High grade gliomas: The role of dynamic contrast-enhanced susceptibility-weighted perfusion MRI and proton MR spectroscopic imaging in differentiating grade III from grade IV

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.

5-Aminolevulinic Acid-Guided Resection in Grade III Tumors-A Comparative Cohort Study

BACKGROUND

The benefits of using 5-aminolevulinic acid (5-ALA) in resection of grade IV tumors have been extensively researched. However, few studies have investigated the use of 5-ALA in grade III gliomas.

OBJECTIVE

To discover whether 5-ALA provides significant benefit in assisting resection of grade III gliomas.

METHODS

A single-center, retrospective cohort study between January 2013 and January 2019 of adult patients with grade III gliomas. Patients were separated into a 5-ALA-guided surgery group (5-ALA-GS) and non-5-ALA-guided surgery group (non-5-ALA-GS). Primary outcome was overall survival (OS); secondary outcomes were both postoperative and 6-month performance status (PS6m), and extent of resection (EoR).

RESULTS

Sixty-nine patients with grade III gliomas were included (39 and 30 patients in the 5-ALA-GS group and non-5-ALA-GS group, respectively). There was no significant difference in tumor characteristics between the groups. No significant difference was observed in OS (P = .072) and EoR (P = .609) between both the groups. In a subgroup of the 5-ALA-GS where gross total resection (GTR) was achieved, there was a better OS (P = .043). Significantly worse outcomes were seen postoperatively (P = .044) and at PS6m (P = .041) in the 5-ALA-GS group, mainly because of the poorer outcome in the subtotal resection subgroup.

CONCLUSION

Despite the benefits of 5-ALA in grade IV glioma surgery, these benefits are limited to a smaller group of patients with grade III tumors where there is sufficient fluorescence to allow GTR. In this cohort of patients, 5-ALA use may result in worse neurological outcomes particularly when GTR is not feasible and therefore caution is warranted.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T

OBJECTIVE

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach.

MATERIALS AND METHODS

Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification.

RESULTS

A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features.

CONCLUSION

In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort.