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Article

Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI

by
Laiz Laura de Godoy
1,†,
Kheng Choon Lim
2,†,
Archith Rajan
1,
Gaurav Verma
3,
Mauro Hanaoka
1,
Donald M. O’Rourke
4,5,6,
John Y. K. Lee
4,5,6,
Arati Desai
5,6,
Sanjeev Chawla
1,* and
Suyash Mohan
1
1
Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
2
Department of Neuroradiology, Singapore General Hospital, Singapore 169609, Singapore
3
Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
4
Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
5
Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
6
Glioblastoma Translational Center of Excellence, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19014, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work as the first author.
Cancers 2023, 15(18), 4453; https://doi.org/10.3390/cancers15184453
Submission received: 1 July 2023 / Revised: 22 August 2023 / Accepted: 31 August 2023 / Published: 7 September 2023
(This article belongs to the Special Issue Role of Novel Imaging Technique in Brain Tumors)
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Abstract

:

Simple Summary

IDH mutation is one of the most important prognostic biomarkers in glioma management. Noninvasive neuroimaging techniques to predict IDH mutant glioma may be valuable for guiding clinical decision-making and monitoring response to targeted therapies. The aim of our prospective study was to investigate the clinical potential of proton MR spectroscopy (1H-MRS) with an optimized TE (97 ms) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites. We confirmed that the oncometabolite 2HG was found to be significant in predicting IDH-mutant gliomas, and both single-voxel and multi-voxel 1H-MRS methods are equally efficient in detecting complex resonances of 2HG. Additionally, Glx (glutamate + glutamine) and NAA (N-acetylaspartate) were also found to be important in distinguishing IDH-mutant from wild-type gliomas. In short, 1H-MRS with an optimized TE may be helpful for noninvasively detecting the abnormally high levels of 2HG with high accuracy and comprehending its interaction with other relevant metabolites in infiltrative gliomas.

Abstract

Purpose: The isocitrate dehydrogenase (IDH) mutation has become one of the most important prognostic biomarkers in glioma management, indicating better treatment response and prognosis. IDH mutations confer neomorphic activity leading to the conversion of alpha-ketoglutarate (α-KG) to 2-hydroxyglutarate (2HG). The purpose of this study was to investigate the clinical potential of proton MR spectroscopy (1H-MRS) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites. Materials and Methods: Thirty-two patients with suspected infiltrative glioma underwent a single-voxel (SVS, n = 17) and/or single-slice-multivoxel (1H-MRSI, n = 15) proton MR spectroscopy (1H-MRS) sequence with an optimized echo-time (97 ms) on 3T-MRI. Spectroscopy data were analyzed using the linear combination (LC) model. Cramér–Rao lower bound (CRLB) values of <40% were considered acceptable for detecting 2HG and <20% for other metabolites. Immunohistochemical analyses for determining IDH mutational status were subsequently performed from resected tumor specimens and findings were compared with the results from spectral data. Mann–Whitney and chi-squared tests were performed to ascertain differences in metabolite levels between IDH-mutant and IDH-wild-type gliomas. Receiver operating characteristic (ROC) curve analyses were also performed. Results: Data from eight cases were excluded due to poor spectral quality or non-tumor-related etiology, and final data analyses were performed from 24 cases. Of these cases, 9/12 (75%) were correctly identified as IDH-mutant or IDH-wildtype gliomas through SVS and 10/12 (83%) through 1H-MRSI with an overall concordance rate of 79% (19/24). The sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 77%, 86%, and 70%, respectively. The metabolite 2HG was found to be significant in predicting IDH-mutant gliomas through the chi-squared test (p < 0.01). The IDH-mutant gliomas also had a significantly higher NAA/Cr ratio (1.20 ± 0.09 vs. 0.75 ± 0.12 p = 0.016) and lower Glx/Cr ratio (0.86 ± 0.078 vs. 1.88 ± 0.66; p = 0.029) than those with IDH wild-type gliomas. The areas under the ROC curves for NAA/Cr and Glx/Cr were 0.808 and 0.786, respectively. Conclusions: Noninvasive optimized 1H-MRS may be useful in predicting IDH mutational status and 2HG may serve as a valuable diagnostic and prognostic biomarker in patients with gliomas

1. Introduction

Since the fourth edition of the WHO Classification of Tumors of the Central Nervous System in 2016, isocitrate dehydrogenase (IDH) mutations have become one of the most important prognostic biomarkers in adult-type infiltrative gliomas [1]. IDH mutational status can distinctly separate astrocytomas and oligodendrogliomas from more aggressive and deadly glioblastomas, regardless of histopathological features [2]. It is well recognized that astrocytomas and oligodendrogliomas harboring the IDH mutation demonstrate a better response to chemoradiation therapy, and these patients generally demonstrate better survival outcomes than those with glioblastomas harboring IDH wild-type alleles [3,4], thus emphasizing the importance of non-invasive identification of IDH mutant gliomas.
IDH mutations gain a neomorphic enzyme activity leading to the conversion of alpha-ketoglutarate (α-KG) to 2-hydroxyglutarate (2HG) in the citric acid cycle, resulting in abnormally high levels of 2HG (Figure 1) [5]. The oncometabolite 2HG has been proposed as a surrogate biomarker of IDH mutational status in gliomas. Proton magnetic resonance spectroscopy (1H-MRS) allows non-invasive assessment of the metabolic landscape of biological tissues [6,7,8,9]. Several prior studies have reported the potential utilities of 1H-MRS in diagnosis, planning treatment strategies, and assessing treatment response in gliomas [10,11,12]. However, precise in vivo detection of 2HG on clinical magnetic field strengths (3T) demands the development of optimized 1H-MRS sequences due to the extensive overlap of 2HG resonances with that of neighboring metabolites such as glutamate, glutamine, and γ-aminobutyric acid (GABA) [12]. A spin-echo point-resolved spectroscopy (PRESS) sequence with an optimized echo time (TE) of 97 ms has been proposed to identify a well-defined narrow 2HG peak at 2.25 ppm, resulting in the detection and quantification of 2HG with greater sensitivity [13].
While immunohistochemical analyses and exomic sequencing are considered gold standards for determining IDH mutational status in gliomas [14,15], tissue heterogeneity, partial sampling of tissue specimens, and the presence of variable amounts of antigens constrain the utility of these methods in the reliable detection of IDH mutational status [16]. Eloquent locations of these neoplasms may also limit neurosurgical interventions. Therefore, the non-invasive identification of IDH mutant gliomas using 1H-MRS plays an important role in patient counseling for therapeutic intervention and prognostication and could be utilized as a pharmacodynamic indicator to monitor treatment response [17,18].
We hypothesized that the pathophysiologic changes due to IDH mutations leading to the accumulation of oncometabolite 2HG could also cause variations in the neurochemical profile of other metabolites observed on 1H-MRS. Therefore, the purpose of this study was to investigate the clinical potential of 1H-MRS, single-voxel (SVS), and single-slice-multivoxel magnetic resonance spectroscopic imaging (1H-MRSI) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites.

2. Methods

2.1. Subjects

A waiver for the informed consent form was requested at the time of Institutional Review Board (IRB) submission, and the research project was approved by the IRB/Ethics Committee of the University of Pennsylvania (protocol # 829645). This study was designed and conducted according to the guidelines of the Declaration of Helsinki. A total of 32 patients, comprising of 30 suspected newly diagnosed infiltrative gliomas and two suspected neoplastic progression, were recruited based on routine MRI findings for this study.

2.2. Data Acquisition

All patients underwent anatomical imaging, single-voxel (SVS, n = 17), and/or single-slice-multivoxel magnetic resonance spectroscopic imaging (1H-MRSI, n = 15) MRI on a 3T Tim Trio whole-body MR scanner (Siemens, Erlangen, Germany) equipped with a 12-channel phased-array head coil. The anatomical imaging protocol included axial 3D-T1-weighted magnetization-prepared rapid acquisition of gradient echo (MPRAGE) imaging and an axial T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging using standard parameters. The postcontrast T1-weighted images were acquired with the same parameters as the precontrast acquisition after administration of a standard dose (0.14 mmol/Kg) of gadobenate dimeglumine (MultiHance, Bracco Imaging, Milano, Italy) intravenous contrast agent using a power injector (Medrad, Idianola, PA, USA).
Both SVS and 1H-MRSI were acquired using a standard spin-echo point-resolved spectroscopy (PRESS) sequence. Sequences parameters for SVS included repetition time (TR)/echo time (TE)/number of excitations (NEX) = 2000/97 ms/128, bandwidth = 1200 Hz. Depending upon the tumor location, size, and geometry and to minimize the prospect of obtaining spectral data with poor signal-to-noise ratio and partial volume-averaging effects, the single voxel size varied from 10 × 10 × 10 mm3 (volume: 1 cm3) to 20 × 20 × 20 mm3 (volume: 8 cm3). Sequences parameters for 1H-MRSI included TR/TE/NEX = 2000/97 ms/3, bandwidth = 1200 Hz, field of view = 16 × 16 cm2–20 × 20 cm2, slice thickness = 15–20 mm, and matrix size = 16 × 16. The typical voxel size varied from 10 × 10 × 15 mm3 (volume: 1.5 cm3) to 10 × 12.5 × 20 mm3 (volume: 2.5 cm3) depending upon the dimensions of the neoplasms. For 1H-MRSI, the volume of interest (VOI) was selected to include neoplasms visible as a hyperintense mass on T2-FLAIR images and contralateral normal brain parenchyma, avoiding scalp, skull base, or sinuses. The outer-volume saturation slabs were placed outside VOI to suppress lipid signals from the scalp. The saturation slabs were applied along multiple directions, depending on tumor location, morphology, and orientation (Figure 2). The data set was acquired using elliptical-K-space sampling with weighted phase encoding. Manual shimming was performed to achieve an optimal fullwidth at half-maximum value (<20 Hz) of the magnitude water signal. Both water-suppressed and unsuppressed spectra were acquired, and the unsuppressed water signal was used for computing metabolite concentrations.

2.3. Data Processing

All spectroscopy data were analyzed using a user-independent spectral fit program (linear combination (LC) model, (http://s-provencher.com/lcmodel.shtml) [19]. The region between 0.2 and 4.2 ppm of the spectrum was analyzed, and the following metabolites were evaluated: Lipids + Lactate, 1.3 ppm; N-acetylaspartate (NAA), 2.02 ppm; creatine (Cr), 3.02 ppm; choline (Cho), 3.22 ppm; glutamate+glutamine (Glx), 2.24 to 2.34 ppm; myoinositol (mI), 3.56 ppm; and 2HG, 2.25 ppm. The resonance of Cr at 3.02 ppm was used as an internal chemical shift reference for computing metabolite ratios. The quality of spectral fitting was evaluated by analyzing the difference spectrum (fitted spectrum subtracted from the original spectrum) and by using Cramér–Rao lower bound (CRLB) values, with less than 40% considered acceptable for detecting 2HG [20] and less than 20% for all other metabolites [21,22]. The number of voxels analyzed in the cases of 1H-MRSI varied from 2 to 41, and at least two neighboring voxels with CRLB values <40% for 2HG were required to classify the cases as 2HG-positive. Histopathological and immunohistochemical analyses for glioma-grade and IDH mutational status were subsequently performed from resected tumor specimens (complete or near total resection of the hyperintense expansile mass as visible on T2-FLAIR image), and the findings were compared with the results from spectral data. Metabolic ratios for NAA/Cr, Cho/Cr, Glx/Cr, mI/Cr, and Lipids + Lactate/Cr from the selected voxels were computed from each patient. Additionally, the absolute concentration of 2HG and metabolite ratio of 2HG/Cr were computed from true-positive IDH mutant gliomas.

2.4. Determination of IDH Status Using Immunohistochemistry

Tumor specimens were fixed in formalin and processed for paraffin embedding. Hematoxylin and eosin staining and immunohistochemistry were conducted on 5-micron-thick formalin-fixed, paraffin-embedded tissue sections mounted on Leica Surgipath slides followed by drying for 60 min at 70 °C. Immunohistochemistry with the anti-IDH1-R132H antibody (Monoclonal Mouse Anti-human IDH1 (R132H), Dianova, DIA Clone H09) and DAB chromogen was performed on a Leica Bond III instrument using Bond Polymer Refine Detection System (Leica Microsystems AR9800) following a 20 min heat-induced epitope retrieval with Epitope Retrieval 2, EDTA, pH 9.0 [23].

2.5. Statistical Analysis

Statistical analyses were performed using a statistical package, SPSS for Windows (v. 18.0; Chicago, IL, USA). Kolmogorov–Smirnov tests were used to determine the nature of data distribution. As the data showed a departure from Gaussian distribution, non-parametric Mann–Whitney U tests were performed to assess differences in metabolite levels between IDH-mutant and IDH-wild-type gliomas. The chi-square test was used to assess differences in categorical variables. A probability (p) value of less than 0.05 was considered significant. Sensitivity, specificity, positive predictive value, and negative predictive value were determined. Receiver operating characteristic (ROC) curve analyses were also performed for the metabolites exhibiting significant differences between two groups (IDH-mutant and IDH-wild-type gliomas).

3. Results

Of these 32 patients, eight were excluded as five patients had sub-optimal spectral quality due to inadequate water suppression for the reliable detection and quantification of metabolites, and three other patients were excluded because they had final histological diagnoses of non-tumor etiology, with pathology reports consistent with reactive brain changes without molecular features of neoplasms. As such, a total of 24 patients were included (mean age = 48.7 ± 15 years, 11 males and 13 females) in the final data analysis (Figure 3—Flowchart). All cases had histopathological confirmation for infiltrative gliomas (22 newly diagnosed and 2 neoplastic progression), ranging from histological grade 2 to grade 4. The immunohistochemistry analyses revealed 15 IDH-mutant gliomas and 9 IDH-wild type glioblastomas. Patient demographics, along with histopathological grading and immuno-histochemical findings, are presented in Table 1.
While SVS was preferred for acquiring spectroscopy data from focal well-circumscribed, more ‘superficial’ lesions, 1H-MRSI was chosen for ill-defined, irregular heterogeneous and larger lesions to avoid incomplete sampling of neoplasms. Because of time constraints, we were able to employ both techniques only in a few cases (n = 3).
Using a CRLB <40% for the detection of 2HG, 9/12 (75%) cases were correctly identified as IDH-mutant or IDH-wild-type gliomas by SVS and 10/12 (83%) by 1H-MRSI (Figure 4 and Figure 5, respectively), with an overall concordance rate of 79% (19/24). Of 5 patients who incorrectly classified by 1H-MRS for IDH mutational status, 2 were false positives (1 each on SVS and 1H-MRSI) and 3 were false negatives (2 on SVS, 1 on 1H-MRSI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in identifying IDH-mutant and IDH-wild-type gliomas were 80%, 77%, 86%, and 70%, respectively (Table 2). The mean concentration and standard errors of 2HG and metabolite ratio of 2HG/Cr in IDH mutant cases were 5.24 ± 1.59 mM and 0.55 ± 0.08, respectively. The metabolite 2HG was found to be significant in predicting IDH-mutant gliomas using chi-squared test (p < 0.01).
The IDH-mutant gliomas also harbored a significantly higher mean concentration of NAA/Cr ratio (1.20 ± 0.09 vs. 0.75 ± 0.12; p = 0.016) and significantly lower Glx/Cr ratio (0.86 ± 0.08 vs. 1.88 ± 0.66; p = 0.029) than those with IDH wild-type gliomas. There were no significant differences (p > 0.05) in the mean concentration for the remaining metabolite ratios between IDH-mutant and IDH-wild-type gliomas (Figure 6). The ROC analyses revealed that areas under the ROC curves for NAA/Cr and Glx/Cr were 0.808 and 0.786, respectively, in distinguishing IDH-mutant from IDH wild-type gliomas.

4. Discussion

In this study, we prospectively analyzed the clinical utility of SVS and 1H-MRSI using an optimized TE (97 ms) in assessing IDH-mutational status by detecting the characteristic resonances of 2HG in patients presenting with newly diagnosed infiltrative gliomas and suspected neoplastic progression. Our results showed that 1H-MRS can identify IDH-mutant gliomas with high accuracy (79%), sensitivity (80%), and specificity (77%). We also found that the IDH mutation affects other metabolic pathways leading to variations in metabolite pools of Glx and NAA. These observations provide insights into the pathophysiology of IDH mutations in gliomas. Moreover, the accurate determination of IDH mutational status at the time of initial presentation has important therapeutic implications when a critical decision about the selection of the optimal treatment strategy is to be made.
Mechanistically, wild-type IDH normally catalyzes the reversible NADP+dependent oxidative decarboxylation of isocitrate to α-KG in the citric acid cycle, while IDH mutations confer a neomorphic enzyme activity converting α-KG to 2HG. The high levels of 2HG change the cellular metabolism by leading to DNA hypermethylation and epigenetic modifications of histone, resulting in tumorigenesis [5,24]. The oncometabolite 2HG has been proposed as a putative biomarker for IDH-specific genetic profiles for gliomas. However, not all IDH-mutant gliomas, especially the non-canonical IDH mutant gliomas (about 20–25% of grade-2 and 5–12% of grade-3 gliomas), show the neomorphic activity of 2HG production [25], suggesting that 2HG detection alone may not always be sufficient for identifying the IDH mutation in gliomas. By exploring the reasons for obtaining false-positive cases, we noted that small tumor volume was found to be a potential limiting factor for the reliable detection of 2HG resonances in our study. Similar findings were observed in a previous study in which investigators reported higher detection sensitivity of 2HG from larger voxel size [26].
The optimized TE allowed us to investigate specific biochemical alterations influenced by the pathologic production of 2HG in IDH-mutant gliomas, showing a clear advantage compared to routine short TE (30–35 ms) [18]. We could detect the 2HG peak at 2.25 ppm with high sensitivity and specificity (80% and 77%, respectively). Additionally, in line with our hypotheses and prior studies [27,28,29], the Glx (glutamate + glutamine)/Cr was found to be significantly decreased in IDH-mutant gliomas in the present study. Our results are supported by an earlier study reporting that glutamate levels become depleted by the enzymatic activity of glutamate dehydrogenase in an attempt to replenish α-KG lost in the conversion to 2HG by the IDH enzyme [30]. Metabolomic analyses using glioma cell lines and surgical specimens have also shown that glutaminolysis serves as a key compensatory pathway to maintain metabolic homeostasis in IDH mutant gliomas. As a result, glutamate levels are significantly reduced in IDH mutant gliomas compared to IDH wild-type counterparts [31]. Additionally, we observed significantly increased levels of NAA in the IDH-mutant group compared to the IDH-wild-type group. However, the biological mechanism of NAA and whether it contributes to tumor pathogenesis remains unclear, and this metabolite might be a confounder of tumor grade between low- and high-grade gliomas [32,33,34].
There is no established optimal CRLB cutoff value or concentration level for the detection of 2HG [18]. Using individual patient data and CRLB values, the optimal cutoff for 2HG was found to be lower than 40% in our cohort [20]. Further validation of this optimal threshold for 2HG is crucial for the application of glioma management in clinical practice. A benefit of 2HG MRS compared to other MRI techniques is that it can be employed as direct evidence of increased measurements of 2HG with enhanced detection accuracy [35]. On the other hand, conventional MRI techniques have shown a wide variability of sensitivities (71–100%) and specificities (51–100%) in the identification of IDH mutant gliomas [36,37,38]. Similarly, studies using diffusion-weighted imaging and perfusion-weighted imaging have also reported a wide range of sensitivities (56–100%) and specificities (63–100%) for distinguishing IDH-mutant from IDH-wild-type gliomas [18,39,40,41].
SVS is a broadly available method, generally providing a better signal-to-noise ratio especially from focal well-circumscribed lesions [42]. Moreover, SVS is an easy-to-acquire sequence that allows good magnetic field shimming over the limited volume of interest and takes shorter acquisition time. On the other hand, 1H-MRSI is a valuable technique for obtaining metabolic information from ill-defined, irregular, heterogeneous, large(er) masses including the entire tumor volume to avoid incomplete sampling. Additionally, the 1H-MRSI method allows acquisition of spectroscopy data with better spatial resolution, thus minimizing the partial volume-averaging effects [35]. This in turn allows the metabolic mapping of tumor heterogeneity with a lesser degree of admixing the signals from different tissue compartments. We believe that both methods are equally good when used appropriately according to the specific tumor’s features. However, 1H-MRSI takes a longer time to acquire the data and this sequence is prone to the voxel bleeding effect, resulting in signal contamination from lipids originating from the scalp and bone marrow. Moreover, it is more difficult to shim well over the larger VOI.
Some previous studies have employed sophisticated spectroscopic sequences such as multiple quantum-filtered and spectral editing techniques and post-processing tools for unambiguously detecting spectral resonances of 2HG from IDH mutant gliomas [23,27,43,44]. Earlier, we have also shown the potential of using two-dimensional localized correlation spectroscopy (2D-L-COSY) at 7 Tesla to detect 2HG in IDH-positive gliomas [23]. However, these sophisticated spectroscopic sequences and tools are not readily and widely available in routine clinical settings.
1H-MRS evaluation of the oncometabolite 2HG is of clinical interest in creating a noninvasive detection technique for IDH mutant gliomas for diagnostic purposes; in differentiating glioma from other etiologies (i.e., solitary metastasis, demyelinating lesion) [22]; in surgical decision making, as aggressive resection of both enhancing and nonenhancing disease might improve survival in IDH-mutant, but not in IDH wild-type gliomas [45]; in treatment monitoring (concentration of 2HG increases sharply with tumor progression, whereas it decreases in response to radio- and chemotherapies) [46]; and even in differentiating tumor recurrence from treatment-related changes. In addition, a recent phase I clinical trial used 2HG MRS to document treatment response to a mutant-IDH1 inhibitor drug and revealed a significant decreasing rate of 2HG levels after one week of treatment [47]. Notably, treatment monitoring of novel target therapies using 2HG detection by MRS may be exploited for personalized and precision medicine and early treatment response in clinical trials.
Our findings may aid in further establishing 2HG as a surrogate marker of IDH mutational status in gliomas. Using an optimized TE of 97 ms, both SVS and multi-voxel 1H-MRS methods are equally efficient in detecting complex resonances of 2HG. Other physiologically sensitive metabolites such as Glx and NAA may also serve as potential biomarkers for detecting IDH mutant gliomas. We believe that the diagnostic performance of 1H-MRS in the identification of IDH mutational status in gliomas can be further improved by using an integrated approach of analyzing metabolite levels of 2HG, NAA, and Glx together in future studies.

5. Limitations

Despite promising results, our study had some shortcomings, including a small patient size and the non-availability of IDH2 mutational status. Although we used a relatively high CRLB value of 40% for detecting 2HG, further in vivo and in vitro experiments are required to determine the optimal threshold CRLB value for detecting 2HG metabolite. Our findings warrant further validation in large-scale and multicenter prospective studies for developing a fast, reliable, and reproducible method for identifying IDH mutational status in gliomas.

6. Conclusions

1H-MRS with an optimized TE may be useful for noninvasively detecting the abnormally high levels of 2HG with high accuracy and understanding its interaction with other important metabolites in infiltrative gliomas. Our findings indicate that 2HG and Glx are potential noninvasive surrogate biomarkers for detecting IDH mutations, which has significant clinical implications for prognostication and implementation of appropriate clinical management procedures in glioma patients.

Author Contributions

Conceptualization, S.C.; S.M. and L.L.d.G.; methodology, S.C., G.V.; software, L.L.d.G., K.C.L., S.C., G.V. and A.R.; formal analysis, L.L.d.G., K.C.L., S.C. and A.R.; investigation, S.C., S.M. and L.L.d.G.; data curation, L.L.d.G., K.C.L., M.H., D.M.O., A.D. and J.Y.K.L.; writing—original draft preparation, L.L.d.G., K.C.L., S.C. and S.M.; writing—review and editing, S.C., S.M., M.H., D.M.O., A.D., J.Y.K.L. and G.V.; supervision, S.C. and S.M.; project administration, S.C., S.M. and L.L.d.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of University of Pennsylvania (protocol code # 829645).

