 and 88.6% sensitivity and 65% specificity. There was no significant difference in Ktrans, Kep, Ve, or λtr between these 2 groups of tumors. rCBV, relative quantification of the cerebral blood flow, and permeability indices were found to be significantly different in various posterior fossa tumors, i.e., pilocytic astrocytoma, ependymoma, and medulloblastoma (p < 0.05)., Conclusion: DCE-derived perfusion metrics are useful in differentiating high-grade tumors from low-grade ones and discriminating among various posterior fossa tumors in the pediatric age group., (© 2017 S. Karger AG, Basel.)

Purpose: Dynamic contrast enhanced (DCE) MRI is used to grade and to monitor the progression of glioma while on treatment. Usually, a fixed hematocrit (Hct) value for adults is assumed to be ~45%; however, it is actually known for individual variations. Purpose of this study was to investigate the effect of measured Hct values in glioma grading using DCE-MRI., Materials and Methods: Fifty glioma patients were included in this study. Kinetic and hemodynamic parameters were estimated for each patient using assumed as well as measured Hct values. To look the changes in Hct value over time, Hct was measured multiple times from 10 of these glioma patients who were on treatment. Simulation was done to look for the effect of extreme variations of Hct values on perfusion metrics. The data was compared to look for significant differences in the perfusion metrics derived from assumed and measured Hct values., Results: The measured Hct value in patients was found to be (40.4±4.28)%. The sensitivity and specificity of DCE-MRI parameters in glioma grading were not significantly influenced by using measured vis-a-vis assumed Hct values. The serial Hct values from 10 patients who were on treatment showed a fluctuation of 15-20% over time. The simulated data showed linear influence of Hct values on kinetic parameters. The tumor grading was altered on altering the Hct values in borderline cases., Conclusion: Hct values influence the hemodynamic and kinetic metrics linearly and may affect glioma grading. However, perfusion metrics values might change significantly with large change in Hct values, especially in patients who are on chemotherapy necessitating its use in the DCE model., (Copyright © 2016 Elsevier Inc. All rights reserved.)

We report the case of a 13-year-old girl presenting with left-sided hemiparesis, altered sensorium and episodic headache with bouts of projectile vomiting. Imaging revealed a large heterodense intraventricular mass lesion displaying focal calcification and hyperintensity on T1- and T2- weighted fluid attenuated inversion recovery (FLAIR) magnetic resonance images suggesting the presence of intratumoral fat. Histologically, the tumour showed sheets of glial cells, focal perithelial rosettes and individual cells showing fat vacuoles. The morphological impression was of an ependymoma with lipomatous differentiation. Glial fibrillary acid protein (GFAP) immunohistochemistry revealed positivity in the cytoplasmic processes of the tumour cells as well as in the cytoplasmic rim of the cells having an adipocytic appearance. S100 and vimentin were also immunoreactive. Ultrastructural studies confirmed the ependymal differentiation of the tumour and the presence of an osmiophilic fat component confirming the diagnosis. After 1 year of follow-up, the patient presented with similar complaints and MRI evidence of recurrence of the tumour. A comprehensive literature review revealed that half of the reported cases of this pattern recurred suggesting a possibly tenacious clinical course.

Ependymomas are relatively uncommon gliomas with poor prognosis despite recent advances in neurooncology. Molecular pathogenesis of ependymomas is not extensively studied. Lack of correlation of histological grade with patient outcome has directed attention towards identification of molecular alterations as novel prognostic markers. Recently, 1q gain has emerged as a potential prognostic marker, associated with decreased survival, especially in posterior fossa, high grade tumors. Cases of intracranial ependymomas were retrieved. Tumors were graded using objective criteria to supplement WHO grading. Fluorescence in situ hybridization for 1q gain was performed on formalin-fixed paraffin embedded sections. Eighty-one intracranial ependymomas were analyzed. Pediatric (76%) and infratentorial (70%) ependymomas constituted the majority. 1q gain was seen in 27 cases (33%), was equally frequent in children (34%) and adults (32%), supratentorial (37%) and infratentorial (32%) location, grade II (33%) and III (25%) tumors. Recurrence was noted in 24 cases and death in 7 cases with 5-year progression-free and overall-survival rates of 37% and 80%, respectively. Grade II tumors had a better survival than grade III tumors; histopathological grade was the only prognostically significant marker. 1q gain had no prognostic significance. 1q gain is frequent in ependymomas in Indian patients, seen across all ages, sites and grades, and thus is likely an early event in pathogenesis. The prognostic value of 1q gain, remains uncertain, and multicentric pooling of data is required. A histopathological grading system using objective criteria correlates well with patient outcome and can serve as an economical option for prognostication of ependymomas.

