Skip to main content

Main menu

  • Home
  • Current Issue
  • Archive
  • Info for
    • Authors
    • Editorial Policies
    • Subscribers
    • Advertisers
    • Editorial Board
    • Special Issues 2025
  • Journal Metrics
  • Other Publications
    • In Vivo
    • Cancer Genomics & Proteomics
    • Cancer Diagnosis & Prognosis
  • More
    • IIAR
    • Conferences
    • 2008 Nobel Laureates
  • About Us
    • General Policy
    • Contact
  • Other Publications
    • Anticancer Research
    • In Vivo
    • Cancer Genomics & Proteomics

User menu

  • Register
  • Subscribe
  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
Anticancer Research
  • Other Publications
    • Anticancer Research
    • In Vivo
    • Cancer Genomics & Proteomics
  • Register
  • Subscribe
  • My alerts
  • Log in
  • My Cart
Anticancer Research

Advanced Search

  • Home
  • Current Issue
  • Archive
  • Info for
    • Authors
    • Editorial Policies
    • Subscribers
    • Advertisers
    • Editorial Board
    • Special Issues 2025
  • Journal Metrics
  • Other Publications
    • In Vivo
    • Cancer Genomics & Proteomics
    • Cancer Diagnosis & Prognosis
  • More
    • IIAR
    • Conferences
    • 2008 Nobel Laureates
  • About Us
    • General Policy
    • Contact
  • Visit us on Facebook
  • Follow us on Linkedin
Research ArticleClinical Studies

Early Metabolic Changes in 1H-MRSI Predictive for Survival in Patients With Newly Diagnosed High-grade Glioma

MICHAEL H. WANG, WILSON ROA, KEITH WACHOWICZ, ATIYAH YAHYA, ALBERT MURTHA, JOHN AMANIE, JONATHAN CHAINEY, HARVEY QUON, SUNITA GHOSH and SAMIR PATEL
Anticancer Research May 2022, 42 (5) 2665-2673; DOI: https://doi.org/10.21873/anticanres.15744
MICHAEL H. WANG
1Division of Radiation Oncology, University of Alberta, Edmonton, Canada;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
WILSON ROA
1Division of Radiation Oncology, University of Alberta, Edmonton, Canada;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
KEITH WACHOWICZ
2Division of Medical Physics, University of Alberta, Edmonton, Canada;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
ATIYAH YAHYA
2Division of Medical Physics, University of Alberta, Edmonton, Canada;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
ALBERT MURTHA
1Division of Radiation Oncology, University of Alberta, Edmonton, Canada;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
JOHN AMANIE
1Division of Radiation Oncology, University of Alberta, Edmonton, Canada;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
JONATHAN CHAINEY
3Division of Neurosurgery, University of Alberta, Edmonton, Canada;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
HARVEY QUON
4Department of Radiation Oncology, Tom Baker Cancer Centre, Calgary, Canada;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
SUNITA GHOSH
5Division of Medical Oncology, University of Alberta, Edmonton, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
SAMIR PATEL
1Division of Radiation Oncology, University of Alberta, Edmonton, Canada;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: samir.patel2@ahs.ca
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Background: The purpose of this study was to evaluate the association of specific threshold values for changes in metabolic metrics measured from 1H magnetic resonance spectroscopic imaging (MRSI) to survival of patients with high-grade glioma treated with multimodality therapy. Patients and Methods: Forty-four patients with newly diagnosed high-grade glioma were prospectively enrolled. Serial MRI and MRSI scans provided measures of tumor choline, creatine, and N-acetylaspartate (NAA). Cox regression analyses adjusted for patient age, KPS, and delivery of concurrent chemotherapy were used to assess the association of changes in metabolic metrics with survival. Results: Median follow-up time for patients at risk was 13.4 years. Overall survival (OS) was longer in patients with ≤20% increase (vs. >20%) in normalized choline (p=0.024) or choline/NAA (p=0.024) from baseline to week 4 of RT. During this period, progression-free survival (PFS) was longer in patients with ≤40% increase (vs. >40%) in normalized choline (p=0.013). Changes in normalized creatine, choline/creatine, and NAA/creatine from baseline to mid-RT were not associated with OS. From baseline to post-RT, changes in metabolic metrics were not associated with OS or PFS. Conclusion: Threshold values for serial changes in choline metrics on mid-RT MRSI associated with OS and PFS were identified. Metabolic metrics at post-RT did not predict for these survival endpoints. These findings suggest a potential clinical role for MRSI to provide an early assessment of treatment response and could enable risk-adapted therapy in clinical trial development and clinical practice.

