Cerebral gliomas: prospective comparison of multivoxel 2D chemical-shift imaging proton MR spectroscopy, echoplanar perfusion and diffusion-weighted MRI

Correlation of ADC values of adult brain tumors with the diagnosis and pathological grade: A cross-sectional multicenter study

Background and Aim

Brain tumors are common, requiring physicians to have a precise understanding of them for accurate diagnosis and treatment. Considering that various histological tumor types present different cellularity, we conducted this research to examine the role of apparent diffusion coefficient (ADC) values in the differential diagnosis and pathologic grading of brain tumor types.

Methods

In this cross-sectional study, we gathered pathology reports of histological samples of adult brain tumors. The tissue sample of brain tumors were examined histologically by a pathologist. The magnetic resonance imaging data of these patients were interpreted by a neuroradiologist. The measured ADC values and ADC ratios were calculated. Standard mean ADC values were expressed as 10- 6 mm2/s. The findings were compared according to the histological diagnosis of each tumor.

Results

Sixty-eight patients were included in the study: 34 (50%) were male, and 34 (50%) were female. The average age of the patients was 51.69 + 16.40 years. In the examination of tumor type, 16 (23.5%) were astrocytoma, 9 (13.2%) were oligodendroglioma, 20 (29.4%) were glioblastoma, 4 (5.9%) were medulloblastoma, and 19 (27.9%) were metastatic tumors. the average value of ADC was statistically significantly different according to the pathological type of tumor (p < 0.001). The two-by-two comparison of average ADC among tumor types revealed significant differences, except for oligodendroglioma and glioblastoma (p-value = 0.87) and glioblastoma and medulloblastoma (p-value = 0.347). The average value of ADC and ADC ratio was statistically significantly different according to the pathological grade of the tumor (p < 0.001). In the two-by-two comparison of average ADC between all pathological grades of the tumor showed a significance difference except for Grade I and Grade II (p-value = 0.355). The mean value of ADC and ADC ratio for glioblastoma and metastatic tumors showed no significant difference.

Conclusion

The assessment of brain tumor grade through ADC examination will help to estimate prognosis and devising suitable therapeutic strategies.

Phase-sensitive deep reconstruction method for rapid multiparametric MR fingerprinting and quantitative susceptibility mapping in the brain

INTRODUCTION

This study explores the potential of Magnetic Resonance Fingerprinting (MRF) with a novel Phase-Sensitivity Deep Reconstruction Network (PS-DRONE) for simultaneous quantification of T1, T2, Proton Density, B1+, phase and quantitative susceptibility mapping (QSM).

METHODS

Data were acquired at 3 T in vitro and in vivo using an optimized EPI-based MRF sequence. Phantom experiments were conducted using a standardized phantom for T1 and T2 maps and a custom-made agar-based gadolinium phantom for B1 and QSM maps. In vivo experiments included five healthy volunteers and one patient diagnosed with brain metastasis. PSDRONE maps were compared to reference maps obtained through standard imaging sequences.

RESULTS

Total scan time was 2 min for 32 slices and a resolution of [1 mm, 1 mm, 4.5 mm]. The reconstruction of T1, T2, Proton Density, B1+ and phase maps were reconstructed within 1 s. In the phantoms, PS-DRONE analysis presented accurate and strongly correlated T1 and T2 maps (r = 0.99) compared to the reference maps. B1 maps from PS-DRONE showed slightly higher values, though still correlated (r = 0.6) with the reference. QSM values showed a small bias but were strongly correlated (r = 0.99) with reference data. In the in vivo analysis, PS-DRONE-derived T1 and T2 values for gray and white matter matched reference values in healthy volunteers. PS-DRONE B1 and QSM maps showed strong correlations with reference values.

CONCLUSION

The PS-DRONE network enables concurrent acquisition of T1, T2, PD, B1+, phase and QSM maps, within 2 min of acquisition time and 1 s of reconstruction time.

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).

Glutamic Acid and Total Creatine as Predictive Markers for Epilepsy in Glioblastoma by Using Magnetic Resonance Spectroscopy Before Surgery

OBJECTIVE

Epilepsy in glioblastoma patients significantly reduces their quality of life; however, little is known about the association between predicting epilepsy and metabolites in tumors. In this study, we used 3.0-T magnetic resonance imaging and ¹H-magnetic resonance spectroscopy (MRS) to quantify metabolite concentrations in patients with varying epilepsy histories.

METHODS

Fifty-one patients with glioblastoma underwent pretreatment 3.0-T MRI/¹H-MRS scanning. Single-voxel (1.5 cm³) MRS, in an enhanced lesion, was acquired using a double-echo point-resolved spectroscopic sequence with chemical-shift selective water suppression. MRS data were quantified with linear combination model (LC-Model) software. We compared the MRS data between groups with and without epilepsy during the postoperative course (EP).

RESULTS

The ratios of glutamate (Glu) and glutamate + glutamine (Glx) to total creatine (Glu/tCr and Glx/tCr) in the tumor were associated with epilepsy history. The receiver operating characteristic curve analysis showed that a Glu/tCr value of 1.81 was 70% sensitive and 90% specific for the prediction of EP (area under curve: 0.82). In the analysis excluding patients with preoperative epilepsy, a Glu/tCr value of 1.81 was 75% sensitive and 88% specific for the prediction (area under curve: 0.87).

CONCLUSIONS

Intratumoral metabolite concentrations measured using pretreatment 3.0-T MRI/¹H-MRS changed characteristically in the group with EP. Our study suggests that the Glu/tCr ratio in tumors has adequate reliability in predicting EP. Pretreatment MRS is a minimally invasive and simple procedure that can provide useful information on glioblastoma patients.

Emerging MR Imaging and Spectroscopic Methods to Study Brain Tumor Metabolism

Proton magnetic resonance spectroscopy (1H-MRS) provides a non-invasive biochemical profile of brain tumors. The conventional 1H-MRS methods present a few challenges mainly related to limited spatial coverage and low spatial and spectral resolutions. In the recent past, the advent and development of more sophisticated metabolic imaging and spectroscopic sequences have revolutionized the field of neuro-oncologic metabolomics. In this review article, we will briefly describe the scientific premises of three-dimensional echoplanar spectroscopic imaging (3D-EPSI), two-dimensional correlation spectroscopy (2D-COSY), and chemical exchange saturation technique (CEST) MRI techniques. Several published studies have shown how these emerging techniques can significantly impact the management of patients with glioma by determining histologic grades, molecular profiles, planning treatment strategies, and assessing the therapeutic responses. The purpose of this review article is to summarize the potential clinical applications of these techniques in studying brain tumor metabolism.

Imaging of the Primary Visual Pathway based on Visual Deficits

Vision loss can occur due to a variety of etiologies along the primary visual pathway. Understanding the anatomic organization of the visual pathway, which spans the globe to the occipital cortex, can help tailor neuroimaging to identify the cause of visual dysfunction. In this review, relevant anatomy and optimization of computed tomography and magnetic resonance imaging techniques will be described. This will be followed by a discussion of imaging findings related to pathologies at each functional anatomic level.

The utility of magnetic resonance spectroscopy in frame-less stereotactic needle biopsy of glioma

OBJECTIVE

Proton magnetic resonance spectroscopy (¹H-MRS) can benefit the differentiation of gliomas preoperative grading and facilitate guiding biopsy. This study was to investigate the optimal metabolite or metabolic ratios of MRS for the biopsy target delineating by using the technique of MRS imaging guided frame-less stereotactic biopsy.

METHODS

During a 4 year period between the Sep 2012 and Oct 2016, 57 patients (25 women, 32 men; mean age, 46.4) with histologic diagnosis of glioma, who underwent the ¹H-MRS imaging guided frameless stereotactic biopsy, were retrospectively reviewed. The metabolite or metabolic ratios values of MRS was measured. And the sensitivity, specificity, accuracy as well as the area under the curve (AUC) of those parameters for glioma grading are calculated based on the receiver operating characteristic curve (ROC) analysis.

RESULTS

65 stereotactic biopsy samples from 57 patients were histopathologically clarified to HGGs (25) or LGGs (40) for quantitative analysis. The Cho, Cho/NAA and Cho/Cr values of LGGs group were significantly lower than that of HGGs (P = 0.09, 0.001, 0.003), and the NAA value of LGGs group was significantly higher than that of HGGs (P = 0.001). The cutoff value of 3.65 for the Cho/NAA ratio provided the best combination of sensitivity (92.0%), specificity (95.0%), and diagnostic accuracy (93.8%) for identifying glioma grade, which was superior to other parameters.

CONCLUSION

The results of our study provided evidence that Cho/NAA ratio had the superior diagnostic performance in distinguishing glioma grade, indicating that the spot of highest Cho/NAA ratio was optimal metabolic targets for spectroscopic guided tissue sampling in homogenous glioma.