Informed Consent Statement

Patient consent was waived at the time of Institutional Review Board (protocol code #829645) submission for analyzing all our previous data on brain tumors.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Louis, D.N.; Perry, A.; Reifenberger, G.; von Deimling, A.; Figarella-Branger, D.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A Summary. Acta Neuropathol. 2016, 131, 803–820. [Google Scholar] [CrossRef] [PubMed]
  2. Louis, D.N.; Perry, A.; Wesseling, P.; Brat, D.J.; Cree, I.A.; Figarella-Branger, D.; Hawkins, C.; Ng, H.K.; Pfister, S.M.; Reifenberger, G.; et al. The 2021 WHO Classification of Tumors of the Central Nervous System: A Summary. Neuro-Oncology 2021, 23, 1231–1251. [Google Scholar] [CrossRef] [PubMed]
  3. Yan, W.; Zhang, W.; You, G.; Bao, Z.; Wang, Y.; Liu, Y.; Kang, C.; You, Y.; Wang, L.; Jiang, T. Correlation of IDH1 Mutation with Clinicopathologic Factors and Prognosis in Primary Glioblastoma: A Report of 118 Patients from China. PLoS ONE 2012, 7, e30339. [Google Scholar] [CrossRef] [PubMed]
  4. Zhang, C.-B.; Bao, Z.-S.; Wang, H.-J.; Yan, W.; Liu, Y.-W.; Li, M.-Y.; Zhang, W.; Chen, L.; Jiang, T. Correlation of IDH1/2 Mutation with Clinicopathologic Factors and Prognosis in Anaplastic Gliomas: A Report of 203 Patients from China. J. Cancer Res. Clin. Oncol. 2014, 140, 45–51. [Google Scholar] [CrossRef]
  5. Dang, L.; White, D.W.; Gross, S.; Bennett, B.D.; Bittinger, M.A.; Driggers, E.M.; Fantin, V.R.; Jang, H.G.; Jin, S.; Keenan, M.C.; et al. Cancer-Associated IDH1 Mutations Produce 2-Hydroxyglutarate. Nature 2009, 462, 739–744. [Google Scholar] [CrossRef] [PubMed]
  6. Bertholdo, D.; Watcharakorn, A.; Castillo, M. Brain Proton Magnetic Resonance Spectroscopy: Introduction and Overview. Neuroimaging Clin. N. Am. 2013, 23, 359–380. [Google Scholar] [CrossRef] [PubMed]
  7. Posse, S.; Otazo, R.; Dager, S.R.; Alger, J. MR Spectroscopic Imaging: Principles and Recent Advances. J. Magn. Reson. Imaging JMRI 2013, 37, 1301–1325. [Google Scholar] [CrossRef]
  8. Salzillo, T.C.; Hu, J.; Nguyen, L.; Whiting, N.; Lee, J.; Weygand, J.; Dutta, P.; Pudakalakatti, S.; Millward, N.Z.; Gammon, S.T.; et al. Interrogating Metabolism in Brain Cancer. Magn. Reson. Imaging Clin. N. Am. 2016, 24, 687–703. [Google Scholar] [CrossRef]
  9. Martín Noguerol, T.; Sánchez-González, J.; Martínez Barbero, J.P.; García-Figueiras, R.; Baleato-González, S.; Luna, A. Clinical Imaging of Tumor Metabolism with 1H Magnetic Resonance Spectroscopy. Magn. Reson. Imaging Clin. N. Am. 2016, 24, 57–86. [Google Scholar] [CrossRef] [PubMed]
  10. Chawla, S.; Krejza, J.; Vossough, A.; Zhang, Y.; Kapoor, G.S.; Wang, S.; O’Rourke, D.M.; Melhem, E.R.; Poptani, H. Differentiation between Oligodendroglioma Genotypes Using Dynamic Susceptibility Contrast Perfusion-Weighted Imaging and Proton MR Spectroscopy. AJNR Am. J. Neuroradiol. 2013, 34, 1542–1549. [Google Scholar] [CrossRef] [PubMed]
  11. Chawla, S.; Oleaga, L.; Wang, S.; Krejza, J.; Wolf, R.L.; Woo, J.H.; O’Rourke, D.M.; Judy, K.D.; Grady, M.S.; Melhem, E.R.; et al. Role of Proton Magnetic Resonance Spectroscopy in Differentiating Oligodendrogliomas from Astrocytomas. J. Neuroimaging Off. J. Am. Soc. Neuroimaging 2010, 20, 3–8. [Google Scholar] [CrossRef] [PubMed]
  12. Kumar, M.; Nanga, R.P.R.; Verma, G.; Wilson, N.; Brisset, J.C.; Nath, K.; Chawla, S. Emerging MR Imaging and Spectroscopic Methods to Study Brain Tumor Metabolism. Front. Neurol. 2022, 13, 789355. [Google Scholar] [CrossRef] [PubMed]
  13. Choi, C.; Ganji, S.; Hulsey, K.; Madan, A.; Kovacs, Z.; Dimitrov, I.; Zhang, S.; Pichumani, K.; Mendelsohn, D.; Mickey, B.; et al. A Comparative Study of Short- and Long-TE 1H MRS at 3 T for in Vivo Detection of 2-Hydroxyglutarate in Brain Tumors. NMR Biomed. 2013, 26, 1242–1250. [Google Scholar] [CrossRef] [PubMed]
  14. Parsons, D.W.; Jones, S.; Zhang, X.; Lin, J.C.-H.; Leary, R.J.; Angenendt, P.; Mankoo, P.; Carter, H.; Siu, I.-M.; Gallia, G.L.; et al. An Integrated Genomic Analysis of Human Glioblastoma Multiforme. Science 2008, 321, 1807–1812. [Google Scholar] [CrossRef] [PubMed]
  15. Yan, H.; Parsons, D.W.; Jin, G.; McLendon, R.; Rasheed, B.A.; Yuan, W.; Kos, I.; Batinic-Haberle, I.; Jones, S.; Riggins, G.J.; et al. IDH1 and IDH2 Mutations in Gliomas. N. Engl. J. Med. 2009, 360, 765–773. [Google Scholar] [CrossRef] [PubMed]
  16. Preusser, M.; Wöhrer, A.; Stary, S.; Höftberger, R.; Streubel, B.; Hainfellner, J.A. Value and Limitations of Immunohistochemistry and Gene Sequencing for Detection of the IDH1-R132H Mutation in Diffuse Glioma Biopsy Specimens. J. Neuropathol. Exp. Neurol. 2011, 70, 715–723. [Google Scholar] [CrossRef]
  17. Choi, C.; Raisanen, J.M.; Ganji, S.K.; Zhang, S.; McNeil, S.S.; An, Z.; Madan, A.; Hatanpaa, K.J.; Vemireddy, V.; Sheppard, C.A.; et al. Prospective Longitudinal Analysis of 2-Hydroxyglutarate Magnetic Resonance Spectroscopy Identifies Broad Clinical Utility for the Management of Patients With IDH-Mutant Glioma. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2016, 34, 4030–4039. [Google Scholar] [CrossRef]
  18. Suh, C.H.; Kim, H.S.; Jung, S.C.; Choi, C.G.; Kim, S.J. 2-Hydroxyglutarate MR Spectroscopy for Prediction of Isocitrate Dehydrogenase Mutant Glioma: A Systemic Review and Meta-Analysis Using Individual Patient Data. Neuro-Oncology 2018, 20, 1573–1583. [Google Scholar] [CrossRef] [PubMed]
  19. Provencher, S.W. Estimation of Metabolite Concentrations from Localized in Vivo Proton NMR Spectra. Magn. Reson. Med. 1993, 30, 672–679. [Google Scholar] [CrossRef] [PubMed]
  20. Batsios, G.; Viswanath, P.; Subramani, E.; Najac, C.; Gillespie, A.M.; Santos, R.D.; Molloy, A.R.; Pieper, R.O.; Ronen, S.M. PI3K/MTOR Inhibition of IDH1 Mutant Glioma Leads to Reduced 2HG Production That Is Associated with Increased Survival. Sci. Rep. 2019, 9, 10521. [Google Scholar] [CrossRef]
  21. Kim, H.; Kim, S.; Lee, H.H.; Heo, H. In-Vivo Proton Magnetic Resonance Spectroscopy of 2-Hydroxyglutarate in Isocitrate Dehydrogenase-Mutated Gliomas: A Technical Review for Neuroradiologists. Korean J. Radiol. 2016, 17, 620–632. [Google Scholar] [CrossRef] [PubMed]
  22. Tietze, A.; Choi, C.; Mickey, B.; Maher, E.A.; Parm Ulhøi, B.; Sangill, R.; Lassen-Ramshad, Y.; Lukacova, S.; Østergaard, L.; von Oettingen, G. Noninvasive Assessment of Isocitrate Dehydrogenase Mutation Status in Cerebral Gliomas by Magnetic Resonance Spectroscopy in a Clinical Setting. J. Neurosurg. 2018, 128, 391–398. [Google Scholar] [CrossRef]
  23. Verma, G.; Mohan, S.; Nasrallah, M.P.; Brem, S.; Lee, J.Y.K.; Chawla, S.; Wang, S.; Nagarajan, R.; Thomas, M.A.; Poptani, H. Non-Invasive Detection of 2-Hydroxyglutarate in IDH-Mutated Gliomas Using Two-Dimensional Localized Correlation Spectroscopy (2D L-COSY) at 7 Tesla. J. Transl. Med. 2016, 14, 274. [Google Scholar] [CrossRef] [PubMed]
  24. Gonçalves, F.G.; Alves, C.A.P.F.; Vossough, A. Updates in Pediatric Malignant Gliomas. Top. Magn. Reson. Imaging TMRI 2020, 29, 83–94. [Google Scholar] [CrossRef] [PubMed]
  25. Franceschi, E.; De Biase, D.; Di Nunno, V.; Pession, A.; Tosoni, A.; Gatto, L.; Tallini, G.; Visani, M.; Lodi, R.; Bartolini, S.; et al. IDH1 Non-Canonical Mutations and Survival in Patients with Glioma. Diagnostics 2021, 11, 342. [Google Scholar] [CrossRef]
  26. Izquierdo-Garcia, J.L.; Viswanath, P.; Eriksson, P.; Chaumeil, M.M.; Pieper, R.O.; Phillips, J.J.; Ronen, S.M. Metabolic Reprogramming in Mutant IDH1 Glioma Cells. PLoS ONE 2015, 10, e0118781. [Google Scholar] [CrossRef]
  27. Bisdas, S.; Chadzynski, G.L.; Braun, C.; Schittenhelm, J.; Skardelly, M.; Hagberg, G.E.; Ethofer, T.; Pohmann, R.; Shajan, G.; Engelmann, J.; et al. MR Spectroscopy for in Vivo Assessment of the Oncometabolite 2-Hydroxyglutarate and Its Effects on Cellular Metabolism in Human Brain Gliomas at 9.4T. J. Magn. Reson. Imaging JMRI 2016, 44, 823–833. [Google Scholar] [CrossRef] [PubMed]
  28. Natsumeda, M.; Motohashi, K.; Igarashi, H.; Nozawa, T.; Abe, H.; Tsukamoto, Y.; Ogura, R.; Okada, M.; Kobayashi, T.; Aoki, H.; et al. Reliable Diagnosis of IDH-Mutant Glioblastoma by 2-Hydroxyglutarate Detection: A Study by 3-T Magnetic Resonance Spectroscopy. Neurosurg. Rev. 2018, 41, 641–647. [Google Scholar] [CrossRef]
  29. Nagashima, H.; Tanaka, K.; Sasayama, T.; Irino, Y.; Sato, N.; Takeuchi, Y.; Kyotani, K.; Mukasa, A.; Mizukawa, K.; Sakata, J.; et al. Diagnostic Value of Glutamate with 2-Hydroxyglutarate in Magnetic Resonance Spectroscopy for IDH1 Mutant Glioma. Neuro-Oncology 2016, 18, 1559–1568. [Google Scholar] [CrossRef] [PubMed]
  30. Ohka, F.; Ito, M.; Ranjit, M.; Senga, T.; Motomura, A.; Motomura, K.; Saito, K.; Kato, K.; Kato, Y.; Wakabayashi, T.; et al. Quantitative Metabolome Analysis Profiles Activation of Glutaminolysis in Glioma with IDH1 Mutation. Tumour Biol. J. Int. Soc. Oncodev. Biol. Med. 2014, 35, 5911–5920. [Google Scholar] [CrossRef]
  31. Dekker, L.J.M.; Verheul, C.; Wensveen, N.; Leenders, W.; Lamfers, M.L.M.; Leenstra, S.; Luider, T.M. Effects of the IDH1 R132H Mutation on the Energy Metabolism: A Comparison between Tissue and Corresponding Primary Glioma Cell Cultures. ACS Omega 2022, 7, 3568–3578. [Google Scholar] [CrossRef]
  32. Reitman, Z.J.; Jin, G.; Karoly, E.D.; Spasojevic, I.; Yang, J.; Kinzler, K.W.; He, Y.; Bigner, D.D.; Vogelstein, B.; Yan, H. Profiling the Effects of Isocitrate Dehydrogenase 1 and 2 Mutations on the Cellular Metabolome. Proc. Natl. Acad. Sci. USA 2011, 108, 3270–3275. [Google Scholar] [CrossRef] [PubMed]
  33. Branzoli, F.; Marjańska, M. Magnetic Resonance Spectroscopy of Isocitrate Dehydrogenase Mutated Gliomas: Current Knowledge on the Neurochemical Profile. Curr. Opin. Neurol. 2020, 33, 413–421. [Google Scholar] [CrossRef] [PubMed]
  34. Wang, Q.; Zhang, H.; Zhang, J.; Wu, C.; Zhu, W.; Li, F.; Chen, X.; Xu, B. The Diagnostic Performance of Magnetic Resonance Spectroscopy in Differentiating High-from Low-Grade Gliomas: A Systematic Review and Meta-Analysis. Eur. Radiol. 2016, 26, 2670–2684. [Google Scholar] [CrossRef] [PubMed]
  35. Choi, C.; Ganji, S.K.; DeBerardinis, R.J.; Hatanpaa, K.J.; Rakheja, D.; Kovacs, Z.; Yang, X.-L.; Mashimo, T.; Raisanen, J.M.; Marin-Valencia, I.; et al. 2-Hydroxyglutarate Detection by Magnetic Resonance Spectroscopy in IDH-Mutated Patients with Gliomas. Nat. Med. 2012, 18, 624–629. [Google Scholar] [CrossRef] [PubMed]
  36. Carrillo, J.A.; Lai, A.; Nghiemphu, P.L.; Kim, H.J.; Phillips, H.S.; Kharbanda, S.; Moftakhar, P.; Lalaezari, S.; Yong, W.; Ellingson, B.M.; et al. Relationship between Tumor Enhancement, Edema, IDH1 Mutational Status, MGMT Promoter Methylation, and Survival in Glioblastoma. AJNR Am. J. Neuroradiol. 2012, 33, 1349–1355. [Google Scholar] [CrossRef]
  37. Lasocki, A.; Tsui, A.; Gaillard, F.; Tacey, M.; Drummond, K.; Stuckey, S. Reliability of Noncontrast-Enhancing Tumor as a Biomarker of IDH1 Mutation Status in Glioblastoma. J. Clin. Neurosci. Off. J. Neurosurg. Soc. Australas. 2017, 39, 170–175. [Google Scholar] [CrossRef]
  38. Zhou, H.; Vallières, M.; Bai, H.X.; Su, C.; Tang, H.; Oldridge, D.; Zhang, Z.; Xiao, B.; Liao, W.; Tao, Y.; et al. MRI Features Predict Survival and Molecular Markers in Diffuse Lower-Grade Gliomas. Neuro-Oncology 2017, 19, 862–870. [Google Scholar] [CrossRef]
  39. Leu, K.; Ott, G.A.; Lai, A.; Nghiemphu, P.L.; Pope, W.B.; Yong, W.H.; Liau, L.M.; Cloughesy, T.F.; Ellingson, B.M. Perfusion and Diffusion MRI Signatures in Histologic and Genetic Subtypes of WHO Grade II-III Diffuse Gliomas. J. Neurooncol. 2017, 134, 177–188. [Google Scholar] [CrossRef] [PubMed]
  40. Yamashita, K.; Hiwatashi, A.; Togao, O.; Kikuchi, K.; Hatae, R.; Yoshimoto, K.; Mizoguchi, M.; Suzuki, S.O.; Yoshiura, T.; Honda, H. MR Imaging-Based Analysis of Glioblastoma Multiforme: Estimation of IDH1 Mutation Status. AJNR Am. J. Neuroradiol. 2016, 37, 58–65. [Google Scholar] [CrossRef] [PubMed]
  41. Xing, Z.; Yang, X.; She, D.; Lin, Y.; Zhang, Y.; Cao, D. Noninvasive Assessment of IDH Mutational Status in World Health Organization Grade II and III Astrocytomas Using DWI and DSC-PWI Combined with Conventional MR Imaging. AJNR Am. J. Neuroradiol. 2017, 38, 1138–1144. [Google Scholar] [CrossRef]
  42. Zhang, Y.; Taub, E.; Salibi, N.; Uswatte, G.; Maudsley, A.A.; Sheriff, S.; Womble, B.; Mark, V.W.; Knight, D.C. Comparison of Reproducibility of Single Voxel Spectroscopy and Whole-Brain Magnetic Resonance Spectroscopy Imaging at 3T. NMR Biomed. 2018, 31, e3898. [Google Scholar] [CrossRef] [PubMed]
  43. Andronesi, O.C.; Kim, G.S.; Gerstner, E.; Batchelor, T.; Tzika, A.A.; Fantin, V.R.; Vander Heiden, M.G.; Sorensen, A.G. Detection of 2-Hydroxyglutarate in IDH-Mutated Glioma Patients by in Vivo Spectral-Editing and 2D Correlation Magnetic Resonance Spectroscopy. Sci. Transl. Med. 2012, 4, 116ra4. [Google Scholar] [CrossRef] [PubMed]
  44. Emir, U.E.; Larkin, S.J.; de Pennington, N.; Voets, N.; Plaha, P.; Stacey, R.; Al-Qahtani, K.; Mccullagh, J.; Schofield, C.J.; Clare, S.; et al. Noninvasive Quantification of 2-Hydroxyglutarate in Human Gliomas with IDH1 and IDH2 Mutations. Cancer Res. 2016, 76, 43–49. [Google Scholar] [CrossRef] [PubMed]
  45. Beiko, J.; Suki, D.; Hess, K.R.; Fox, B.D.; Cheung, V.; Cabral, M.; Shonka, N.; Gilbert, M.R.; Sawaya, R.; Prabhu, S.S.; et al. IDH1 Mutant Malignant Astrocytomas Are More Amenable to Surgical Resection and Have a Survival Benefit Associated with Maximal Surgical Resection. Neuro-Oncology 2014, 16, 81–91. [Google Scholar] [CrossRef] [PubMed]
  46. de la Fuente, M.I.; Young, R.J.; Rubel, J.; Rosenblum, M.; Tisnado, J.; Briggs, S.; Arevalo-Perez, J.; Cross, J.R.; Campos, C.; Straley, K.; et al. Integration of 2-Hydroxyglutarate-Proton Magnetic Resonance Spectroscopy into Clinical Practice for Disease Monitoring in Isocitrate Dehydrogenase-Mutant Glioma. Neuro-Oncology 2016, 18, 283–290. [Google Scholar] [CrossRef]
  47. Andronesi, O.C.; Arrillaga-Romany, I.C.; Ly, K.I.; Bogner, W.; Ratai, E.M.; Reitz, K.; Iafrate, A.J.; Dietrich, J.; Gerstner, E.R.; Chi, A.S.; et al. Pharmacodynamics of Mutant-IDH1 Inhibitors in Glioma Patients Probed by in Vivo 3D MRS Imaging of 2-Hydroxyglutarate. Nat. Commun. 2018, 9, 1474. [Google Scholar] [CrossRef]
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Figure 1. Relationship of IDH mutations to levels of 2HG (an oncometabolite) and DNA methylation.
Figure 1. Relationship of IDH mutations to levels of 2HG (an oncometabolite) and DNA methylation.
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Figure 2. A patient with anaplastic astrocytoma. (A) T2-FLAIR shows a left frontal lobe mass. (B) Four outer-volume saturation slabs are placed outside the single voxel to suppress contamination from lipid signals arising from the scalp and bone marrow. (C) Spectra demonstrate elevated Cho/Cr (0.63; CRLB = 2%) and metabolic levels of 2HG (2HG/Cr = 0.4; CRLB = 33%; black arrow). (D) 1H-MRSI grid overlaid on T2-FLAIR image showing different voxels from tumor. (E) Spectra from a red voxel within the mass demonstrates elevated Cho/Cr (0.84; CRLB = 3%) and metabolic levels of 2HG (2HG/Cr = 0.49; CRLB = 36%, black arrow). Histopathological and immunohistochemical analyses were consistent with grade-3 astrocytoma with positive IDH1 mutational status. Red lines: fitting spectra. Black lines: real spectra.
Figure 2. A patient with anaplastic astrocytoma. (A) T2-FLAIR shows a left frontal lobe mass. (B) Four outer-volume saturation slabs are placed outside the single voxel to suppress contamination from lipid signals arising from the scalp and bone marrow. (C) Spectra demonstrate elevated Cho/Cr (0.63; CRLB = 2%) and metabolic levels of 2HG (2HG/Cr = 0.4; CRLB = 33%; black arrow). (D) 1H-MRSI grid overlaid on T2-FLAIR image showing different voxels from tumor. (E) Spectra from a red voxel within the mass demonstrates elevated Cho/Cr (0.84; CRLB = 3%) and metabolic levels of 2HG (2HG/Cr = 0.49; CRLB = 36%, black arrow). Histopathological and immunohistochemical analyses were consistent with grade-3 astrocytoma with positive IDH1 mutational status. Red lines: fitting spectra. Black lines: real spectra.
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Figure 3. The flow-chart documenting inclusion and exclusion criteria along with number of patients who underwent SVS or 1H-MRSI and IDH mutational status of gliomas.
Figure 3. The flow-chart documenting inclusion and exclusion criteria along with number of patients who underwent SVS or 1H-MRSI and IDH mutational status of gliomas.
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Figure 4. A glioma patient, status post resection. (A) T2-FLAIR and (B) T2-W images demonstrate expansile signal abnormality lateral to resection cavity in the right frontal lobe. (C) Post-contrast T1 image shows no abnormal enhancement. (D) SVS demonstrates elevated Cho/Cr (0.37; CRLB = 6%) and metabolic levels of 2HG (2HG/Cr = 0.4; CRLB = 33%; black arrow). Histopathological and immunohistochemical analyses were consistent with grade-3 oligodendroglioma with positive IDH1 mutational status. Red lines: fitting spectra. Black lines: real spectra.
Figure 4. A glioma patient, status post resection. (A) T2-FLAIR and (B) T2-W images demonstrate expansile signal abnormality lateral to resection cavity in the right frontal lobe. (C) Post-contrast T1 image shows no abnormal enhancement. (D) SVS demonstrates elevated Cho/Cr (0.37; CRLB = 6%) and metabolic levels of 2HG (2HG/Cr = 0.4; CRLB = 33%; black arrow). Histopathological and immunohistochemical analyses were consistent with grade-3 oligodendroglioma with positive IDH1 mutational status. Red lines: fitting spectra. Black lines: real spectra.
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Figure 5. A patient with anaplastic astrocytoma. (A) T2-FLAIR and (B) T2-weighted image show a right temporal lobe mass. (C) Post-contrast T1 image shows faint enhancing foci within the mass. (D) 1H-MRSI grid overlaid on T2-FLAIR image showing different colored voxels (yellowish and reddish: CRLB ≤ 40%; greenish and blueish: CRLB ≥ 40% for detecting 2HG). (E) Spectra from red voxel encompassing the tumor (white arrow) demonstrates elevated Cho/Cr (0.79; CRLB = 3%) and metabolic levels of 2HG (2HG/Cr =1.05; CRLB = 16%, black arrow). Histopathological and immunohistochemical analyses were consistent with grade-3 astrocytoma with positive IDH1 mutational status. Red lines: fitting spectra. Black lines: real spectra.
Figure 5. A patient with anaplastic astrocytoma. (A) T2-FLAIR and (B) T2-weighted image show a right temporal lobe mass. (C) Post-contrast T1 image shows faint enhancing foci within the mass. (D) 1H-MRSI grid overlaid on T2-FLAIR image showing different colored voxels (yellowish and reddish: CRLB ≤ 40%; greenish and blueish: CRLB ≥ 40% for detecting 2HG). (E) Spectra from red voxel encompassing the tumor (white arrow) demonstrates elevated Cho/Cr (0.79; CRLB = 3%) and metabolic levels of 2HG (2HG/Cr =1.05; CRLB = 16%, black arrow). Histopathological and immunohistochemical analyses were consistent with grade-3 astrocytoma with positive IDH1 mutational status. Red lines: fitting spectra. Black lines: real spectra.
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Figure 6. Bar graphs represent the metabolite concentration ratio as mean ± SEM (standard error of the mean). Non-parametric Mann–Whitney U tests were performed to ascertain the differences in metabolite ratios between two groups. The symbol (*) indicates significant difference (p < 0.05). The IDH-mutant gliomas had a significantly higher mean concentration of NAA/Cr ratio (1.20 ± 0.09 versus 0.75 ± 0.12; p = 0.016) compared with IDH-wild-type patients and significantly lower Glx/Cr ratio (0.86 ± 0.08 vs. 1.88 ± 0.66; p = 0.029) than IDH-wild-type gliomas.
Figure 6. Bar graphs represent the metabolite concentration ratio as mean ± SEM (standard error of the mean). Non-parametric Mann–Whitney U tests were performed to ascertain the differences in metabolite ratios between two groups. The symbol (*) indicates significant difference (p < 0.05). The IDH-mutant gliomas had a significantly higher mean concentration of NAA/Cr ratio (1.20 ± 0.09 versus 0.75 ± 0.12; p = 0.016) compared with IDH-wild-type patients and significantly lower Glx/Cr ratio (0.86 ± 0.08 vs. 1.88 ± 0.66; p = 0.029) than IDH-wild-type gliomas.
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Table 1. Patient demographics, histological/immuno-histochemical findings, and 1H-MRS results.
Table 1. Patient demographics, histological/immuno-histochemical findings, and 1H-MRS results.
PatientGenderAge
(Years)		1H-MRS Modality1H-MRS 2HG ResultsIDH1 StatusHistopathology ResultsStatus at Time of 1H-MRS
1F54SVSNegativeIDH1-R132H negativeGlioblastoma, WHO grade 4New
2M63SVSPositiveIDH1-R132H mutantOligodendroglioma, WHO grade 2New
3F32SVSPositiveIDH1-mutantMixed Oligoastrocytoma, WHO grade 3Recurrent
4F34SVSPositiveIDH1-R132H mutantAstrocytoma, WHO grade 3New
5M38SVSNegativeIDH1-R132H negativeGlioblastoma (RTK1 subclass), WHO grade 4New
6F53SVSNegativeIDH1-R132H negativeGlioblastoma, WHO grade 4New
7F72SVSNegativeIDH1-R132H negativeMolecular Glioblastoma, WHO grade 4New
8M24SVSPositiveIDH1-R132H mutantAnaplastic Astrocytoma, WHO grade 3New
9F36SVSPositiveIDH1-R132H mutantAstrocytoma, WHO grade 3New
10F48SVSPositiveIDH1-R132H mutantAstrocytoma, WHO grade 3New
11F46SVSPositiveIDH1-R132H mutantOligodendroglioma, WHO grade 2New
12M64SVSNegativeIDH1-R132H negativeGlioblastoma, WHO grade 4New
13F281H-MRSIPositiveIDH1-R132H mutantAstrocytoma, WHO grade 3New
14M361H-MRSIPositiveIDH1-R132H mutantAnaplastic Astrocytoma, WHO grade 3New
15M691H-MRSINegativeIDH1-R132H negativeAnaplastic Astrocytoma, WHO grade 3New
16F401H-MRSIPositiveIDH1-R132H mutantAnaplastic Astrocytoma, WHO grade 3New
17F361H-MRSIPositiveIDH1-R132H mutantAnaplastic Oligoastrocytoma, WHO Grade 3New
18M391H-MRSINegativeIDH1-R132H negativeGlioblastoma, WHO grade 4New
19M531H-MRSINegativeIDH1-R132H negativeGlioblastoma with sarcomatous features, WHO Grade 4New
20M351H-MRSIPositiveIDH1-mutantOligodendroglioma, WHO grade 2New
21M301H-MRSIPositiveIDH1-mutantAnaplastic astrocytoma, WHO Grade 3New
22M381H-MRSIPositiveIDH1-R132H mutantDiffuse Astrocytoma, WHO Grade 2New
23F511H-MRSIPositiveIDH1-mutantRecurrent Astrocytoma, progression to WHO grade 4Recurrent
24F801H-MRSINegativeIDH1-R132H negativeInfiltrating astrocytoma, WHO grade 2New
1H-MRS = proton MR spectroscopy; 1H-MRSI = proton magnetic resonance spectroscopic imaging; 2HG = 2-hydroxyglutarate, IDH = Isocitrate dehydrogenase; New= newly diagnosed gliomas; SVS = single-voxel spectroscopy.
Table 2. Diagnostic performance of 2HG for prediction of IDH-mutant glioma.
Table 2. Diagnostic performance of 2HG for prediction of IDH-mutant glioma.
2HG Status
NegativePositive
IDH StatusWild-Type72
Mutant312
Accuracy (%)79
Sensitivity (%)80
Specificity (%)77
Positive Predictive Value (%)86
Negative Predictive Value (%)70
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de Godoy, L.L.; Lim, K.C.; Rajan, A.; Verma, G.; Hanaoka, M.; O’Rourke, D.M.; Lee, J.Y.K.; Desai, A.; Chawla, S.; Mohan, S. Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI. Cancers 2023, 15, 4453. https://doi.org/10.3390/cancers15184453

AMA Style

de Godoy LL, Lim KC, Rajan A, Verma G, Hanaoka M, O’Rourke DM, Lee JYK, Desai A, Chawla S, Mohan S. Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI. Cancers. 2023; 15(18):4453. https://doi.org/10.3390/cancers15184453

Chicago/Turabian Style

de Godoy, Laiz Laura, Kheng Choon Lim, Archith Rajan, Gaurav Verma, Mauro Hanaoka, Donald M. O’Rourke, John Y. K. Lee, Arati Desai, Sanjeev Chawla, and Suyash Mohan. 2023. "Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI" Cancers 15, no. 18: 4453. https://doi.org/10.3390/cancers15184453

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