Key Words:
  • Prospective study
  • predictive biomarker
  • high-grade glioma
  • glioblastoma
  • magnetic resonance spectroscopic imaging

The current standard tool to assess treatment response of high-grade glioma is MRI (1). Radiographic response assessment using MRI is based on measurement of the maximum diameters of contrast-enhancing tumor in two dimensions and qualitative evaluation of abnormal signal on T2/fluid-attenuated inversion recovery regions (1, 2). However, poor definition of tumor boundaries due to brain infiltration and heterogeneous contrast enhancement increases difficulty in measuring these dimensions consistently (3). Substantial inter-observer variability, confounding effects of therapy such as anti-angiogenic agents, and difficulty differentiating tumor progression from pseudo-progression can delay response assessment based on MRI by months (3, 4). Early response assessment could enable earlier decision-making to individualize therapy, which is of particular importance considering the poor prognosis of high-grade glioma (5, 6).

In vivo 1H magnetic resonance spectroscopic imaging (MRSI) has been used to evaluate cellular metabolism in an effort to predict tumor recurrence before morphologic changes become apparent on MRI (7, 8). It has been previously demonstrated that high-grade gliomas have abnormally elevated choline and reduced N-acetylaspartate (NAA) and creatine compared with normal brain (9-11). Metabolic changes within the first three months after completion of therapy, the standard time point for response evaluation in the clinical setting, correlate with survival of patients with high-grade gliomas (12-16). Few studies, however, have assessed the utility of early MRSI biomarkers during the mid-course of radiation therapy (RT) (14-17). Metrics describing the levels of choline including the choline/NAA ratio, choline-NAA index, and excess choline relative to NAA during and after RT may be useful for predicting survival for patients with high-grade glioma (15,17). However, there is no consensus on the threshold values defining patient groups with differences in clinical outcomes from these metrics (18).

In this prospective study, we investigated whether specific threshold values for change in metabolic metrics using 1H MRSI were associated with treatment response and survival of patients with newly diagnosed high-grade glioma treated with multimodality therapy. Identification of specific threshold values for metabolic changes that predict for survival may be useful in clinical practice and clinical trials to stratify patients and enable individualized, response-based therapy to improve outcomes for patients expected to respond poorly.

Patients and Methods

Patient enrollment. This prospective study enrolled patients with histologically-confirmed newly-diagnosed high-grade glioma at a single institution. Eligibility criteria included age of 18 years or older and Karnofsky Performance Status (KPS) of 60 or higher, allowing delivery of post-operative RT. Patients with contraindications to MRI scanning, previous head or neck region RT, scleroderma, and systemic lupus erythematosus were excluded. Radiation therapy target volumes were defined using co-registered CT simulation and pre- and post-gadolinium T1- and T2-weighted MRI sequences with 1.0 mm slices. Treatment was delivered with three-dimensional conformal RT to a dose of 60 Gy in 30 fractions over 6 weeks (6). MRSI was performed prior to initiation of RT (at baseline), the fourth week of RT (mid-RT), and two months after completion of RT (post-RT). The study received institutional review board approval, and informed consent was obtained from all patients prior to enrollment.