Background and Purpose: Studies of brain tumors have identified altered tissue metabolism and water diffusion in MRI normal appearing tissue regions. In this retrospective study the relationship of these imaging measures with tumor grade in gliomas was investigated., Methods: MR spectroscopic imaging of whole brain and mean diffusivity (MD) measurements were obtained in subjects with untreated glioma and from normal control subjects. Mean metabolite values for N-acetylaspartate (NAA), total creatine (Cre), and total choline (Cho) were obtained in gray- and white-matter regions for the hemisphere contralateral to the tumor location, and MD values were obtained from contralateral normal-appearing white matter. Analyses tested for differences in mean values between subject groups while accounting for age., Results: Analysis demonstrated increased NAA/Cre and MD, and decreased Cho/NAA for all tumor grades relative to control values. Differences between tumor grades were also observed for NAA, NAA/Cre, and Cho/NAA. Abnormal values of water diffusion were also observed, but with only a weak association between alterations in diffusion and tissue metabolites., Conclusions: This study supports previous observations of altered tissue metabolism and water diffusion in normal-appearing white matter while additionally finding differences of metabolite values in gray matter and an association with tumor grade., (Copyright © 2013 by the American Society of Neuroimaging.)

Background: Ependymomas are relatively rare glial tumours, whose pathogenesis is not well elucidated. They are enigmatic tumours that show site-specific differences in their biological behaviour. Recent studies have hypothesized that ependymoma cancer stem cells (CSCs) are derived from radial glia and express stem cell markers such as nestin, which is associated with a poor prognosis. CSCs reside in 'vascular niches', where endothelial cells and molecular signals like vascular endothelial growth factor (VEGF) play an important role in their survival. Studies analysing VEGF expression in ependymomas showed that ependymal vascular proliferation is less sensitive to induction by VEGF, questioning the possible beneficial effect of anti-VEGF therapy in ependymomas. We aimed to study nestin and VEGF immunoexpression in ependymomas, correlate them with clinicopathological parameters and reveal a role for VEGF in ependymomas that extends beyond the context of tumour angiogenesis., Methods: We analysed 126 cases of ependymomas of different grades and locations for nestin and VEGF immunoexpression. Endothelial cells were labelled with CD34. Vascular patterns and microvascular density was determined., Results: Nestin and VEGF expression in tumour cells were more frequent in supratentorial tumours [89% (33/37) and 65% (24/37) respectively], and were associated with a significantly poor progression-free survival (PFS). VEGF expression did not reveal any correlation with necrosis or bizarre vascular patterns., Conclusions: Supratentorial location is an independent predictor of a poor PFS. Significant coexpression of nestin and VEGF suggests that latter possibly augments stem cell survival. Thus, anti-VEGF therapy may be a good option in future for nestin immunopositive ependymomas., (© 2013 British Neuropathological Society.)

Purpose: Magnetic resonance (MR) imaging and computed tomography (CT) are used almost exclusively in radiation therapy planning of glioblastoma multiforme (GBM), despite their well-recognized limitations. MR spectroscopic imaging (MRSI) can identify biochemical patterns associated with normal brain and tumor, predominantly by observation of choline (Cho) and N-acetylaspartate (NAA) distributions. In this study, volumetric 3-dimensional MRSI was used to map these compounds over a wide region of the brain and to evaluate metabolite-defined treatment targets (metabolic tumor volumes [MTV])., Methods and Materials: Volumetric MRSI with effective voxel size of ∼1.0 mL and standard clinical MR images were obtained from 19 GBM patients. Gross tumor volumes and edema were manually outlined, and clinical target volumes (CTVs) receiving 46 and 60 Gy were defined (CTV46 and CTV60, respectively). MTVCho and MTVNAA were constructed based on volumes with high Cho and low NAA relative to values estimated from normal-appearing tissue., Results: The MRSI coverage of the brain was between 70% and 76%. The MTVNAA were almost entirely contained within the edema, and the correlation between the 2 volumes was significant (r=0.68, P=.001). In contrast, a considerable fraction of MTVCho was outside of the edema (median, 33%) and for some patients it was also outside of the CTV46 and CTV60. These untreated volumes were greater than 10% for 7 patients (37%) in the study, and on average more than one-third (34.3%) of the MTVCho for these patients were outside of CTV60., Conclusions: This study demonstrates the potential usefulness of whole-brain MRSI for radiation therapy planning of GBM and revealed that areas of metabolically active tumor are not covered by standard RT volumes. The described integration of MTV into the RT system will pave the way to future clinical trials investigating outcomes in patients treated based on metabolic information., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Ependymomas are relatively uncommon tumours of the central nervous system which arise from the ependymal lining of the ventricles and spinal canal. The molecular changes leading to ependymal oncogenesis are not completely understood. We examined chromosome 9q33-34 locus for gain, potential oncogenes at this locus (Notch-1 and Tenascin-C) and Notch pathway target genes (Hes-1, Hey-2 & C-myc) in ependymomas by fluorescent in situ hybridization (FISH) and immunohistochemistry (IHC), respectively, to assess if they have any correlation with clinical characteristics. We analyzed 50 cases of ependymomas by FISH for 9q gain and by IHC for Notch-1 and its target gene proteins (Hes-1, Hey-2 and C-myc) expression. We also performed IHC for Tenascin-C to rule out any correlation with aggressiveness/grade of tumour. FISH study revealed significant chromosome 9q gain in ependymomas of adult onset (age > 18 years) and spinal cord origin. Notch-1 showed significantly more frequent immunohistochemical expression in supratentorial and anaplastic ependymomas. Tenascin-C (TN-C) expression was significant in intracranial, childhood (age ≤ 18 years) and anaplastic ependymomas. Of the three Notch pathway target gene proteins (Hes-1, Hey-2 and C-myc), Hes-1 and C-myc expression showed significant correlation with anaplastic and adult onset ependymomas, respectively. Genetic alterations are independent prognostic markers in ependymomas. A clinicopathological correlation with various molecular signatures may be helpful in the development of new therapeutic targets.

Purpose: To modify the generalized tracer kinetic model (GTKM) by introducing an additional tissue uptake leakage compartment in extracellular extravascular space (LTKM). In addition, an implicit determination of voxel-wise local arterial input function (AIF) Cp (t) was performed to see whether these changes help in better discrimination between low- and high-grade glioma using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)., Materials and Methods: The modified model (LTKM) was explored and fitted to the concentration-time curve C(t) of each voxel, in which the local AIF Cp (t) could be estimated by a time invariant convolution approximation based on a separately measured global AIF Ca (t). A comparative study of tracer kinetic analysis was performed on 184 glioma patients using DCE-MRI data on 1.5T and 3T MRI systems., Results: The LTKM analysis provided more accurate pharmacokinetic parameters as evidenced by their relative constancy with respect to the length of concentration-time curve used. In addition, LTKM with local AIF resulted in improved discrimination between low-grade and high-grade gliomas., Conclusion: LTKM with local AIF provides more accurate estimation of physiological parameters and improves discrimination between low-grade and high-grade gliomas as compared with GTKM., (Copyright © 2013 Wiley Periodicals, Inc., a Wiley company.)

Introduction: The study was performed to compare dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with 3-dimensional (3D) pseudocontinuous arterial spin labeling (PCASL) MRI in gliomas with an aim to see whether arterial spin labeling (ASL)-derived cerebral blood flow (CBF) values can be used as an alternative to DCE-MRI for its grading., Materials and Methods: Sixty-four patients with glioma (37 male; mean age, 43 years; 38 high grade and 26 low grade) underwent 3D-PCASL and DCE-MRI. The DCE indices (relative cerebral blood volume, rCBV; relative CBF, rCBF; permeability, k and kep; and leakage, ve) and ASL (absolute and rCBF) values were quantified from the tumors. Student independent t test was used to compare ASL and DCE-MRI indices. Pearson correlation was used to see correlation between DCE- and ASL-derived CBF values in tumor and normal parenchyma., Results: On Student t test, neither ASL-derived absolute CBF (P = 0.78) nor rCBF (P = 0.12) values were found to be significantly different in 2 groups, whereas DCE indices except ve were significantly higher in high-grade gliomas. Arterial spin labeling-derived rCBF values weakly correlated with DCE-derived rCBF values, whereas these did not show correlation in normal grey (P = 0.12, r = 0.2) and white (P = 0.26, r = 0.14) matter regions., Conclusions: Three-dimensional pseudocontinuous arterial spin labeling does not appear to be a reliable technique in the current form and may not be a suitable replacement for DCE in grading of glioma.

Introduction: Accurate grading of cerebral glioma using conventional structural imaging techniques remains challenging due to the relatively poor sensitivity and specificity of these methods. The purpose of this study was to evaluate the relative sensitivity and specificity of structural magnetic resonance imaging and MR measurements of perfusion, diffusion, and whole-brain spectroscopic parameters for glioma grading., Methods: Fifty-six patients with radiologically suspected untreated glioma were studied with T1- and T2-weighted MR imaging, dynamic contrast-enhanced MR imaging, diffusion tensor imaging, and volumetric whole-brain MR spectroscopic imaging. Receiver-operating characteristic analysis was performed using the relative cerebral blood volume (rCBV), apparent diffusion coefficient, fractional anisotropy, and multiple spectroscopic parameters to determine optimum thresholds for tumor grading and to obtain the sensitivity, specificity, and positive and negative predictive values for identifying high-grade gliomas. Logistic regression was performed to analyze all the parameters together., Results: The rCBV individually classified glioma as low and high grade with a sensitivity and specificity of 100 and 88 %, respectively, based on a threshold value of 3.34. On combining all parameters under consideration, the classification was achieved with 2 % error and sensitivity and specificity of 100 and 96 %, respectively., Conclusion: Individually, CBV measurement provides the greatest diagnostic performance for predicting glioma grade; however, the most accurate classification can be achieved by combining all of the imaging parameters.

Introduction: The purpose of the present study was to look for the possible predictors which might discriminate between high- and low-grade gliomas by pooling dynamic contrast-enhanced (DCE)-perfusion derived indices and immunohistochemical markers., Methods: DCE-MRI was performed in 76 patients with different grades of gliomas. Perfusion indices, i.e., relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), permeability (k (trans) and k (ep)), and leakage (v (e)) were quantified. MMP-9-, PRL-3-, HIF-1α-, and VEGF-expressing cells were quantified from the excised tumor tissues. Discriminant function analysis using these markers was used to identify discriminatory variables using a stepwise procedure. To look for correlations between immunohistochemical parameters and DCE metrics, Pearson's correlation coefficient was also used., Results: A discriminant function for differentiating between high- and low-grade tumors was constructed using DCE-MRI-derived rCBV, k (ep), and v (e). The form of the functions estimated are "D (1) = 0.642 × rCBV + 0.591 × k (ep) - 1.501 × v (e) - 1.550" and "D (2) = 1.608 × rCBV + 3.033 × k (ep) + 5.508 × v (e) - 8.784" for low- and high-grade tumors, respectively. This function classified overall 92.1% of the cases correctly (89.1% high-grade tumors and 100% low-grade tumors). In addition, VEGF expression correlated with rCBV and rCBF, whereas MMP-9 expression correlated with k (ep). A significant positive correlation of HIF-1α with rCBV and VEGF expression was also found., Conclusion: DCE-MRI may be used to differentiate between high-grade and low-grade brain tumors non-invasively, which may be helpful in appropriate treatment planning and management of these patients. The correlation of its indices with immunohistochemical markers suggests that this imaging technique is useful in tissue characterization of gliomas.

Objective: To look for the association of tissue matrix metalloproteinase 9 (MMP-9) expression with dynamic contrast-enhanced magnetic resonance imaging and to see whether these can prognosticate patients with glioblastoma multiforme (GBM)., Methods: Forty-seven patients with GBM underwent dynamic contrast-enhanced magnetic resonance imaging to look for association of its indices with tissue MMP-9 expression using Pearson correlation. Kaplan-Meier survival analysis was performed to study the survival pattern for low-, medium-, and high-tissue MMP-9 expression and kep values., Results: Among perfusion indices, kep, k, and ve significantly correlated with MMP-9 expression. Matrix metalloproteinase 9 expression was found to be best estimated by kep using a quadratic model. The 1-year survival in low-, medium-, and high-tissue MMP-9 and kep groups were 59%, 45%, and 7%, and 59%, 33%, and 15%, respectively., Conclusion: The association of kep and MMP-9 expression with survival suggests that kep may be used as imaging biomarker of GBM progression and its prognostication.

Objective: To compare dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) with diffusion tensor imaging (DTI) for predicting tumor infiltration in a conventional MRI normal-appearing internal capsule adjacent to the tumor in patients with glioblastoma multiforme., Methods: Thirty patients with glioblastoma multiforme underwent a neurological examination for motor assessment, DCE MRI, and DTI. On fused DCE and DTI images, regions of interest were placed on the normal-appearing internal capsule and the corresponding region of the contralateral internal capsule. These patients were pooled into group 1 (improved after surgery, n = 9) and group 2 (did not improve after surgery, n = 21) on the basis of motor strength. The DTI and perfusion metrics were statistically analyzed to look for a predictor of motor functionality., Results: Multivariate logistic regression analysis showed only cerebral blood volume to be the predictor of improvement in motor functionality after surgery (P = 0.043)., Conclusions: We conclude that cerebral blood volume appears to be the predictor of motor functionality after surgery and may indirectly suggest tumor infiltration, whereas DTI helps to precisely localize the fiber tracts.

The purpose of this study was to determine whether proton magnetic resonance spectroscopy (PMRS) and diffusion tensor imaging (DTI) indices, fractional anisotropy (FA) and mean diffusivity (MD) can be used to distinguish brain abscess from cystic brain tumors, which are difficult to distinguish by conventional magnetic resonance imaging (MRI). Fifty-three patients with intracranial cystic mass lesions and 10 normal controls were studied. Conventional MRI, PMRS and DTI of all the patients were performed on a 1.5-T GE scanner. Forty patients were with brain abscess and 13 with cystic tumors. Cytosolic amino acids (AAs) were present in 32 of 40 brain abscess patients. Out of 13 patients with cystic tumors, lactate and choline were seen in 3 and only lactate was present in 10 patients on PMRS. All 40 cases of abscess had high FA, while all 13 cases of tumor cysts had high MD values. We conclude that FA measurements are more sensitive in predicting the abscess, while PMRS and MD are more specific in differentiating abscess from cystic tumors. We suggest that PMRS should be combined with DTI rather than with diffusion-weighted imaging as FA can be used as an additional parameter for separation of abscess from other cystic intracranial mass lesions.

Objective: To look for the impact of leak correction on correlation of perfusion indices with microvessel density (MVD) and vascular endothelial growth factor (VEGF) in astrocytomas., Methods: Dynamic contrast-enhanced magnetic resonance imaging was performed in 64 patients with varying grades of astrocytoma. Perfusion indices (ie, relative cerebral blood volume (rCBV) with and without leak correction, relative cerebral blood flow (rCBF), permeability (k(trans)), and leakage (v(e)) were quantified. MVD and VEGF-expressing cells were quantified from the excised tumor tissues and were correlated with perfusion metrics., Results: Perfusion indices showed significant difference among the astrocytoma grades. The corrected rCBV correlated better with MVD and VEGF. The corrected rCBV correlated (r = 0.853, P = <0.001) strongly, whereas the uncorrected rCBV (r = 0.592, P = <0.001) and k(trans) (r = 0.498, P = 0.001) correlated moderately with tumor grade. The corrected rCBV discriminated 100% low-grade from high-grade astrocytoma, while uncorrected rCBV did this in 95.5% low-grade and 71.4% high-grade astrocytoma., Conclusions: Corrected rCBV better correlates with grade and is more accurate in discriminating low-grade from high-grade astrocytoma compared with uncorrected rCBV.

Introduction: It is not always possible to differentiate infective from neoplastic brain lesions with conventional MR imaging. In this study, we assessed the utility of various perfusion indices in the differentiation of infective from neoplastic brain lesions., Methods: A total of 103 patients with infective brain lesions (group I, n=26) and neoplastic brain lesions (high-grade glioma, HGG, group II, n=52; low-grade glioma, LGG, group III, n=25) underwent dynamic contrast-enhanced MR imaging. The perfusion indices, including relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), transfer coefficient (k(trans)) and leakage (v(e)), were calculated and their degree of correlation with immunohistologically obtained microvessel density (MVD) and vascular endothelial growth factor (VEGF) determined. The rCBV was corrected for the leakage effect. Discriminant analysis for rCBV, rCBF, k(trans) and v(e) was performed to predict the group membership of each case and post hoc analysis was performed to look for group differences., Results: The rCBV, rCBF, k(trans), v(e), MVD and VEGF were significantly different (P<0.001) between the three groups. Discriminant analysis showed that rCBV predicted 73.1% of the infective lesions, 84.6% of the HGG and 72.0% of the LGG. The rCBF classified 86.5% of the HGG, 80.0% of the LGG and 65.4% of the infective lesions. The k(trans) discriminated 98.1% of the HGG, 76.0% of the LGG and 88.5% of the infective lesions correctly. The v(e) classified 98.1% of the HGG, 76.0% of the LGG and 84.6% the infective lesions. The rCBV was correlated significantly with MVD and VEGF, while the correlation between k(trans) and MVD was not significant., Conclusion: Physiological perfusion indices such as k(trans) and v(e) appear to be useful in differentiating infective from neoplastic brain lesions. Adding these indices to the current imaging protocol is likely to improve tissue characterization of these focal brain mass lesions.

Purpose: The aim of this study was to ascertain whether diffusion tensor imaging (DTI) metrics fractional anisotropy (FA), mean diffusivity (MD), linear case (CL), planar case (CP), spherical case (CS)-can characterize a threshold dose and temporal evolution of changes in normal-appearing white matter (NAWM) of adults with low-grade gliomas (LGGs) treated with radiation therapy (RT)., Methods and Materials: Conventional and DTI imaging were performed before RT in 5 patients and subsequently, on average, at 3 months (n = 5), 8 months (n = 3), and 14 months (n = 5) following RT for a total of 18 examinations. Isodose distribution at 5-Gy intervals were visualized in all the slices of fluid attenuated inversion recovery (FLAIR) and the corresponding DTI images without diffusion sensitization (b0DTI). The latter were exported for relative quantitative analysis., Results: Compared to pre-RT values, FA and CL decreased, whereas CS increased at 3 and 8 months and recovered partially at 14 months for the dose bins >55 Gy and 50-55 Gy. For the 45 50 Gy bin, the FA and CL decreased with an increase in CS at 3 months; no further change was seen at 8 or 14 months. For the >55 Gy and 50-55 Gy bins, CP decreased and MD increased at 3 months and returned to baseline at 8 months following RT., Conclusion: Radiation-induced changes in NAWM can be detected at 3 months after RT, with changes in FA, CL, and CS (but not CP or MD) values seen at a threshold dose of 45-50 Gy. A partial recovery was evident by 14 months to regions that received doses of 50-55 Gy and >55 Gy, thus providing an objective measure of radiation effect on NAWM.

Delineation of various target volumes using contrast-enhanced magnetic resonance imaging (MRI) and/or computed tomography (CT) constitutes the primary step for radiation therapy planning (RTP) in brain tumors. This study presents a quantification and comparative evaluation of the various clinical target volumes (CTV) and gross target volumes (GTV) as outlined by contrast-enhanced CT and MRI, along with its implications for postoperative radiotherapy of brain tumors. Twenty-one patients of gliomas were considered for this prospective study. Peritumoral edema as CTV and residual tumor as GTV were delineated separately in postoperative contrast-enhanced CT and MRI. These volumes were estimated separately and their congruence studied for contrast-enhanced CT and MRI. Compared to MRI, CT underestimated the volumes, with significant differences seen in the mean CTV (mean +/- SD: -62.92 +/- 93.99 cc; P = 0.006) and GTV (mean +/- SD: -21.08 +/- 36.04 cc; P = 0.014). These differences were found to be significant for high-grade gliomas (CTV: P = 0.045; GTV: P = 0.044), while they were statistically insignificant for low-grade gliomas (CTV: P = 0.080; GTV: P = 0.117). The mean differences in the volumes for CTV and GTV were estimated to be -106.7% and -62.6%, respectively, taking the CT volumes as the baseline. Thus, even though, electron density information from CT is essential for RTP, target delineation during postoperative radiotherapy of brain tumors, especially for high-grade tumors, should be based on MRI so as to avoid inadvertent geographical misses, especially in the regions of peritumoral edema.

Dynamic contrast-enhanced (DCE) T(1)-weighted magnetic resonance imaging (MRI) is a powerful tool capable of providing quantitative assessment of contrast uptake and characterization of microvascular structure in human gliomas. The kinetics of the bolus injection doped with increasing concentrations of gadopentate dimeglumine (Gd-DTPA) depends on tissue as well as pulse sequence parameters. A simple method is described that overcomes the limitation of relative signal increase measurement and may lead to improved accuracy in quantification of perfusion indices of glioma. Based on an analysis of the contrast behavior of spoiled gradient-recalled echo sequence; a parameter K with arbitrary unit 5.0 is introduced, which provides a better approximation to the differential T(1) relaxation rate. DCE-MRI measurements of relative cerebral blood volume (rCBV) and cerebral blood flow (rCBF) were calculated in 25 patients with brain tumors (15=high-grade glioma, 10=low-grade glioma). The mean rCBV was 6.46 +/- 2.45 in high-grade glioma and 2.89 +/- 1.47 in the low-grade glioma. The rCBF was 3.94 +/- 1.47 in high-grade glioma while 2.25 +/- 0.87 in low-grade glioma. A significant difference in rCBF and rCBV was found between high- and low-grade gliomas. This simple and robust technique reveals the complexity of tumor vasculature and heterogeneity that may aid in therapeutic management especially in nonenhancing high-grade gliomas. We conclude that the precontrast medium steady-state residue parameter K may be useful in improved quantification of perfusion indices in human glioma using T(1)-weighted DCE-MRI.

Background: The objective of this study is to evaluate the sensitivity and specificity of DWI in differentiating brain abscesses from other intracranial cystic lesions., Methods: One hundred fifteen patients with 147 cystic lesions (mean age, 26.4 year) were prospectively studied with DWI on a 1.5-T magnetic resonance imaging. Lesions appearing hyperintense on DWI with the ADC values of lower than 0.9 +/- 0.13 x 10(-3) mm(2)/s (mean +/- SD) were considered as brain abscess, whereas hypointense lesions on DWI with the ADC values 2.2 +/- 0.9 x 10(-3) mm(2)/s were categorized as nonabscess cystic lesions., Results: Ninety-three of 97 brain abscess lesions were hyperintense on DWI, with significantly low (P = .0001) ADC value (0.87 +/- 0.05 x 10(-3) mm(2)/s) (mean +/- SEM), compared with 48 nonabscess lesions (2.89 +/- 0.05 x 10(-3) mm(2)/s). Four of 97 brain abscess lesions in 65 patients were false negative, and 2 of 50 nonabscess lesions in 50 patients were false positive for the diagnosis of brain abscess. The ADC value of the tumor cysts (2.9 +/- 0.05 x 10(-3) mm(2)/s) was significantly lower (P = .02) compared with benign cysts and neurocysticercosis (3.2 +/- 0.05 x 10(-3) mm(2)/s) among nonabscess group. The sensitivity of DWI for the differentiation of brain abscesses from nonabscesses was 96%; specificity, 96%; positive predictive value, 98%; negative predictive value, 92%; and accuracy of the test, 96%., Conclusions: Diffusion-weighted imaging has high sensitivity and specificity for the differentiation of brain abscess from other nonabscess intracranial cystic lesions.

Capabilities of diffusion-weighted (DW) and magnetization transfer (MT) imaging are well established for tissue characterization in various pathologies individually. However, the effect of suppression of macromolecules on applying MT pulse on signals associated with DW imaging and resulting change in the apparent diffusion coefficient (ADC) of water molecules has not been demonstrated previously. In the present study, we have performed DW echo planar imaging (EPI) with and without MT preparation pulse to see the effect of macromolecular signal suppression on ADC. A total of 10 normal volunteers and 20 patients with different intracranial cystic lesions [abscesses (n=10), cystic tumors (n=5), arachnoid cysts (n=5)] were subjected to DW imaging (b=0 and 1000 s/mm(2)) with and without MT saturation pulse. Analysis of region of interest (ROI) from different areas of white matter in normal volunteers and in the wall and cavity of cystic lesions in patients was carried out for calculating the ADC values. We found a significant increase (P<.05) in the ADC values in brain parenchyma and cavity of those intracranial cystic lesions having considerable amount of proteins after the application of MT preparation pulse except for arachnoid cysts. This is due to the size of the macromolecules present in the normal and abnormal tissue. Our studies suggest that this technique is likely to give a novel image contrast and may be of value in improving the tissue specificity in pathologies associated with variable macromolecular size.