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      Diagnostic Accuracy of Magnetic Resonance Spectroscopy in Predicting the Grade of Glioma Keeping Histopathology as the Gold Standard

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          Abstract

          Background

          Gliomas are the most prevalent intrinsic tumors of the central nervous system and are categorized from grade I to grade IV. Magnetic resonance imaging (MRI) provides exact diagnosis, prognosis, and assessment of tumor response to current chemotherapy/immunotherapy and radiation therapy. With histopathology serving as the gold standard, we aimed to assess the diagnostic accuracy of magnetic resonance spectroscopy (MRS) in predicting glioma grade.

          Methodology

          This cross-sectional study was conducted in the Department of Radiology, KRL Hospital, Islamabad, from December 15, 2019, to September 30, 2021. After providing written consent, 80 patients with untreated gliomas were included in this study. The voxel of interest was identified using MRI brain conventional contrast-enhanced sequences to assess the grade of the gliomas and link it to the histology report. Following this identification, tissue metabolites were calculated using MRS.

          Results

          The patients’ age ranged from 13 to 80 years, with a mean age of 49.5 years. Male patients comprised 57.5% of the total study population, while female patients comprised 42.5%. Overall, 23.75% of patients had low-grade tumors, while 76.25% had high-grade tumors. Low-grade tumors had a choline (Cho)/creatine (Cr) metabolite ratio of 1.7421, whereas high-grade tumors had an average Cho/Cr metabolite ratio of 2.5575. N-acetyl aspartate (NAA)/Cr ratio was 1.6368 in low grade and 0.6734 in high-grade tumors. Sensitivity of 77% and specificity of 84.2% were noted, with 78.75% diagnostic accuracy for the Cho/Cr ratio.

          Conclusions

          Multivoxel MRS has been shown to reliably predict the grade of gliomas despite its non-invasive nature and lack of procedural challenges. When used together Cho/Cr and NAA/Cr ratios and histopathology can accurately determine tumor grade and can be used as a supplementary non-invasive technique.

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          Most cited references23

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          Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging.

          Sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of conventional MR imaging in predicting glioma grade are not high. Relative cerebral blood volume (rCBV) measurements derived from perfusion MR imaging and metabolite ratios from proton MR spectroscopy are useful in predicting glioma grade. We evaluated the sensitivity, specificity, PPV, and NPV of perfusion MR imaging and MR spectroscopy compared with conventional MR imaging in grading primary gliomas. One hundred sixty patients with a primary cerebral glioma underwent conventional MR imaging, dynamic contrast-enhanced T2*-weighted perfusion MR imaging, and proton MR spectroscopy. Gliomas were graded as low or high based on conventional MR imaging findings. The rCBV measurements were obtained from regions of maximum perfusion. Metabolite ratios (choline [Cho]/creatine [Cr], Cho/N-acetylaspartate [NAA], and NAA/Cr) were measured at a TE of 144 ms. Tumor grade determined with the three methods was then compared with that from histopathologic grading. Logistic regression and receiver operating characteristic analyses were performed to determine optimum thresholds for tumor grading. Sensitivity, specificity, PPV, and NPV for identifying high-grade gliomas were also calculated. Sensitivity, specificity, PPV, and NPV for determining a high-grade glioma with conventional MR imaging were 72.5%, 65.0%, 86.1%, and 44.1%, respectively. Statistical analysis demonstrated a threshold value of 1.75 for rCBV to provide sensitivity, specificity, PPV, and NPV of 95.0%, 57.5%, 87.0%, and 79.3%, respectively. Threshold values of 1.08 and 1.56 for Cho/Cr and 0.75 and 1.60 for Cho/NAA provided the minimum C2 and C1 errors, respectively, for determining a high-grade glioma. The combination of rCBV, Cho/Cr, and Cho/NAA resulted in sensitivity, specificity, PPV, and NPV of 93.3%, 60.0%, 87.5%, and 75.0%, respectively. Significant differences were noted in the rCBV and Cho/Cr, Cho/NAA, and NAA/Cr ratios between low- and high-grade gliomas (P <.0001,.0121,.001, and.0038, respectively). The rCBV measurements and metabolite ratios both individually and in combination can increase the sensitivity and PPV when compared with conventional MR imaging alone in determining glioma grade. The rCBV measurements had the most superior diagnostic performance (either with or without metabolite ratios) in predicting glioma grade. Threshold values can be used in a clinical setting to evaluate tumors preoperatively for histologic grade and provide a means for guiding treatment and predicting postoperative patient outcome.
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            Incidence of gliomas by anatomic location.

            The anatomic location of a glioma influences prognosis and treatment options. The aim of our study was to describe the distribution of gliomas in different anatomic areas of the brain. A representative population-based sample of 331 adults with glioma was used for preliminary analyses. The anatomic locations for 89 patients from a single center were analyzed in more detail from radiologic imaging and recorded on a three-dimensional 1 x 1 x 1-cm grid. The age-standardized incidence rate of gliomas was 4.7 per 100,000 person-years. The most frequent subtypes were glioblastoma (47%) and grade II-III astrocytoma (23%), followed by oligodendroglioma and mixed glioma. The gliomas were located in the frontal lobe in 40% of the cases, temporal in 29%, parietal in 14%, and occipital lobe in 3%, with 14% in the deeper structures. The difference in distribution between lobes remained after adjustment for their tissue volume: the tumor:volume ratio was 4.5 for frontal, 4.8 for temporal, and 2.3 for parietal relative to the occipital lobe. The area with the densest occurrence was the anterior subcortical brain. Statistically significant spatial clustering was found in the three-dimensional analysis. No differences in location were found among glioblastoma, diffuse astrocytoma, and oligodendroglioma. Our results demonstrate considerable heterogeneity in the anatomic distribution of gliomas within the brain.
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              Imaging of brain tumors: MR spectroscopy and metabolic imaging.

              The utility of magnetic resonance spectroscopy (MRS) in diagnosis and evaluation of treatment response to human brain tumors has been widely documented. The role of MRS in tumor classification, tumors versus nonneoplastic lesions, prediction of survival, treatment planning, monitoring of therapy, and post-therapy evaluation is discussed. This article delineates the need for standardization and further study in order for MRS to become widely used as a routine clinical tool.
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                Author and article information

                Journal
                Cureus
                Cureus
                2168-8184
                Cureus
                Cureus (Palo Alto (CA) )
                2168-8184
                9 February 2022
                February 2022
                : 14
                : 2
                : e22056
                Affiliations
                [1 ] Diagnostic Radiology, KRL Hospital, Islamabad, PAK
                [2 ] General Medicine, Surrey Docks Health Centre, London, GBR
                [3 ] Public Health, Health Services Academy, Islamabad, PAK
                [4 ] Clinical Research, Maroof International Hospital, Islamabad, PAK
                [5 ] Urology, Guy's and St Thomas' NHS Foundation Trust, London, GBR
                [6 ] Physiology, Mayo Hospital, Lahore, PAK
                [7 ] House Officer, Jinnah Postgraduate Medical Centre, Karachi, PAK
                [8 ] Medical Student, Jinnah Sindh Medical University, Karachi, PAK
                Author notes
                Article
                10.7759/cureus.22056
                8916061
                35340513
                49a9a96c-65a7-4bed-ba5f-91661e2b0263
                Copyright © 2022, Rafique et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 February 2022
                Categories
                Radiology
                Oncology
                Nuclear Medicine

                sensitivity,specificity,magnetic resonance imaging,glioma,diagnostic accuracy

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