MR data acquisition. Single-slice, multi-voxel post-gadolinium scans were acquired in the same session as conventional MRI with 1-mm thick slices. Studies were performed on a 1.5 Tesla magnet (Philips, Andover, MA) from May 2004 until November 2005. A single point-resolved spectroscopic (PRESS) sequence was obtained with echo time (TE) of 272 ms and repetition time (TR) of 2200 ms for volume localization as previously described (19). A 10×10×1 matrix of 1.0×1.1×2.0 cm voxels (individual volume, 2.2 cc) was used for a total PRESS-box of 10.0×11.0×2.0 cm. The PRESS-box included the contrast-enhancing lesion and contralateral normal brain and excluded bone and subcutaneous fat which may complicate shimming and water suppression. Two voxels were manually selected from the matrix for analysis: 1) a tumor voxel at the edge of the contrast-enhancing lesion maximally covering the visible tumor while limiting inclusion of necrotic-appearing areas, and 2) a control voxel in an anatomically corresponding region of the contralateral normal brain outside of the RT beams (Figure 1). Voxels were manually selected based on anatomic reference to T1-weigted images co-registered to MRSI data and overlaid as much as possible in the same location on serial MRSI studies in the same patient. Patients enrolled after November 2005 were scanned on a 3 Tesla magnet (Philips, Andover, MA, USA) using a TE of 100 ms and TR of 3,800 ms. For 3 Tesla scans, a matrix of smaller 0.75×0.75×1.0 cm voxels (individual volume, 0.56 cc) was used for better resolution cm and sets of three voxels were placed each in tumor and contralateral normal brain to reduce the impact of tissue heterogeneity from partial volume effect of tumor and normal tissue within the voxels. Total acquisition time was less than 30 min for all scans.

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Magnetic resonance spectroscopic imaging from a sample patient; a 63 year-old male with Karnofsky Performance Status of 80 who was diagnosed with glioblastoma and treated with concurrent chemoradiation (progression-free survival=20.1 months, overall survival=24.5 months), scanned on a 3 Tesla magnet. (A) Single point-resolved spectroscopic sequence for volume localization (PRESS-box; blue squares) within region of interest (green box) with voxel selection for contralateral normal brain and tumor (left and right yellow squares, respectively). (B) Region of interest for choline measurement and (C) N-acetylaspartate (NAA) measurement. (D) Choline, creatine and NAA spectra for contralateral normal brain and (E) for tumor.

Studies were performed on the 1.5 T scanner for 25 patients and on the 3 T scanner for 14 patients. In total, 95 scans were performed: 33 subjects received a scan at baseline, 33 at mid-RT, and 29 at post-RT. Scans with limited quality due to poor quality of spectra from lipid contamination, or poor coverage of the target lesion were excluded from the analysis (n=4). In total, metabolites were analyzed from 820 voxels from 91 evaluable scans. Missing scans were due to clinical time constraints or missed appointments (n=11), clinical deterioration or death (n=8), or withdrawn consent (n=7).

Metabolite data processing. Spectral arrays were generated using the Philips console software providing peak assignation of choline at 3.2 ppm, creatine at 3.0 ppm and NAA at 2.0 ppm. Peak area measurements provided estimates of metabolite levels for 1.5 Tesla scans. For 3 Tesla scans, the mean metabolite level from each set of three voxels was used for analysis. Relative metabolite ratios of choline/NAA, choline/creatine and NAA/creatine were calculated. Tumor metabolite levels and ratios were normalized with data from contralateral normal brain to enable comparison across serial exams and remove dependencies originating from differences in magnetic field strength and echo time. Metabolite levels and ratios in tumor were divided by the value of the same metric in contralateral normal brain on a scan-specific basis to calculate normalized metrics.

Statistical analysis. Frequency and proportions were used to describe the categorical variables. Median and range was used to describe continuous variables. Serial changes in metabolites were assessed by paired analysis in groups of patients at two time points using the Wilcoxon signed-rank test (two-tailed). Overall survival (OS) and progression-free survival (PFS) were defined as the time to death from any cause and clinical or radiologic progression, respectively, from date of initial diagnosis. In cases where pseudoprogression was suspected, serial MRIs were reviewed in interdisciplinary rounds that included a neuroradiologist. The Kaplan-Meier method was used to estimate OS and PFS with patients censored at last follow-up if alive. Median follow-up time was calculated by reversing the censoring variable in the Kaplan-Meier analysis of death. Log-rank tests were used to compare the survival curves of OS and PFS stratified for clinicopathologic factors. Association of change in normalized choline (nCho), normalized NAA (nNAA), normalized creatine (nCr), normalized choline/NAA (nCho/NAA), normalized choline/creatine (nCho/Cr), and normalized NAA/creatine (nNAA/Cr) with OS and PFS was analyzed using a Cox regression model that was adjusted for age, KPS, and delivery of concurrent chemotherapy with RT. Threshold values for changes in metrics were set at 20% and 40% and tested in the opposite direction of change during the examined time period; for example, 20% and 40% increase in metrics that were decreasing over the time period. Results were considered statistically significant at p<0.05. All analyses were conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA).

