Role of magnetic resonance spectroscopy in grading of primary brain tumors

Diagnostic algorithm for glioma grading using dynamic susceptibility contrast‑enhanced magnetic resonance perfusion and proton magnetic resonance spectroscopy

The present retrospective study aimed to investigate the diagnostic capacity of and design a diagnostic algorithm for dynamic susceptibility contrast-enhanced MRI (DSCE-MRI) and proton magnetic resonance spectroscopy (1H-MRS) in grading low-grade glioma (LGG) and high-grade glioma (HGG). This retrospective study enrolled 57 patients, of which 14 had LGG and 43 had HGG, five had World Health Organization grade 1, nine had grade 2, 20 had grade 3 and 23 had grade 4 glioma. All subjects underwent a standard 3T MRI brain tumor protocol with conventional MRI (cMRI) and advanced techniques, including DSCE-MRI and 1H-MRS. The associations of grade categorization with parameters in tumor and peritumor regions in the DSCE-MRI were examined, including tumor relative cerebral blood volume (TrCBV) and peripheral relative (Pr)CBV, as well as Tr and Pr cerebral blood flow (CBF) and 1H-MRS, including the creatine (Cr) and N-acetyl aspartate (NAA) ratios of choline (Cho), i.e. the TCho/NAA, PCho/NAA, TCho/Cr and PCho/Cr metabolite ratios. The data were compared using the Mann-Whitney U-test, independent samples t-test, Chi-square test, Fisher's exact test and receiver operating characteristic curve analyses. Decision tree analysis established an algorithm based on cutoffs for specified significant parameters. The PrCBF had the highest performance in the preoperative prediction of histological glioma grading, followed by the TrCBV, PrCBF, TrCBV, PCho/NAA, PCho/Cr, TCho/NAA and TCho/Cr. An algorithm based on TrCBV, PrCBF and TCho/Cr had a diagnostic accuracy of 100% for LGG and 90.7% for HGG and a misclassification risk of 7%. The cutoffs (sensitivity and specificity) were 2.48 (86 and 100%) for TrCBV, 1.26 (83.7 and 100%) for PrCBF and 3.18 (69.8 and 78.6%) for TCho/Cr. In conclusion, the diagnostic algorithm using TrCBV, PrCBF and TCho/Cr values, which were obtained from DSCE-MRI and 1H-MRS, increased diagnostic accuracy to 100% for LGGs and 90.7% for HGGs compared to previous studies using conventional MRI. This non-invasive advanced MRI diagnostic algorithm is recommended for clinical application for constructing preoperative strategies and prognosis of patients with glioma.

Unveiling the etiological impact of GST-M1, GST-T1, and P53 genotypic variations on brain carcinogenesis

BACKGROUND

Functional variants of glutathione-S-transferase (GST)-M1, GST-T1, p53 might modulate brain cancer risk by altering the rate of metabolism and clearance of carcinogens from the brain tissue. In this study, the role of GST-M1, GST-T1, p53 polymorphisms on brain tumor was investigated.

METHODS AND RESULTS

Brain tumor tissues of 143 patients were obtained from the Gulhane Training and Research Hospital, Department of Neurosurgery between 2019 and 2020. In the xenobiotic mechanism, the null allele frequency in the GST-T1, GST-M1 gene regions of Phase II enzymes by qPCR method were investigated. Single nucleotide polymorphism encoding Arg/Pro conversion in the p53 gene region was analyzed in 120 cases by sequence analysis method. The data were analyzed statistically with patient's demographic and clinical data. GST-M1, GST-T1, p53 genotypes of the patient group were determined. The most frequent genotype was null genotype (0/0) for GST-M1 (χ2 = 39.756, p < 0.001). GST-M1 genotype frequencies were 30.8%, 23.1%, 44.3% for 1/1, 1/0, 0/0, respectively. The most frequent genotype was GST-T1 1/1 following by GST-T1 1/0 (χ2 = 0.335, p = 0.846). GST-T1 genotype frequencies were 64.3%, 30.8%, 4.9% for 1/1, 1/0, 0/0, respectively. GST-M1 null genotype might be associated with the development of brain tumors. Genotype distribution obtained in p53 exon 4 codon 72; Arg/Arg was determined as 31 (25.8%), Arg/Pro 70 (58.3%), and Pro/Pro 19 (15.8%) in the case group, while there were 18 (38.3%), 23 (48.9%), and 6 (12.8%) respectively in the control group. However, the genotype distribution of p53 exon 4 codon 72 among tumorous tissue did not significantly vary from healthy control tissues (χ²=2.536, p = 0.281).

CONCLUSION

The null allele frequency encountered in the GST-M1, GST-T1 gene regions is consistent with the rates in the gene pool called Caucasian in the literature. GST-M1 gene polymorphism may play a crucial role in brain carcinogenesis in Turkish patients. This study based on clinical data is thought to help to understand the important epidemiological features of brain tumors.

Implantable NMR Microcoils in Rats: A New Tool for Exploring Tumor Metabolism at Sub-Microliter Scale?

The aim of this study was to evaluate the potential of a miniaturized implantable nuclear magnetic resonance (NMR) coil to acquire in vivo proton NMR spectra in sub-microliter regions of interest and to obtain metabolic information using magnetic resonance spectroscopy (MRS) in these small volumes. For this purpose, the NMR microcoils were implanted in the right cortex of healthy rats and in C6 glioma-bearing rats. The dimensions of the microcoil were 450 micrometers wide and 3 mm long. The MRS acquisitions were performed at 7 Tesla using volume coil for RF excitation and microcoil for signal reception. The detection volume of the microcoil was measured equal to 450 nL. A gain in sensitivity equal to 76 was found in favor of implanted microcoil as compared to external surface coil. Nine resonances from metabolites were assigned in the spectra acquired in healthy rats (n = 5) and in glioma-bearing rat (n = 1). The differences in relative amplitude of choline, lactate and creatine resonances observed in glioma-bearing animal were in agreement with published findings on this tumor model. In conclusion, the designed implantable microcoil is suitable for in vivo MRS and can be used for probing the metabolism in localized and very small regions of interest in a tumor.

Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue

PURPOSE

To prospectively investigate the capabilities of texture analysis (TA) based on apparent diffusion coefficient (ADC) map of the entire tumor volume and the whole volume of peri-tumoral edema, in discriminating between high-grade glioma (HGG) and low-grade glioma (LGG).

MATERIALS AND METHODS

A total of 33 patients with histopathological proven glioma were prospectively included. There were 20 men and 13 women with a mean age of 54.5±14.7 (standard deviation [SD]) years (range: 34-75years). TA parameters of whole tumor and peri-tumoral edema were extracted from the ADC map obtained with diffusion-weighted spin-echo echo-planar magnetic resonance imaging at 1.5-T. TA variables of HGG were compared to those of LGG. The optimum cut-off values of TA variables and their corresponding sensitivity, specificity and accuracy for differentiating between LGG and HGG were calculated using receiver operating characteristic curve analysis.

RESULTS

Mean and median tumoral ADC of HGG were significantly lower than those of LGG, at 1.23×10^-3 mm²/s and 1.21×10^-3 mm²/s cut-off values, yielding 70% sensitivity each (95% CI: 59-82% and 61-80%, respectively), 80% (95% CI: 79-98%) and 90% (95% CI: 82-97%) specificity, and 73% (95% CI: 66-91%) and 76% (95% CI: 72-90%) accuracy, respectively. Significant differences in tumoral and peri-tumoral kurtosis were found between HGG and LGG at 1.60 and 0.314 cut-off values yielding sensitivities of 74% (95% CI: 58-83%) and 70% (95% CI: 59-84%), specificities of 90% (95% CI: 80-95%) and 70% (95% CI: 64-83%) and accuracies of 79% (95% CI: 69-89%) and 70% (95% CI: 64-77%), respectively.

CONCLUSION

Measurements of whole tumoral and peri-tumoral TA, based on ADC maps, provide useful information that helps distinguish between HGG and LGG.

The combined role of MR spectroscopy and perfusion imaging in preoperative differentiation between high- and low-grade gliomas

Background

Brain tumors are an important health problem. The preoperative classification of gliomas by non-invasive techniques is a significant problem. Relative cerebral blood volume and spectroscopy have the ability to sample the entire lesion non-invasively. The present study aims to evaluate the combined role of dynamic susceptibility perfusion and spectroscopy in the classification of primary brain tumors. The combination of both provides overall diagnostic accuracy (100%). Relative cerebral blood volume in peritumoral region plays an important additional role in this regard.

Results

On the basis of histopathology, among 50 patients with brain tumors, high-grade gliomas accounted for 58%, while low-grade gliomas accounted for 42%. The relative cerebral blood volume in the tumor had the best sensitivity, specificity, and accuracy of 96.8%, 95.3%, and 96, respectively. The use of relative cerebral blood volume and choline/N-acetyl Aspartate increased diagnostic accuracy by 100%.

Conclusion

The combination of magnetic resonance spectroscopy and perfusion can increase sensitivity and positive predictive value to define the degree of glioma.

Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation

Objectives

Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method—Heterogeneity Diffusion Imaging (HDI)—to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan.

Methods

Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction.

Results

The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV.

Conclusions

Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI’s clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading.