Short echo time MR spectroscopy of brain tumors: grading of cerebral gliomas by correlation analysis of normalized spectral amplitudes

GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma

Purpose

There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions. However, the use of advanced MRI in clinical practice remains scarce due to the downstream effects of siloed glioma imaging research with limited representation of MRI specialists in established consortia; and the associated lack of available tools and expertise in clinical settings. These shortcomings delay the translation of scientific breakthroughs into novel treatment strategy. As a response we have developed the network “Glioma MR Imaging 2.0” (GliMR) which we present in this article.

Methods

GliMR aims to build a pan-European and multidisciplinary network of experts and accelerate the use of advanced MRI in glioma beyond the current “state-of-the-art” in glioma imaging. The Action Glioma MR Imaging 2.0 (GliMR) was granted funding by the European Cooperation in Science and Technology (COST) in June 2019.

Results

GliMR’s first grant period ran from September 2019 to April 2020, during which several meetings were held and projects were initiated, such as reviewing the current knowledge on advanced MRI; developing a General Data Protection Regulation (GDPR) compliant consent form; and setting up the website.

Conclusion

The Action overcomes the pre-existing limitations of glioma research and is funded until September 2023. New members will be accepted during its entire duration.

Role of Multivoxel Intermediate TE 2D CSI MR Spectroscopy and 2D Echoplanar Diffusion Imaging in Grading of Primary Glial Brain Tumours

INTRODUCTION

Preoperative tumour grading is imperative owing to difference in invasive, aggressive tendencies of different grades of glial tumours implying varied prognosis, therapeutic options. Histopathological examination has inherent sampling errors. Magnetic Resonance Spectroscopy (MRS) and Diffusion Weighted Imaging (DWI) can provide non invasive information about internal mileu hence, aiding in tumour grading by adding to information provided by conventional MRI sequences.

AIM

To evaluate the role of multivoxel intermediate TE 2D CSI MRS and 2D echoplanar diffusion imaging in grading of primary glial brain tumours.

MATERIAL AND METHODS

A prospective study was conducted in Department of Radiology, Teerthanker Mahaveer Medical College and Research Centre, Uttar Pradesh, India, from April 2015 to August 2016 after obtaining necessary approvals from Institutional Ethical Committee and written informed consent from all participants on histopathological proven cases of glial brain tumours that underwent multivoxel MRS using intermediate TE 2D chemical shift imaging and DWI using 2D echoplanar imaging. Tumour grade calculated on MRI using MRS and DWI was compared with histopathological grading. Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, specificity and accuracy were calculated for each parameter and statistical significance was evaluated using two tailed Pearson test.

RESULTS

Choline: N Acetyl aspartate (Cho: NAA) and Choline: creatinine (Cho: Cr) ratios from MRS as well as Apparent Diffusion Coffecient (ADC) values from DWI were significantly higher with increasing severity of tumour grade. Accuracy of 58.6% was obtained with DWI while it was 83% with MRS. MRS and DWI used together provided 88.4% accuracy. All parameters evaluated showed statistical significance.

CONCLUSION

Both DWI as well as MRS were found to have statistically significant roles in grading of glial brain tumours. MRS was found to be more useful than DWI.

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.

Early prediction of response to Vorinostat in an orthotopic rat glioma model

Glioblastoma is the most common primary brain tumor and is uniformly fatal despite aggressive surgical and adjuvant therapy. As survival is short, it is critical to determine the value of therapy early on in treatment. Improved early predictive assessment would allow neuro-oncologists to personalize and adjust or change treatment sooner to maximize the use of efficacious therapy. During carcinogenesis, tumor suppressor genes can be silenced by aberrant histone deacetylation. This epigenetic modification has become an important target for tumor therapy. Suberoylanilide hydroxamic acid (SAHA, Vorinostat, Zolinza) is an orally active, potent inhibitor of histone deacetylase (HDAC) activity. A major shortcoming of the use of HDAC inhibitors in the treatment of patients with brain tumors is the lack of reliable biomarkers to predict and determine response. Histological evaluation may reflect tumor viability following treatment, but is an invasive procedure and impractical for glioblastoma. Another problem is that response to SAHA therapy is associated with tumor redifferentiation and cytostasis rather than tumor size reduction, thus limiting the use of traditional imaging methods. A noninvasive method to assess drug delivery and efficacy is needed. Here, we investigated whether changes in (1)H MRS metabolites could render reliable biomarkers for an early response to SAHA treatment in an orthotopic animal model for glioma. Untreated tumors exhibited significantly elevated alanine and lactate levels and reduced inositol, N-acetylaspartate and creatine levels, typical changes reported in glioblastoma relative to normal brain tissues. The (1)H MRS-detectable metabolites of SAHA-treated tumors were restored to those of normal-like brain tissues. In addition, reduced inositol and N-acetylaspartate were found to be potential biomarkers for mood alteration and depression, which may also be alleviated with SAHA treatment. Our study suggests that (1)H MRS can provide reliable metabolic biomarkers at the earliest stage of SAHA treatment to predict the therapeutic response.

Spectroscopic evaluation of glioma grading at 3T: the combined role of short and long TE

Purpose. To evaluate the diagnostic value of 3T ¹H-MRS in grading cerebral gliomas using short and long echo times. Methods. 1H-MRS was performed on 71 patients with untreated cerebral gliomas. Metabolite ratios of NAA/Cr, Cho/Cr, Cho/NAA, and mI/Cr were calculated for short and long TE and compared between low and high grade gliomas. Lipids were qualitatively evaluated. ROC analysis was performed to obtain the cut-off values for the metabolic ratios presenting statistical difference between the two glioma grades. Results. Intratumoral Cho/Cr at both TEs and long TE Cho/NAA were significantly different between low and high grade gliomas. Peritumoral NAA/Cr of both TEs, as well as long TE Cho/Cr and Cho/NAA ratios, significantly differentiated the two tumor grades. Diagnostic sensitivity of peritumoral short TE NAA/Cr proved to be superior over the other metabolic ratios, whereas intratumoral short TE Cho/Cr reached the highest levels of specificity and accuracy. Overall, short TE 1H-MRS reached higher total sensitivity in predicting glioma grade, over long TE. Conclusion. An advantage was found in using short TE over long TE 1H-MRS in the discrimination of low versus high grade gliomas. Moreover, the results suggested that the peritumoral area of gliomas may be more valuable in predicting glioma grade than using only the intratumoral area.

Classification of single-voxel 1H spectra of brain tumours using LCModel

This study presents a novel method for the direct classification of (1)H single-voxel MR brain tumour spectra using the widespread analysis tool LCModel. LCModel is designed to estimate individual metabolite proportions by fitting a linear combination of in vitro metabolite spectra to an in vivo MR spectrum. In this study, it is used to fit representations of complete tumour spectra and to perform a classification according to the highest estimated tissue proportion. Each tumour type is represented by two spectra, a mean component and a variability term, as calculated using a principal component analysis of a training dataset. In the same manner, a mean component and a variability term for normal white matter are also added into the analysis to allow a mixed tissue approach. An unbiased evaluation of the method is carried out through the automatic selection of training and test sets using the Kennard and Stone algorithm, and a comparison of LCModel classification results with those of the INTERPRET Decision Support System (IDSS) which incorporates an advanced pattern recognition method. In a test set of 46 spectra comprising glioblastoma multiforme, low-grade gliomas and meningiomas, LCModel gives a classification accuracy of 90% compared with an accuracy of 95% by IDSS.

Use of in vivo two-dimensional MR spectroscopy to compare the biochemistry of the human brain to that of glioblastoma

PURPOSE

To develop an in vivo two-dimensional localized correlation spectroscopy technique with which to monitor the biochemistry of the human brain and the pathologic characteristics of diseases in a clinically applicable time, including ascertainment of appropriate postprocessing parameters with which to allow diagnostic and prognostic molecules to be measured, and to investigate how much of the chemical information, known to be available from malignant cultured cells, could be recorded in vivo from human brain.

MATERIALS AND METHODS

The study was approved by the institutional review board and was compliant with HIPAA. With use of a 3.0-T clinical magnetic resonance (MR) unit and a 32-channel head coil, localized correlation spectroscopy was performed in six healthy control subjects and six patients with glioblastoma multiforme (GBM) with an acquisition time of 11 minutes. Two-dimensional spectra were processed and analyzed and peak volume ratios were tabulated. The data used were proved to be normally distributed by passing the Shapiro-Wilk normality test. The first row of the spectra was extracted to examine diagnostic features. The pathologic characteristics and grade of each GBM were determined after biopsy or surgery. Statistically significant differences were assessed by using a t test.

RESULTS

The localized correlation spectroscopy method assigned biochemical species from the healthy human brain. The correlation spectra of GBM were of sufficiently high quality that many of the cross peaks, recorded previously from malignant cell models in vitro, were observed, demonstrating a statistically significant difference (P < .05) between the cross peak volumes measured for healthy subjects and those with GBM (which include lipid, alanine, N-acetylaspartate, γ-aminobutyric acid, glutamine and glutamate, glutathione, aspartate, lysine, threonine, total choline, glycerophosphorylcholine, myo-inositol, imidazole, uridine diphosphate glucose, isocitrate, lactate, and fucose). The first row of the spectra was found to contain diagnostic features.

CONCLUSION

Localized correlation spectroscopy of the human brain at 3.0 T with use of a 32-channel head coil was performed in 11 minutes and provided information about neurotransmitters, metabolites, lipids, and macromolecules. The method was able to help differentiate healthy brain from the biochemical signature of GBM in vivo. This method may, in the future, reduce the need for biopsy and is now applicable for the study of selected neurologic diseases.