Histogram-based analysis of cerebral blood flow using arterial spin labeling MRI in de novo brain gliomas: relationship to histopathologic grade and molecular markers

PURPOSE

We developed multiple histogram-based CBF indices and evaluated their association with histopathologic grade in de novo brain tumor patients. Furthermore, the associations between these advanced CBF indices and molecular markers, including IDH1 mutation, ATRX loss, and 1p/19q co-deletion were also investigated.

METHODS

Thirteen de novo brain tumor patients (age 21-68 years, 9 M/4F) who were enrolled in our prospective study were scanned on 3 T MRI using a pCASL perfusion sequence following IRB-approved written informed consent. All patients have since undergone surgical intervention with tissue sampling for histopathologic tumor grading and molecular marker assessment. Tumor region of interest (ROI) were manually delineated on FLAIR images including the full extent of the tumor and peritumoral edema. Fourteen rCBF indices were derived from the histogram of the voxels with the ROI. Multi-linear regression was then used to compare rCBF indices with histopathologic tumor grade and molecular markers.

RESULTS

Averaged rCBF in top 10 and top 20 voxels (p < 0.004), but not the entire tumor ROI, was positively associated with WHO tumor grade. After accounting for tumor grade, the presence of 1p/19q co-deletion was associated with higher rCBF in top voxels, as well as with standard deviation of rCBF in the tumor ROI (p < 0.001). ATRX retention was related to higher rCBF, and this effect appears to be present in both higher-perfusion (p < 0.004) and low-perfusion (p < 0.05) voxels. IDH mutation was not significantly associated with any of the CBF indices investigated.

CONCLUSION

ASL MRI may provide useful supplemental noninvasive imaging assessment of brain tumor grade and molecular marker status.

Magnetic Resonance Spectroscopy for Prediction of Grades of Diffusely Infiltrating Intracranial Astrocytomas

Objective Gliomas, the most frequent primary brain tumors, have various grades, among which grade II, III, and IV are diffusely infiltrating astrocytomas. As therapeutic approaches and outcome differ considerably, depending on the grade of these tumors, prediction is important regarding outcome. Magnetic resonance spectroscopy (MRS) can be of help in understanding of the biochemical changes of pathological state to study, monitor, predict grading and outcome of gliomas.

Materials and Methods All the 30 patients in the study with intracranial diffusely infiltrating astrocytoma had MRS study using 1.5 Tesla MR scanner. The study population was divided into three groups on the basis of the grades of the tumor according to histopathology. Mean height of choline (Ch), N-acetyl aspartate (NAA), creatine (Cr) peak, and choline/creatine (Ch/Cr) ratio was documented. Mean value of each variable among three grades was analyzed and compared with analysis of variance (ANOVA) test (F-test).

Results There was positive relationship regarding Ch/Cr ratio among astrocytomas grade II to IV and the result was significant (p = 0.047). A positive relationship of Ch peak was also observed for grade II to IV astrocytoma, though the result was not significant (p = 0.578). The result was nonsignificant for NAA (p = 0.696) and Cr (p = 0.740) peak also.

Conclusion We recommend Ch/Cr ratio to be considered as a dependable MRS data to comment on low- or high-grade astrocytomas. MRS can be done as a routine investigation in the developing countries for astrocytomas where other facilities are less accessible for reliable prediction and counseling.

Efficiency of High and Standard b Value Diffusion-Weighted Magnetic Resonance Imaging in Grading of Gliomas

Background

Glioma is the most common fatal malignant tumor of the CNS. Early detection of glioma grades based on diffusion-weighted imaging (DWI) properties is considered one of the most recent noninvasive promising tools in the assessment of glioma grade and could be helpful in monitoring patient prognosis and response to therapy.

Aim

This study aimed to investigate the accuracy of DWI at both standard and high b values (b = 1000 s/mm² and b = 3000 s/mm²) to distinguish high-grade glioma (HGG) from low-grade glioma (LGG) in clinical practice based on histopathological results.

Materials and Methods

Twenty-three patients with glioma had DWI at l.5 T MR using two different b values (b = 1000 s/mm² and b = 3000 s/mm²) at Al-Shifa Medical Complex after obtaining ethical and administrative approvals, and data were collected from March 2019 to March 2020. Minimum, maximum, and mean of apparent diffusion coefficient (ADC) values were measured through drawing region of interest (ROI) on a solid part at ADC maps. Data were analyzed by using the MedCalc analysis program, version 19.0.4, receiver operating characteristic (ROC) curve analysis was done, and optimal cutoff values for grading gliomas were determined. Sensitivity and specificity were also calculated.

Results

The obtained results showed the ADC_mean, ADC_ratio, ADC_max, and ADC_min were performed to differentiate between LGG and HGG at both standard and high b values. Moreover, ADC values were inversely proportional to glioma grade, and these differences are more obvious at high b value. Minimum ADC values using standard b value were 1.13 ± 0.17 × 10⁻³ mm²/s, 0.89 ± 0.85 × 10⁻³ mm²/s, and 0.82 ± 0.17 × 10⁻³ mm²/s for grades II, III, and IV, respectively. Concerning high b value, ADC_min values were 0.76 ± 0.07 × 10⁻³ mm²/s, 0.61 ± 0.01 × 10⁻³ mm²/s, and 0.48 ± 0.07 × 10⁻³ mm²/s for grades II, III, and IV, respectively. ADC values were inversely correlated with results of glioma grades, and the correlation was stronger at ADC₃₀₀₀ (r = −0.722, P ≤ 0.001). The ADC₃₀₀₀ achieved the highest diagnostic accuracy with an area under the curve (AUC) of 0.618, 100% sensitivity, 85.7% specificity, and 85.7% accuracy for glioma grading at a cutoff point of ≤0.618 × 10⁻³ mm²/s. The high b value showed stronger agreement with histopathology compared with standard b value results (k = 0.89 and 0.79), respectively.

Conclusion

The ADC values decrease with an increase in tumor cellularity. Meanwhile, high b value provides better tissue contrast by reflecting more tissue diffusivity. Therefore, ADC-derived parameters at high b value are more useful in the grading of glioma than those obtained at standard b value. They might be a better surrogate imaging sequence in the preoperative evaluation of gliomas.

Multiparametric MRI: practical approach and pictorial review of a useful tool in the evaluation of brain tumours and tumour-like lesions

MRI has a vital role in the assessment of intracranial lesions. Conventional MRI has limited specificity and multiparametric MRI using diffusion-weighted imaging, perfusion-weighted imaging and magnetic resonance spectroscopy allows more accurate assessment of the tissue microenvironment. The purpose of this educational pictorial review is to demonstrate the role of multiparametric MRI for diagnosis, treatment planning and for assessing treatment response, as well as providing a practical approach for performing and interpreting multiparametric MRI in the clinical setting. A variety of cases are presented to demonstrate how multiparametric MRI can help differentiate neoplastic from non-neoplastic lesions compared to conventional MRI alone.

Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: Systematic review and meta-analysis

OBJECTIVE

Quantitative apparent diffusion coefficient (ADC) values of diffusion weighted imaging (DWI) could be applied to grade gliomas. This meta-analysis was conducted to assess the accuracy of ADC analysis in differentiating high-grade (HGGs) from low-grade gliomas (LGGs).

METHODS

PubMed, Cochrane library, Science Direct, and Embase were searched to identify suitable studies up to September 1, 2018. The quality of studies was evaluated by the quality assessment of diagnostic accuracy studies (QUADAS 2). We estimated the pooled sensitivity, specificity, positive and negative likelihood ratios (LR), diagnostic accuracy ratio (DOR) with 95% confidence intervals (CI), and determined the accuracy of the data by using the summary receiver operating characteristic (SROC) and calculating the area under the curve (AUC) to identity the accuracy of ADC analysis in grading gliomas.

RESULTS

Eighteen studies including 1172 patients were included and analyzed. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC with 95% CIs of DWI with b values of 1000 s/mm for separating HGGs from LGGs were 0.81 (95% CI 0.75-0.86), 0.87 (95% CI 0.81-0.91), 6.1 (95% CI 4.2-8.9), 0.22 (95% CI 0.17-0.29), 28 (95% CI 17-45), and 0.91 (95% CI 0.88-0.93), respectively. DWI with b values of 3000 s/mm showed slightly higher accuracy than that of 1000 (sensitivity 0.80, specificity 0.90 and AUC 0.92). Meta-regression analyses showed that field strengths and b values had significant impacts on diagnostic efficacy. Deeks testing confirmed no significant publication bias in all studies.

CONCLUSIONS

This meta-analysis suggested that ADC analysis of DWI have high accuracy in differentiating HGGs from LGGs. Standardized methodology is warranted to guide the use of this technique for clinical decision-making.

Perfusion and permeability MRI in glioma grading

Background

MRI is successful in showing the anatomy of probable pathologies of the central nervous system. Although it may not be sufficient to reveal physiological and metabolic changes, advanced MRI techniques, such as perfusion and permeability MRI, are the key to overcoming these limitations. The aim of this study was to detect the efficacy of permeability and perfusion MRI techniques.

Results

The study included 38 patients with a pathology result of primary brain glioma. The permeability MRI (Ktrans, Ve), perfusion MRI values (CBV, CBF), and pathology results were evaluated. The high-grade group included 22 patients, and the low-grade group, 16 patients. Mean CBV and CBF, median Ktrans, and Ve values were higher in the high-grade group. All parameters tended to elevate with grade and had a positive correlation. CBV > 2.25, with sensitivity and specificity of 100%, CBF > 2.02, with sensitivity and specificity of 100%, Ktrans > 0.043, with sensitivity of 81.82% and specificity of 100%, and Ve > 0.255, with sensitivity and specificity of 100%, can predict high grade.

Conclusion

Perfusion and permeability MRI can be used safely for the differentiation of high- and low-grade gliomas and for the prediction of glioma grades.

Clinical MR Biomarkers

Non-invasive magnetic resonance imaging (MRI) techniques are increasingly applied in the clinic with a fast growing body of evidence regarding its value for clinical decision making. In contrast to biochemical or histological markers, the key advantages of imaging biomarkers are the non-invasive nature and the spatial and temporal resolution of these approaches. The following chapter focuses on clinical applications of novel MR biomarkers in humans with a strong focus on oncologic diseases. These include both clinically established biomarkers (part 1-4) and novel MRI techniques that recently demonstrated high potential for clinical utility (part 5-7).

Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction

Glioblastoma is recognized as World Health Organization (WHO) grade IV glioma with an aggressive growth pattern. The current clinical practice in diagnosis and prognosis of Glioblastoma using MRI involves multiple steps including manual tumor sizing. Accurate identification and segmentation of multiple abnormal tissues within tumor volume in MRI is essential for precise survival prediction. Manual tumor and abnormal tissue detection and sizing are tedious, and subject to inter-observer variability. Consequently, this work proposes a fully automated MRI-based glioblastoma and abnormal tissue segmentation, and survival prediction framework. The framework includes radiomics feature-guided deep neural network methods for tumor tissue segmentation; followed by survival regression and classification using these abnormal tumor tissue segments and other relevant clinical features. The proposed multiple abnormal tumor tissue segmentation step effectively fuses feature-based and feature-guided deep radiomics information in structural MRI. The survival prediction step includes two representative survival prediction pipelines that combine different feature selection and regression approaches. The framework is evaluated using two recent widely used benchmark datasets from Brain Tumor Segmentation (BraTS) global challenges in 2017 and 2018. The best overall survival pipeline in the proposed framework achieves leave-one-out cross-validation (LOOCV) accuracy of 0.73 for training datasets and 0.68 for validation datasets, respectively. These training and validation accuracies for tumor patient survival prediction are among the highest reported in literature. Finally, a critical analysis of radiomics features and efficacy of these features in segmentation and survival prediction performance is presented as lessons learned.

Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: systematic review and meta-analysis

OBJECTIVES

Texture analysis (TA) is a method used for quantifying the spatial distributions of intensities in images using scanning software. MRI TA could be applied to grade gliomas. This meta-analysis was performed for assessing the accuracy of MRI TA in differentiating low-grade gliomas from high-grade ones.

METHODS

PubMed, Cochrane Library, Science Direct and Embase were searched for identifying suitable studies from their inception to 1 September 2018. The quality of the studies was evaluated on the basis of the Quality Assessment of Diagnostic Accuracy Studies guidelines. We estimated the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic OR (DOR) using the summary receiver operating characteristic (SROC) for identifying the accuracy of MRI TA in grading gliomas. Fagan nomogram was applied for assessing the clinical utility of TA.

RESULTS

Six studies including 440 patients were included and analysed. The pooled sensitivity, specificity, PLR, NLR and DOR with 95% CIs were 0.93 (95% CI 0.88 to 0.96), 0.86 (95% CI 0.81 to 0.89), 6.4 (95% CI 4.8 to 8.6), 0.08 (95% CI 0.05 to 0.15) and 78 (95% CI 39 to 156), respectively. The SROC curve showed an area under the curve of 0.96 (95% CI 0.93 to 0.97). Deeks test confirmed no significant publication bias in all studies. Fagan nomogram revealed that the post-test probability increased by 43% in patients with positive pre-test.

CONCLUSIONS

The findings of this meta-analysis suggested that MRI TA has high accuracy in differentiating low-grade gliomas from high-grade ones. A standardised methodology is warranted to guide the use of this technique for clinical decision-making.

Three-dimensional echo planar spectroscopic imaging for differentiation of true progression from pseudoprogression in patients with glioblastoma

Accurate differentiation of true progression (TP) from pseudoprogression (PsP) in patients with glioblastomas (GBMs) is essential for planning adequate treatment and for estimating clinical outcome measures and future prognosis. The purpose of this study was to investigate the utility of three-dimensional echo planar spectroscopic imaging (3D-EPSI) in distinguishing TP from PsP in GBM patients. For this institutional review board approved and HIPAA compliant retrospective study, 27 patients with GBM demonstrating enhancing lesions within six months of completion of concurrent chemo-radiation therapy were included. Of these, 18 were subsequently classified as TP and 9 as PsP based on histological features or follow-up MRI studies. Parametric maps of choline/creatine (Cho/Cr) and choline/N-acetylaspartate (Cho/NAA) were computed and co-registered with post-contrast T₁ -weighted and FLAIR images. All lesions were segmented into contrast enhancing (CER), immediate peritumoral (IPR), and distal peritumoral (DPR) regions. For each region, Cho/Cr and Cho/NAA ratios were normalized to corresponding metabolite ratios from contralateral normal parenchyma and compared between TP and PsP groups. Logistic regression analyses were performed to obtain the best model to distinguish TP from PsP. Significantly higher Cho/NAA was observed from CER (2.69 ± 1.00 versus 1.56 ± 0.51, p = 0.003), IPR (2.31 ± 0.92 versus 1.53 ± 0.56, p = 0.030), and DPR (1.80 ± 0.68 versus 1.19 ± 0.28, p = 0.035) regions in TP patients compared with those with PsP. Additionally, significantly elevated Cho/Cr (1.74 ± 0.44 versus 1.34 ± 0.26, p = 0.023) from CER was observed in TP compared with PsP. When these parameters were incorporated in multivariate regression analyses, a discriminatory model with a sensitivity of 94% and a specificity of 87% was observed in distinguishing TP from PsP. These results indicate the utility of 3D-EPSI in differentiating TP from PsP with high sensitivity and specificity.

Applying Amide Proton Transfer MR Imaging to Hybrid Brain PET/MR: Concordance with Gadolinium Enhancement and Added Value to [18F]FDG PET

PURPOSE

The purpose of this study is to evaluate the diagnostic concordance and metric correlations of amide proton transfer (APT) imaging with gadolinium-enhanced magnetic resonance imaging (MRI) and 2-deoxy-2-[¹⁸F-]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography (PET), using hybrid brain PET/MRI.

PROCEDURES

Twenty-one subjects underwent brain gadolinium-enhanced [¹⁸F]FDG PET/MRI prospectively. Imaging accuracy was compared between unenhanced MRI, MRI with enhancement, APT-weighted (APTW) images, and PET based on six diagnostic criteria. Among tumors, the McNemar test was further used for concordance assessment between gadolinium-enhanced imaging, APT imaging, and [¹⁸F]FDG PET. As well, the relation of metrics between APT imaging and PET was analyzed by the Pearson correlation analysis.

RESULTS

APT imaging and gadolinium-enhanced MRI showed superior and similar diagnostic accuracy. APTW signal intensity and gadolinium enhancement were concordant in 19 tumors (100 %), while high [¹⁸F]FDG avidity was shown in only 12 (63.2 %). For the metrics from APT imaging and PET, there was significant correlation for 13 hypermetabolic tumors (P < 0.05) and no correlation for the remaining six [¹⁸F]FDG-avid tumors.

CONCLUSIONS

APT imaging can be used to increase diagnostic accuracy with no need to administer gadolinium chelates. APT imaging may provide an added value to [¹⁸F]FDG PET in the evaluation of tumor metabolic activity during brain PET/MR studies.

Magnetic resonance imaging of arterial stroke mimics: a pictorial review

Acute ischaemic stroke represents the most common cause of new sudden neurological deficit, but other diseases mimicking stroke happen in about one-third of the cases. Magnetic resonance imaging (MRI) is the best technique to identify those 'stroke mimics'. In this article, we propose a diagnostic approach of those stroke mimics on MRI according to an algorithm based on diffusion-weighted imaging (DWI), which can be abnormal or normal, followed by the results of other common additional MRI sequences, such as T2 with gradient recalled echo weighted imaging (T2-GRE) and fluid-attenuated inversion recovery (FLAIR). Analysis of the signal intensity of the parenchyma, the intracranial arteries and, overall, of the veins, is crucial on T2-GRE, while anatomic distribution of the parenchymal lesions is essential on FLAIR. Among stroke mimics with abnormal DWI, T2-GRE demonstrates obvious abnormalities in case of intracerebral haemorrhage or cerebral amyloid angiopathy, but this sequence also allows to propose alternative diagnoses when DWI is negative, such as in migraine aura or headaches with associated neurological deficits and lymphocytosis (HaNDL), in which cortical venous prominence is observed at the acute phase on T2-GRE. FLAIR is also of major interest when DWI is positive by better showing evocative distribution of cerebral lesions in case of seizure (involving the hippocampus, pulvinar and cortex), hypoglycaemia (bilateral lesions in the posterior limb of the internal capsules, corona radiata, striata or splenium of the corpus callosum) or in posterior reversible encephalopathy syndrome (PRES). Other real stroke mimics such as mitochondrial myopathy, encephalopathy, lactic acidosis, stroke-like episodes (MELAS), Susac's syndrome, brain tumour, demyelinating diseases and herpes simplex encephalitis are also included in our detailed and practical algorithm. KEY POINTS: • About 30% of sudden neurological deficits are due to non-ischaemic causes. • MRI is the best technique to identify stroke mimics. • Our practical illustrated algorithm based on DWI helps to recognise stroke mimics.

The Role of Advanced Brain Tumor Imaging in the Care of Patients with Central Nervous System Malignancies

OPINION STATEMENT

T1-weighted post-contrast and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) constitute the gold standard for diagnosis and response assessment in neuro-oncologic patients but are limited in their ability to accurately reflect tumor biology and metabolism, particularly over the course of a patient's treatment. Advanced MR imaging methods are sensitized to different biophysical processes in tissue, including blood perfusion, tumor metabolism, and chemical composition of tissue, and provide more specific information on tissue physiology than standard MRI. This review provides an overview of the most common and emerging advanced imaging modalities in the field of brain tumor imaging and their applications in the care of neuro-oncologic patients.

Mutant IDH1 gliomas downregulate phosphocholine and phosphoethanolamine synthesis in a 2-hydroxyglutarate-dependent manner

BACKGROUND

Magnetic resonance spectroscopy (MRS) studies have identified elevated levels of the phospholipid precursor phosphocholine (PC) and phosphoethanolamine (PE) as metabolic hallmarks of cancer. Unusually, however, PC and PE levels are reduced in mutant isocitrate dehydrogenase 1 (IDHmut) gliomas that produce the oncometabolite 2-hydroxyglutarate (2-HG) relative to wild-type IDH1 (IDHwt) gliomas. The goal of this study was to determine the molecular mechanism underlying this unusual metabolic reprogramming in IDHmut gliomas.

METHODS

Steady-state PC and PE were quantified using 31P-MRS. To quantify de novo PC and PE synthesis, we used 13C-MRS and measured flux to 13C-PC and 13C-PE in cells incubated with [1,2-13C]-choline and [1,2-13C]-ethanolamine. The activities of choline kinase (CK) and ethanolamine kinase (EK), the enzymes responsible for PC and PE synthesis, were quantified using 31P-MR-based assays. To interrogate the role of 2-HG, we examined IDHwt cells incubated with 2-HG and, conversely, IDHmut cells treated with the IDHmut inhibitor AGI-5198. To examine the role of hypoxia-inducible factor 1-α (HIF-1α), we silenced HIF-1α using RNA interference. To confirm our findings in vivo and in the clinic, we studied IDHwt and IDHmut orthotopic tumor xenografts and glioma patient biopsies.

RESULTS

De novo synthesis of PC and PE was reduced in IDHmut cells relative to IDHwt. Concomitantly, CK activity and EK activity were reduced in IDHmut cells. Pharmacological manipulation of 2-HG levels established that 2-HG was responsible for reduced CK activity, EK activity, PC and PE. 2-HG has previously been reported to stabilize levels of HIF-1α, a known regulator of CK activity. Silencing HIF-1α in IDHmut cells restored CK activity, EK activity, PC and PE to IDHwt levels. Our findings were recapitulated in IDHmut orthotopic tumor xenografts and, most importantly, in IDHmut patient biopsies, validating our findings in vivo and in the clinic.

CONCLUSIONS

This study identifies, to our knowledge for the first time, a direct role for 2-HG in the downregulation of CK and EK activity, and thereby, PC and PE synthesis in IDHmut gliomas. These results highlight the unusual reprogramming of phospholipid metabolism in IDHmut gliomas and have implications for the identification of MRS-detectable metabolic biomarkers associated with 2-HG status.

Diagnostic performance of apparent diffusion coefficient parameters for glioma grading

This study was to evaluate the diagnostic performance of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) parameters derived from diffusion tensor imaging in the differentiation between grade II and III gliomas. The records of 60 patients (30 women, 30 men; mean age, 45.4 years) suspected of having gliomas who underwent an ADC image-guided stereotactic biopsy were retrospectively reviewed. The values of FA and ADC were measured, and the sensitivity, specificity, accuracy and area under the curve (AUC) of those parameters were calculated based on the receiver operating characteristic curve analysis. A predictive diagnostic equation was also constructed and evaluated. Significant differences in minimum ADC values were found in the quantitative analysis between the grade III and II glioma groups. The sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), accuracy and AUC for identifying grade III and II gliomas at the optimum cut-off value of 0.895 × 10^-3 mm²/s of minimum ADC were 81.0, 89.1, 77.3, 91.1, 86.6 and 0.87, respectively. The predictive diagnostic equation was superior to the single minimum ADC indicator with a sensitivity of 90.5%, a specificity of 84.8%, a PPV of 73.1%, an NPV of 95.1%, and an accuracy of 86.6%, respectively. The study provides evidence that minimum ADC values have a superior diagnostic performance in differentiating grade III and II gliomas, and the predictive diagnostic equation may be helpful in the differentiation.

Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest

The present study aimed to explore the role of texture analysis with apparent diffusion coefficient (ADC) maps based on different regions of interest (ROI) in determining glioma grade. Thirty patients with glioma underwent diffusion-weighted imaging (DWI). ADC values were determined from the following three ROIs: i) whole tumor; ii) solid portion; and iii) peritumoral edema. Texture features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs) using the non-parametric Wilcoxon rank-sum test or the unpaired Student's t-test. Receiver operating characteristic (ROC) curves were constructed to determine the optimum threshold for inhomogeneity values in discrimination of HGGs from LGGs. With a spearman rank correlation model, the aforementioned ADC inhomogeneity values were correlated with the Ki-67 labeling index. With whole tumor ROI, inhomogeneity values proved to be significantly different between HGGs and LGGs (P<0.001). With solid portion ROI, inhomogeneity and median values showed significant difference between HGGs and LGGs (P=0.001 and P=0.043, respectively). With peritumoral edema ROI, entropy and edema volume demonstrated positive results (P=0.016, P<0.001). The whole tumor inhomogeneity parameter performed with better diagnostic accuracy (P=0.048) than selecting the solid portion ROI. The association between inhomogeneity and Ki-67 labeling index was significantly positive in whole tumor and solid portion ROI (R=0.628, P<0.001 and R=0.470, P=0.009). Texture analysis of DWI based on different ROI can provide various significant parameters to evaluate tumor heterogeneity, which were correlated with tumor grade. Particularly, the inhomogeneity value derived from whole tumor ROI provided high diagnostic value and predicting the status of tumor proliferation.

Magnetic resonance perfusion for differentiating low-grade from high-grade gliomas at first presentation

BACKGROUND

Gliomas are the most common primary brain tumour. They are graded using the WHO classification system, with Grade II-IV astrocytomas, oligodendrogliomas and oligoastrocytomas. Low-grade gliomas (LGGs) are WHO Grade II infiltrative brain tumours that typically appear solid and non-enhancing on magnetic resonance imaging (MRI) scans. People with LGG often have little or no neurologic deficit, so may opt for a watch-and-wait-approach over surgical resection, radiotherapy or both, as surgery can result in early neurologic disability. Occasionally, high-grade gliomas (HGGs, WHO Grade III and IV) may have the same MRI appearance as LGGs. Taking a watch-and-wait approach could be detrimental for the patient if the tumour progresses quickly. Advanced imaging techniques are increasingly used in clinical practice to predict the grade of the tumour and to aid clinical decision of when to intervene surgically. One such advanced imaging technique is magnetic resonance (MR) perfusion, which detects abnormal haemodynamic changes related to increased angiogenesis and vascular permeability, or "leakiness" that occur with aggressive tumour histology. These are reflected by changes in cerebral blood volume (CBV) expressed as rCBV (ratio of tumoural CBV to normal appearing white matter CBV) and permeability, measured by K^trans.

OBJECTIVES

To determine the diagnostic test accuracy of MR perfusion for identifying patients with primary solid and non-enhancing LGGs (WHO Grade II) at first presentation in children and adults. In performing the quantitative analysis for this review, patients with LGGs were considered disease positive while patients with HGGs were considered disease negative.To determine what clinical features and methodological features affect the accuracy of MR perfusion.

SEARCH METHODS

Our search strategy used two concepts: (1) glioma and the various histologies of interest, and (2) MR perfusion. We used structured search strategies appropriate for each database searched, which included: MEDLINE (Ovid SP), Embase (Ovid SP), and Web of Science Core Collection (Science Citation Index Expanded and Conference Proceedings Citation Index). The most recent search for this review was run on 9 November 2016.We also identified 'grey literature' from online records of conference proceedings from the American College of Radiology, European Society of Radiology, American Society of Neuroradiology and European Society of Neuroradiology in the last 20 years.

SELECTION CRITERIA

The titles and abstracts from the search results were screened to obtain full-text articles for inclusion or exclusion. We contacted authors to clarify or obtain missing/unpublished data.We included cross-sectional studies that performed dynamic susceptibility (DSC) or dynamic contrast-enhanced (DCE) MR perfusion or both of untreated LGGs and HGGs, and where rCBV and/or K^trans values were reported. We selected participants with solid and non-enhancing gliomas who underwent MR perfusion within two months prior to histological confirmation. We excluded studies on participants who received radiation or chemotherapy before MR perfusion, or those without histologic confirmation.

DATA COLLECTION AND ANALYSIS

Two review authors extracted information on study characteristics and data, and assessed the methodological quality using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We present a summary of the study characteristics and QUADAS-2 results, and rate studies as good quality when they have low risk of bias in the domains of reference standard of tissue diagnosis and flow and timing between MR perfusion and tissue diagnosis.In the quantitative analysis, LGGs were considered disease positive, while HGGs were disease negative. The sensitivity refers to the proportion of LGGs detected by MR perfusion, and specificity as the proportion of detected HGGs. We constructed two-by-two tables with true positives and false negatives as the number of correctly and incorrectly diagnosed LGG, respectively, while true negatives and false positives are the number of correctly and incorrectly diagnosed HGG, respectively.Meta-analysis was performed on studies with two-by-two tables, with further sensitivity analysis using good quality studies. Limited data precluded regression analysis to explore heterogeneity but subgroup analysis was performed on tumour histology groups.

MAIN RESULTS

Seven studies with small sample sizes (4 to 48) met our inclusion criteria. These were mostly conducted in university hospitals and mostly recruited adult patients. All studies performed DSC MR perfusion and described heterogeneous acquisition and post-processing methods. Only one study performed DCE MR perfusion, precluding quantitative analysis.Using patient-level data allowed selection of individual participants relevant to the review, with generally low risks of bias for the participant selection, reference standard and flow and timing domains. Most studies did not use a pre-specified threshold, which was considered a significant source of bias, however this did not affect quantitative analysis as we adopted a common rCBV threshold of 1.75 for the review. Concerns regarding applicability were low.From published and unpublished data, 115 participants were selected and included in the meta-analysis. Average rCBV (range) of 83 LGGs and 32 HGGs were 1.29 (0.01 to 5.10) and 1.89 (0.30 to 6.51), respectively. Using the widely accepted rCBV threshold of <1.75 to differentiate LGG from HGG, the summary sensitivity/specificity estimates were 0.83 (95% CI 0.66 to 0.93)/0.48 (95% CI 0.09 to 0.90). Sensitivity analysis using five good quality studies yielded sensitivity/specificity of 0.80 (95% CI 0.61 to 0.91)/0.67 (95% CI 0.07 to 0.98). Subgroup analysis for tumour histology showed sensitivity/specificity of 0.92 (95% CI 0.55 to 0.99)/0.42 (95% CI 0.02 to 0.95) in astrocytomas (6 studies, 55 participants) and 0.77 (95% CI 0.46 to 0.93)/0.53 (95% CI 0.14 to 0.88) in oligodendrogliomas+oligoastrocytomas (6 studies, 56 participants). Data were too sparse to investigate any differences across subgroups.

AUTHORS' CONCLUSIONS

The limited available evidence precludes reliable estimation of the performance of DSC MR perfusion-derived rCBV for the identification of grade in untreated solid and non-enhancing LGG from that of HGG. Pooled data yielded a wide range of estimates for both sensitivity (range 66% to 93% for detection of LGGs) and specificity (range 9% to 90% for detection of HGGs). Other clinical and methodological features affecting accuracy of the technique could not be determined from the limited data. A larger sample size of both LGG and HGG, preferably using a standardised scanning approach and with an updated reference standard incorporating molecular profiles, is required for a definite conclusion.

Preoperative grading of supratentorial nonenhancing gliomas by high b-value diffusion-weighted 3 T magnetic resonance imaging

The purpose of this study was to determine the difference in discrimination between high- and low-grade supratentorial nonenhancing gliomas (HGGs and LGGs, respectively) when using apparent diffusion coefficient (ADC) values with high or standard b-value. Thirty-nine patients underwent conventional magnetic resonance imaging and diffusion-weighted imaging (DWI) with standard and high b-values (b = 1000 and 3000 s/mm², respectively). Minimum, maximum, and mean ADC values (ADC_MIN, ADC_MAX, and ADC_MEAN, respectively) were measured from ADC maps with both b-values. Receiver operating curve analysis was used to determine the cutoff ADC values for distinguishing between nonenhancing HGGs and LGGs. ADC_MIN, ADC_MAX, and ADC_MEAN values for the nonenhancing HGGs were lower than those for LGGs. These differences were much larger when a high b-value was used (all P < 0.0001) than when a standard b-value was used (P = 0.0001, <0.0001, and <0.0001, respectively). Discriminant analysis indicated that the greatest likelihood for discriminating HGGs and LGGs when ADC_MEAN was obtained with a high b-value, with cutoff value of 0.814 × 10^-3 mm²/s. ADC values obtained with a high b-value can be useful for grading and surgical management of nonenhancing HGGs and LGGs. The lowest degree of overlap was obtained when ADC_MEAN was determined with a b-value of 3000 s/mm².

Pretreatment ADC Values Predict Response to Radiosurgery in Vestibular Schwannomas

BACKGROUND AND PURPOSE

The response rate of vestibular schwannomas to radiation therapy is variable, and there are surgical options available in the event of treatment failure. The aim of this study was to determine whether pre- and posttreatment ADC values can predict the tumor response to radiation therapy.

MATERIALS AND METHODS

From a data base of 162 patients with vestibular schwannomas who underwent radiation therapy with gamma knife, CyberKnife, or fractionated stereotactic radiation therapy as the first-line therapy between January 2003 and December 2013, we found 20 patients who had pretreatment ADC values. There were 108 patients (including these 20) had serial MR images that included DWI allowing calculated ADC values from 2-132 months after radiation therapy. Two reviewers measured the mean, minimum, and maximum ADC values from elliptical ROIs that included tumor tissue only. Treatment responders were defined as those with a tumor total volume shrinkage of 20% or more after radiation therapy.

RESULTS

The pretreatment mean minimum ADC for nonresponders was 986.7 × 10^-6 mm²/s (range, 844-1230 × 10^-6 mm²/s) and it was 669.2 × 10^-6 mm²/s (range, 345-883 × 10^-6 mm²/s) for responders. This difference was statistically significant (P < .001). Using a minimum ADC value of 800 × 10^-6 mm²/s led to the correct classification of 18/20 patients based on pretreatment ADC values. The intraclass correlation between reviewers was 0.61. No posttreatment ADC values predicted response.

CONCLUSIONS

Pretreatment ADC values of vestibular schwannomas are lower in responders than nonresponders. Using a minimum ADC value of 800 × 10^-6 mm²/s correctly classified 90% of cases.

Diffusion-weighted magnetic resonance imaging in cystic renal masses

INTRODUCTION

We aimed to introduce the diagnostic value of diffusion-weighted (DWI) magnetic resonance imaging (MRI) for distinguishing benign and malignant renal cystic masses.

METHODS

Abdominal DWI-MRIs of patients with Bosniak categories 2F, 3, and 4 cystic renal masses were evaluated retrospectively. Cystic masses were assigned as benign or malignant according to histopathological or followup MRI findings and compared with apparent diffusion coefficient (ADC) values.

RESULTS

There were 30 patients (18 males and 12 females, mean age was 59.23 ± 12.08 years [range 38-83 years]) with cystic renal masses (eight Bosniak category 2F, 12 Bosniak category 3, 10 Bosniak category 4). Among them, 14 cysts were diagnosed as benign and 16 as malignant by followup imaging or histopathological findings. For the malignant lesions, the mean ADC values were lower than for benign lesions (p=0.001). An ADC value of ≤2.28 ×10^-6 mm²/s or less had a sensitivity of 75% and a specificity of 92.86% for detecting malignancy.

CONCLUSIONS

ADC can improve the diagnostic performance of MRI in the evaluation of complex renal cysts when used together with conventional MRI sequences.

'Low grade glioma': an update for radiologists

With the recent publication of a new World Health Organization brain tumour classification that reflects increased understanding of glioma tumour genetics, there is a need for radiologists to understand the changes and their implications for patient management. There has also been an increasing trend for adopting earlier, more aggressive surgical approaches to low-grade glioma (LGG) treatment. We will summarize these changes, give some context to the increased role of tumour genetics and discuss the associated implications of their adoption for radiologists. We will discuss the earlier and more radical surgical resection of LGG and what it means for patients undergoing imaging.

Multiparametric MR Imaging in the Assessment of Brain Tumors

Functional MR imaging methods make possible the quantification of dynamic physiologic processes that occur in the brain. Moreover, the use of these advanced imaging techniques in the setting of oncologic treatment of the brain is widely accepted and has found worldwide routine clinical use.

Dynamic 1H-MRS assessment of brain tumors: a novel approach for differential diagnosis of glioma

Purpose

To determine whether the changes of [Cho/NAA] ratio in patients with glioma, measured by dynamic ¹H-MRS can be used to differentiate between high-grade and low-grade gliomas.

Materials and Methods

This prospective study was approved by the institutional ethics committee. Written informed consent was obtained. Forty-nine patients with biopsy-proven glioma and 20 normal control subjects were recruited in this study. The maximum [Cho/NAA] ratios, acquired at 0 min, and at 6 min, were calculated and assessed from volume of interests (VOI) in the tumor areas and in the surrounding normal tissue for each patient. Absolute difference in the [Cho/NAA] ratios, from MRS acquired at 0 and 6 min, in high-grade glioma, low-grade glioma, and control subjects were compared.

Results

The maximum [Cho/NAA] ratio acquired from the tumor area at the 0 min is 6.08 ± 2.02, which was significantly different (p = .017) from that acquired after 6 min, 4.87 ± 2.13. The [Cho/NAA] ratio from the surrounding normal tissue area did not change significantly from spectra acquired at different times (0 min, 6 min). Absolute difference in [Cho/NAA] ratios acquired at 0 and 6 min time points were significantly higher (P < 0.001) in high-grade glioma (= 3.86 ± 3.31) than in low-grade glioma (= 0.81 ± 0.90), and control subjects (0.061 ± 0.026, P = 0.000), while there was no significantly difference in low-grade glioma and control subjects.

Conclusions

Dynamic ¹H-MRS can be useful for differential diagnosis between high-grade and low-grade gliomas as well as insight into the heterogeneity within the tumor.

Neuroimaging

Imaging is integral to the management of patients with brain tumors. Conventional structural imaging provides exquisite anatomic detail but remains limited in the evaluation of molecular characteristics of intracranial neoplasms. Quantitative and physiologic biomarkers derived from advanced imaging techniques have been increasingly utilized as problem-solving tools to identify glioma grade and assess response to therapy. This chapter provides a comprehensive overview of the imaging strategies used in the clinical assessment of patients with gliomas and describes how novel imaging biomarkers have the potential to improve patient management.

Metabolic approach for tumor delineation in glioma surgery: 3D MR spectroscopy image-guided resection

OBJECT The extent of resection is one of the most essential factors that influence the outcomes of glioma resection. However, conventional structural imaging has failed to accurately delineate glioma margins because of tumor cell infiltration. Three-dimensional proton MR spectroscopy ((1)H-MRS) can provide metabolic information and has been used in preoperative tumor differentiation, grading, and radiotherapy planning. Resection based on glioma metabolism information may provide for a more extensive resection and yield better outcomes for glioma patients. In this study, the authors attempt to integrate 3D (1)H-MRS into neuronavigation and assess the feasibility and validity of metabolically based glioma resection. METHODS Choline (Cho)-N-acetylaspartate (NAA) index (CNI) maps were calculated and integrated into neuronavigation. The CNI thresholds were quantitatively analyzed and compared with structural MRI studies. Glioma resections were performed under 3D (1)H-MRS guidance. Volumetric analyses were performed for metabolic and structural images from a low-grade glioma (LGG) group and high-grade glioma (HGG) group. Magnetic resonance imaging and neurological assessments were performed immediately after surgery and 1 year after tumor resection. RESULTS Fifteen eligible patients with primary cerebral gliomas were included in this study. Three-dimensional (1)H-MRS maps were successfully coregistered with structural images and integrated into navigational system. Volumetric analyses showed that the differences between the metabolic volumes with different CNI thresholds were statistically significant (p < 0.05). For the LGG group, the differences between the structural and the metabolic volumes with CNI thresholds of 0.5 and 1.5 were statistically significant (p = 0.0005 and 0.0129, respectively). For the HGG group, the differences between the structural and metabolic volumes with CNI thresholds of 0.5 and 1.0 were statistically significant (p = 0.0027 and 0.0497, respectively). All patients showed no tumor progression at the 1-year follow-up. CONCLUSIONS This study integrated 3D MRS maps and intraoperative navigation for glioma margin delineation. Optimum CNI thresholds were applied for both LGGs and HGGs to achieve resection. The results indicated that 3D (1)H-MRS can be integrated with structural imaging to provide better outcomes for glioma resection.

The diagnostic performance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: A systematic review and meta-analysis

OBJECTIVE

Magnetic resonance spectroscopy (MRS) is a powerful tool for preoperative grading of gliomas. We performed a meta-analysis to evaluate the diagnostic performance of MRS in differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs).

METHODS

PubMed and Embase databases were systematically searched for relevant studies of glioma grading assessed by MRS through 27 March 2015. Based on the data from eligible studies, pooled sensitivity, specificity, diagnostic odds ratio and areas under summary receiver operating characteristic curve (SROC) of different metabolite ratios were obtained.

RESULTS

Thirty articles comprising a total sample size of 1228 patients were included in our meta-analysis. Quantitative synthesis of studies showed that the pooled sensitivity/specificity of Cho/Cr, Cho/NAA and NAA/Cr ratios was 0.75/0.60, 0.80/0.76 and 0.71/0.70, respectively. The area under the curve (AUC) of the SROC was 0.83, 0.87 and 0.78, respectively.

CONCLUSIONS

MRS demonstrated moderate diagnostic performance in distinguishing HGGs from LGGs using tumoural metabolite ratios including Cho/Cr, Cho/NAA and NAA/Cr. Although there was no significant difference in AUC between Cho/Cr and Cho/NAA groups, Cho/NAA ratio showed higher sensitivity and specificity than Cho/Cr ratio and NAA/Cr ratio. We suggest that MRS should combine other advanced imaging techniques to improve diagnostic accuracy in differentiating HGGs from LGGs.

KEY POINTS

• MRS has moderate diagnostic performance in distinguishing HGGs from LGGs. • There is no significant difference in AUC between Cho/Cr and Cho/NAA ratios. • Cho/NAA ratio is superior to NAA/Cr ratio. • Cho/NAA ratio shows higher sensitivity and specificity than Cho/Cr and NAA/Cr ratios. • MRS should combine other advanced imaging techniques to improve diagnostic accuracy.

[(18)F]-fluoro-L-thymidine PET and advanced MRI for preoperative grading of gliomas

Purpose

Conventional MRI based on contrast enhancement is often not sufficient in differentiating grade II from grade III and grade III from grade IV diffuse gliomas. We assessed advanced MRI, MR spectroscopy and [¹⁸F]-fluoro-l-thymidine ([¹⁸F]-FLT) PET as tools to overcome these limitations.

Methods

In this prospective study, thirty-nine patients with diffuse gliomas of grades II, III or IV underwent conventional MRI, perfusion, diffusion, proton MR spectroscopy (¹H-MRS) and [¹⁸F]-FLT-PET imaging before surgery. Relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC), Cho/Cr, NAA/Cr, Cho/NAA and FLT-SUV were compared between grades.

Results

Cho/Cr showed significant differences between grade II and grade III gliomas (p = 0.03). To discriminate grade II from grade IV and grade III from grade IV gliomas, the most relevant parameter was the maximum value of [¹⁸F]-FLT uptake FLT_max (respectively, p < 0.001 and p < 0.0001). The parameter showing the best correlation with the grade was the mean value of [¹⁸F]-FLT uptake FLT_mean (R² = 0.36, p < 0.0001) and FLT_max (R² = 0.5, p < 0.0001).

Conclusion

Whereas advanced MRI parameters give indications for the grading of gliomas, the addition of [¹⁸F]-FLT-PET could be of interest for the accurate preoperative classification of diffuse gliomas, particularly for identification of doubtful grade III and IV gliomas.

•

Comparison of advanced MRI and FLT PET in glioma grading

•

FLT shows the best correlation with glioma grade.

•

Both MRI and PET should be used for doubtful patients.

Early Cerebral Blood Volume Changes Predict Progression After Convection-Enhanced Delivery of Topotecan for Recurrent Malignant Glioma

OBJECTIVE

To assess whether early changes in enhancing tumor volume (eTV) and relative cerebral blood volume (rCBV) 1 month after convection-enhanced delivery of topotecan in patients with recurrent malignant glioma correlated with 6-month disease progression status.

METHODS

Sixteen patients were enrolled in a Phase Ib trial of convection-enhanced delivery of topotecan for recurrent malignant glioma. Each patient was evaluated with serial follow-up magnetic resonance imaging at baseline and at 4- to 8-week intervals. Changes at 1 month compared with baseline in eTV and rCBV were evaluated as potential predictors of 6-month progression status, classified as either progressive disease or nonprogressive disease. Relationships between percent changes in eTV and rCBV at 1 month with the probability of progressive disease at 6 months were estimated by the use of logistic regression analysis. Receiver operating characteristic curves for varying percent change thresholds in eTV and rCBV were evaluated by the use of 6-month progressive disease as the reference.

RESULTS

There was a significant difference in the percent change in rCBV at 1 month in patients with progressive disease compared with those with nonprogressive disease at 6 months (+12% vs. -29%, P = 0.02). Logistic regression analysis demonstrated on average that a 10% increase in rCBV at 1 month after convection-enhanced delivery of topotecan was associated with 1.7 times the odds of developing progressive disease at 6 months (95% confidence interval [CI] 1.0-2.9 P = 0.05). Receiver operating characteristic analysis for determining progressive disease at 6 months showed a greater area under the curve with rCBV (0.867; 95% CI 0.66-1.00) than with change in enhancing tumor volume (0.767; 95% CI 0.51-1.00).

CONCLUSION

In this selected population of patients with recurrent malignant glioma treated with convection-enhanced delivery of topotecan, early changes in rCBV at 4 weeks after therapy may help predict progression status at 6 months.

Differentiation of hemangioblastomas from pilocytic astrocytomas using 3-T magnetic resonance perfusion-weighted imaging and MR spectroscopy

INTRODUCTION

Hemangioblastomas and pilocytic astrocytomas (PAs) present similar imaging features on conventional MR imaging, making differential diagnosis a challenge. The purpose of this study was to evaluate the usefulness of dynamic susceptibility-weighted contrast-enhanced perfusion-weighted imaging (DSC-PWI) and proton MR spectroscopic imaging in the differentiation of hemangioblastomas and PAs.

METHODS

A 3.0-T MR imaging unit was used to perform DSC-PWI and conventional MR imaging on 14 patients with hemangioblastomas and 22 patients with PAs. Four patients with hemangioblastomas and 10 PA patients also underwent proton MR spectroscopy. Parameters of relative peak height (rPH) and relative percentage of signal intensity recovery (rPSR) were acquired by DSC-PWI and variables of N-acetylaspasrtate (NAA)/creatine (Cr), choline (Cho)/Cr, and lactate-lipid (Lac-Lip)/Cr by MR spectroscopy. The sensitivity, specificity, and the area under the receiver operating characteristic curve of all analyzed parameters at respective cutoff values were determined.

RESULTS

Higher rPH but lower rPSR values were detected in hemangioblastomas compared to PAs. The NAA/Cr ratio was significantly lower in hemangioblastomas compared with PAs. The threshold values ≥3.2 for rPH provide sensitivity, specificity, positive predictive values, and negative predictive values of 85.7, 95.5, 92.3, and 91.3%, respectively, for differentiating hemangioblastomas from PAs. The optimal threshold values were ≤0.9 for rPSR and ≤1.5 for NAA/Cr ratios in tumor.

CONCLUSION

Significantly higher rPH and lower NAA/Cr were seen in patients with hemangioblastomas when compared with PA patients, suggesting that DSC-PWI and proton MR spectroscopy are helpful in the characterization and differentiation of these two types of tumors.

Low-grade gliomas: the challenges of imaging

WHO grade II gliomas are a major challenge for magnetic resonance imaging (MRI) due to their delayed anaplastic transformation. Today it is possible to individually characterize tumor progression from diagnosis to anaplastic transformation based on the many parameters identified in studies in the literature and the possibility of integrating these data into mathematical models. Early identification of negative morphological and metabolic factors, as well as treatment follow-up, help identify predictive factors of tumor progression, as well as determine treatment response to adapt management of this disease.

Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation

The accurate diagnosis of glioma subtypes is critical for appropriate treatment, but conventional histopathologic diagnosis often exhibits significant intra-observer variability and sampling error. The aim of this study was to investigate whether histogram analysis using an automatically segmented region of interest (ROI), excluding cystic or necrotic portions, could improve the differentiation between low-grade and high-grade gliomas. Thirty-two patients (nine low-grade and 23 high-grade gliomas) were included in this retrospective investigation. The outer boundaries of the entire tumors were manually drawn in each section of the contrast-enhanced T1 -weighted MR images. We excluded cystic or necrotic portions from the entire tumor volume. The histogram analyses were performed within the ROI on normalized apparent diffusion coefficient (ADC) maps. To evaluate the contribution of the proposed method to glioma grading, we compared the area under the receiver operating characteristic (ROC) curves. We found that an ROI excluding cystic or necrotic portions was more useful for glioma grading than was an entire tumor ROI. In the case of the fifth percentile values of the normalized ADC histogram, the area under the ROC curve for the tumor ROIs excluding cystic or necrotic portions was significantly higher than that for the entire tumor ROIs (p < 0.005). The automatic segmentation of a cystic or necrotic area probably improves the ability to differentiate between high- and low-grade gliomas on an ADC map.

Comparison of multiple parameters obtained on 3T pulsed arterial spin-labeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading

BACKGROUND AND PURPOSE

Pulsed arterial spin-labeling, DTI, and MR spectroscopy provide useful data for tumor evaluation. We evaluated multiple parameters by using these pulse sequences and the Ki-67 labeling index in newly diagnosed supratentorial gliomas.

MATERIALS AND METHODS

All 32 patients, with grade II (3 each of diffuse astrocytoma, oligodendroglioma, and oligoastrocytoma), grade III (3 anaplastic astrocytomas, 4 anaplastic oligodendrogliomas, and 1 anaplastic oligoastrocytoma), and grade IV (14 glioblastomas and 1 glioblastoma with an oligodendroglioma component) cases underwent pulsed arterial spin-labeling, DTI, and MR spectroscopy studies by using 3T MR imaging. The following variables were used to compare the tumors: relative cerebral blood flow, fractional anisotropy; ADC tumor/normal ratios; and the Cho/Cr, NAA/Cho, NAA/Cr, and lactate/Cr ratios. A logistic regression and receiver operating characteristic analysis were used to assess parameters with a high sensitivity and specificity to identify the threshold values for separate grading. We compared the Ki-67 index with various MR imaging parameters in tumor specimens.

RESULTS

Significant correlations were observed between the Ki-67 index and the mean, maximum, and minimum ADC, Cho/Cr, and lactate/Cr ratios. The receiver operating characteristic analysis showed that the combination of the minimum ADC and Cho/Cr ratios could differentiate low-grade and high-grade gliomas, with a sensitivity and specificity of 87.0% and 88.9%, respectively. The mean and maximum relative cerebral blood flow ratios were used to classify glioblastomas from other-grade astrocytomas, with a sensitivity and specificity of 92.9% and 83.3%, respectively.

CONCLUSIONS

Our findings indicate that pulsed arterial spin-labeling, DTI, and MR spectroscopy are useful for predicting glioma grade. Additionally, the parameters obtained on DTI and MR spectroscopy closely correlated with the proliferative potential of gliomas.

Impacts of MR spectroscopic imaging on glioma patient management

Magnetic resonance (MR) modalities are routine imaging tools in the diagnosis and management of gliomas. MR spectroscopic imaging (MRSI), which relies on the metabolic characteristics of tissues, has been developed to accelerate the understanding of gliomas and to aid in effective clinical decision making and development of targeted therapies. In this review, the potentials and practical challenges to frequently use this technique in clinical management of gliomas are discussed. The applications of new biomarkers detectable by MRSI in differential glioma diagnosis, pre- and post-treatment evaluations, and neurosurgery are also addressed.

Comparison of perfusion, diffusion, and MR spectroscopy between low-grade enhancing pilocytic astrocytomas and high-grade astrocytomas

BACKGROUND AND PURPOSE

The differentiation of pilocytic astrocytomas and high-grade astrocytomas is sometimes difficult. There are limited comparisons in the literature of the advanced MR imaging findings of pilocytic astrocytomas versus high-grade astrocytomas. The purpose of this study was to assess the MR imaging, PWI, DWI, and MR spectroscopy characteristics of pilocytic astrocytomas compared with high-grade astrocytomas.

MATERIALS AND METHODS

Sixteen patients with pilocytic astrocytomas and 22 patients with high-grade astrocytomas (8-66 years of age; mean, 36 ± 17 years) were evaluated by using a 1.5T MR imaging unit. MR imaging, PWI, DWI, and MR spectroscopy were used to determine the differences between pilocytic astrocytomas and high-grade astrocytomas. The sensitivity, specificity, and the area under the receiver operating characteristic curve of all analyzed parameters at respective cutoff values were determined.

RESULTS

The relative cerebral blood volume values were significantly lower in pilocytic astrocytomas compared with the high-grade astrocytomas (1.4 ± 0.9 versus 3.3 ± 1.4; P = .0008). The ADC values were significantly higher in pilocytic astrocytomas compared with high-grade astrocytomas (1.5 × 10(-3) ± 0.4 versus 1.2 × 10(-3) ± 0.3; P = .01). The lipid-lactate in tumor/creatine in tumor ratios were significantly lower in pilocytic astrocytomas compared with high-grade astrocytomas (8.3 ± 11.2 versus 43.3 ± 59.2; P = .03). The threshold values ≥1.33 for relative cerebral blood volume provide sensitivity, specificity, positive predictive values, and negative predictive values of 100%, 67%, 87%, and 100%, respectively, for differentiating high-grade astrocytomas from pilocytic astrocytomas. The optimal threshold values were ≤1.60 for ADC, ≥7.06 for lipid-lactate in tumor/creatine in tumor, and ≥2.11 for lipid-lactate in tumor/lipid-lactate in normal contralateral tissue.

CONCLUSIONS

Lower relative cerebral blood volume and higher ADC values favor a diagnosis of pilocytic astrocytoma, while higher lipid-lactate in tumor/creatine in tumor ratios plus necrosis favor a diagnosis of high-grade astrocytomas.

Data-driven grading of brain gliomas: a multiparametric MR imaging study

PURPOSE

To grade brain gliomas by using a data-driven analysis of multiparametric magnetic resonance (MR) imaging, taking into account the heterogeneity of the lesions at MR imaging, and to compare these results with the most widespread current radiologic reporting methods.

MATERIALS AND METHODS

One hundred eighteen patients with histologically confirmed brain gliomas were evaluated retrospectively. Conventional and advanced MR sequences (perfusion-weighted imaging, MR spectroscopy, and diffusion-tensor imaging) were performed. Three evaluations were conducted: semiquantitative (based on conventional and advanced sequences with reported cutoffs), qualitative (exclusively based on conventional MR imaging), and quantitative. For quantitative analysis, four volumes of interest were placed: regions with contrast material enhancement, regions with highest and lowest signal intensity on T2-weighted images, and regions of most restricted diffusivity. Statistical analysis included t test, receiver operating characteristic (ROC) analysis, discriminant function analysis (DFA), leave-one-out cross-validation, and Kendall coefficient of concordance.

RESULTS

Significant differences were noted in age, relative cerebral blood volume (rCBV) in contrast-enhanced regions (cutoff > 2.59; sensitivity, 80%; specificity, 91%; area under the ROC curve [AUC] = 0.937; P = .0001), areas of lowest signal intensity on T2-weighted images (>2.45, 57%, 97%, 0.852, and P = .0001, respectively), restricted diffusivity regions (>2.61, 54%, 97%, 0.808, and P = .0001, respectively), and choline/creatine ratio in regions with the lowest signal intensity on T2-weighted images (>2.07, 49%, 88%, 0.685, and P = .0007, respectively). DFA that included age; rCBV in contrast-enhanced regions, areas of lowest signal intensity on T2-weighted images, and areas of restricted diffusivity; and choline/creatine ratio in areas with lowest signal intensity on T2-weighted images was used to classify 95% of patients correctly. Quantitative analysis showed a higher concordance with histologic findings than qualitative and semiquantitative methods (P < .0001).

CONCLUSION

A quantitative multiparametric MR imaging evaluation that incorporated heterogeneity at MR imaging significantly improved discrimination between low- and high-grade brain gliomas with a very high AUC (ie, 0.95), thus reducing the risk of inappropriate or delayed surgery, respectively.

Recognition of anaplastic foci within low-grade gliomas using MR spectroscopy

BACKGROUND

The recognition of anaplastic foci within low-grade gliomas is of extreme importance in patients under follow-up for Grade II gliomas. We present the algorithm of MR spectroscopy (MRS)-guided brain biopsy and its correlation with tumour histology.

METHODS

Twenty-seven patients harbouring suspected Grade II/III glioma were examined on our 3T MR. 2D PRESS-CSI metabolite images of Choline/Creatine, Creatine/N-acetylaspartate and Choline/N-acetylaspartate were calculated and exported to the DICOM format. According to these maps, a stereobiopsy was performed at the point of maximum Choline/Creatine ratio prior to tumour resection. In the case of enhancing tumour, a subsequent biopsy was performed from the point of enhancement. Comparisons were made between the histology of the biopsied specimens and the resected tumours.

RESULTS

Eleven tumours were diagnosed as high-grade and sixteen as low-grade lesions. The correlation between main spectroscopic ratios (Cho/Cr and Cho/NAA) was strongly positive at the points of maximum Cho/Cr. Similar results were obtained at the points of contrast enhancement. Comparison of histological parameters of biopsy samples at the points of maximum Cho/Cr and histological examination of the completely resected tumours gives a strong correlation of tumour grade, number of mitoses and Ki-67 expression. The diagnostic accuracy of MRS-guided biopsy was 84%. The absolute value of Cho/NAA was higher in high-grade compared to that of low-grade lesions. The value of Cho/NAA ratio of 0.9 using MRS produced a sensitivity and specificity of 78% in the differentiation between low-grade and high-grade lesions. Combining MRS with structural MR, the sensitivity increased to 86% and the specificity to 80%.

CONCLUSIONS

Strong correlation was demonstrated between Cho/Cr and Ch/NAA ratios. Strong correlation was demonstrated between histological parameters of biopsy samples taken using Cho/Cr ratio and those from total tumour examination. Diagnostic accuracy of MRS-guided biopsy was 84%. Sensitivity and specificity of MRS combined with structural MR reaches 86% and 80%.

Upconversion nanoparticles conjugated with Gd(3+) -DOTA and RGD for targeted dual-modality imaging of brain tumor xenografts

Glioblastoma multiforme (GBM) is the most common and malignant form of primary brain tumors in human. Small molecular magnetic resonance imaging (MRI) contrast agents are used for GBM diagnosis. However, conventional contrast agents have several limitations, such as low T1 relaxivity, short circulation half lives and absence of tumor targeting. Herein, we develop an upconversion nanoprobe labeled with Gd(3+) -DOTA and RGD (UCNP-Gd-RGD) for dual-modality imaging of glioblastoma. The preparation of UCNP-Gd-RGD starts with amine-functional upconversion nanoparticle core, followed by PEGylation, Gd(3+) DOTA conjugation and RGD labeling. The obtained UCNP-Gd-RGD has improved colloidal stability and reduced cytotoxicity compared with the UCNP core counterpart. Meanwhile, UCNP-Gd-RGD shows strong upconversion luminescence in deep-red region and three times enhancement of T1 relaxivity over Gd(3+) DOTA. Due to the recognition between UCNP-Gd-RGD and integrin αv β3 receptors, the nanoprobe specifically binds to U87MG cells, as evidenced by confocal microscopy and quantified by ICP-MS. Furthermore, UCNP-Gd-RGD demonstrates a preferential retention in subcutaneous U87MG tumor xenograft as shown in both in vivo upconversion fluorescence/MR imaging studies and ex vivo analysis. UCNP-Gd-RGD, conjugated with numerous RGD peptide and T1 contrast enhancing molecules, is promising for MR imaging of glioblastoma and delineating the tumor boundary before surgery. In addition, NIR-to-red upconversion characteristic of UCNP-Gd-RGD facilitates its potential intra-operative use for fluorescence-guided tumor resection.