Results

Patient population. Forty-four consecutively eligible patients were enrolled from May 27, 2004, to July 28, 2009. The last visit with a trial patient was conducted on February 8, 2018. Three patients without histological confirmation of high-grade glioma were excluded from analysis. Baseline patient characteristics of patients included in the analyses are listed in Table I. The median age was 53 years, and most patients had a KPS of 80 or higher (n=26) and pathological diagnosis of glioblastoma (n=29). All patients received maximal safe resection (n=26) or biopsy (n=15). Concurrent chemotherapy was delivered with RT in 30 patients (73.2%) using temozolomide (n=27) or lomustine (n=3). No patient received bevacizumab.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table I.

Patient characteristics at baseline.

Median follow-up time for patients at risk was 160.7 months. At trial completion, data regarding vital status was available for 39 patients (95.1%) and missing for two patients who withdrew consent. Median survival for the entire cohort was 20.4 months, with 2-and 5-year actuarial OS of 43.9% (95% CI=25.8-56.8) and 18.1% (95% CI=6.0-30.3). Twenty-eight patients progressed during the study period. Median PFS was 17.4 months, with 2-and 5-year actuarial PFS of 29.0% (95% CI=12.1-45.9) and 9.1% (95% CI=2.3-20.5). Improved median OS was seen in patients with age <70 years vs. ≥70 years (21.7 months vs. 4.7 months; log-rank p=0.005), KPS ≥80 vs. <80 (25.5 months vs. 10.4 months; log-rank p=0.008), anaplastic histology (37.5 months vs. 12.1 months; log-rank p=0.026), gross total or subtotal resection vs. biopsy alone (35.0 months vs. 11.8 months; log-rank p=0.004) and in patients treated with concurrent chemoradiation vs. RT alone (24.6 months vs. 5.3 months; log-rank p=0.001).

Serial changes in metabolites. Table II shows changes in normalized metabolic parameters for subjects with evaluable MRSI data at a minimum of two time points (n=31). Normalized choline was elevated at baseline, with decrease at mid-RT but this did not reach statistical significance (p=0.051). This finding may be consistent with an early reduction in tumor burden. At post-RT, relative to baseline, there were decreases in nCho and nCho/NAA and a trend of decrease in nCho/Cr (p=0.052). These findings are consistent with response to therapy. There were no statistically significant changes in nNAA at mid-RT or post-RT. While matched-paired analysis showed that nNAA/Cr decreased from baseline to post-RT, the absolute difference was minimal (8.9%).

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table II.

Changes in metabolic parameters with radiation therapy (RT).

Survival analyses. In the period from baseline to mid-RT, improved median OS was seen in patients with ≤20% increase in nCho of 24.6 months (IQR=11.8-64.7 months) vs. 8.0 months (IQR=5.3-21.6 months) for patients with >20% increase (log-rank p=0.014). During this period, median PFS was 17.4 months (IQR=9.9-31.9 months) vs. 4.5 months (IQR=0.0-7.3 months; log-rank p=0.033) in patients with ≤20% increase in nCho vs. >20% increase, respectively. Figure 2 shows OS by patient groups defined by threshold values responsible for the strongest statistical significance in change of metabolic ratios from baseline to mid-RT. Differences in OS for increase in nCho/NAA for all patients did not reach statistical significance on Kalpan-Meier analysis (median OS, 35.0 months for ≤20% increase vs. 9.9 months for >20% increase; log-rank p=0.20; Figure 2A). In subgroup analysis of patients with glioblastoma, patients with an increase in nCho/NAA of ≤20% had median OS of 20.4 months (IQR=11.8-64.7 months) vs. 9.9 months (IQR=5.3-19.7 months) in patients with an increase of >20% (log-rank p=0.029; Figure 2B). There were no differences in OS in patient groups separated by a threshold value of ≤40% vs. >40% for nCho/NAA. For nCho/Cr, median OS was improved in patients with ≤40% increase in this metric between baseline and mid-RT for patients with glioblastoma (Figure 2D). Overall survival was not different in patient groups defined by increases in nCr and nNAA/Cr from the period of baseline to mid-RT.

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

Kaplan-Meier analyses for overall survival of patients, by change in metabolites from baseline to mid-radiation therapy. Overall survival according to change in (A) normalized choline/N-acetylaspartate (nCho/NAA) in all patients, (B) nCho/NAA in patients with glioblastoma, (C) normalized choline/creatine (nCho/Cr) in all patients, and (D) nCho/Cr in patients with glioblastoma.

Overall survival by change in choline metric ratios in the period from baseline to post-RT was also examined. There were no differences in OS between patient groups defined by threshold values of changes in any metabolic metric during this period. Similar results were seen in exploratory analysis of the subset of patients with glioblastoma.

Regression analysis. Metabolic variations associated with survival in regression analysis are listed in Table III. In the period from baseline to mid-RT, a threshold value of ≤20% increase in nCho or nCho/NAA was associated with improved OS in the study population. During this period, an increase of ≤20% in nCho had a trend towards association with improved PFS (p=0.051) and an increase of ≤40% in nCho was associated with improved PFS (p=0.013). For nCho/NAA, an increase of ≤20% from baseline to mid-RT was associated with improved OS for all patients and in the subset of patients with glioblastoma. In patients with glioblastoma, decreases of ≤40% of nNAA/Cr from baseline to mid-RT were associated with improved PFS (p=0.021) but not OS (p=0.24). Changes in nCho/Cr and nCr from baseline to mid-RT were not associated with differences in OS or PFS.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table III.

Metabolic changes associated with overall survival on Cox regression analysis adjusted for age, Karnofsky Performance Status, and delivery of concurrent chemotherapy during radiation therapy (RT).

In the period from baseline to post-RT, changes in metabolic metrics during this period were not associated with OS or PFS. Metabolite levels and ratios at baseline were not associated with differences in OS or PFS. Multivariable analysis was not performed for subgroups of patients due to small sample size.

Discussion

This prospective study of serial 1H MRSI for patients with high-grade glioma found associations between threshold values for changes in metabolic metrics and survival. Our findings provide specific threshold values for metabolic changes that predict response to therapy as early as mid-RT. An increase in normalized choline and choline/NAA of >20% from baseline to mid-RT was associated with reduced OS. In the period from baseline to post-RT, however, changes in metabolic metrics were not associated with OS. These results support the role of MRSI to provide noninvasive tracking of tumor metabolism and burden, consistent with prior studies using MRSI to provide an early prediction of response to therapy and survival.

Previous studies have shown elevated levels of choline with reduced levels of NAA and creatine within the tumor compared to normal brain (9-11), which is consistent with our findings. Increased choline is thought to be associated with cell membrane phospholipid turnover secondary to tumor cell proliferation and cell density (9, 10). Ratios of choline/NAA and choline/creatine in tumor are often used to measure metabolic abnormality in glioblastoma. In our patients, the normalized choline, choline/NAA, and choline/creatine ratios were elevated at baseline then decreased over time with RT, consistent with other studies (10, 17, 20). These declines presumably reflect response to treatment leading to reduction in cell proliferation and cell density within the tumor. As this occurs, it becomes increasingly difficult to distinguish tumor from normal brain after treatment, potentially explaining the low sensitivity and specificity of MRSI to distinguish tumor recurrence from pseudo-progression (21, 22).

Variation in choline levels early in the course of therapy has been reported as a predictor of survival, consistent with our dataset (12, 14, 15, 17). This study provides a specific threshold value of >20% increase choline/NAA at mid-RT, above which OS was reduced. The choline-NAA index (CNI), calculated from choline and NAA levels using iterative regression using multiple voxels in tumor and regions of normal brain (23), and excess choline relative to NAA (xsChoN) at mid- and post-RT have been found to correlate to OS and PFS (12, 14, 15), underlying the predictive value of choline early within the treatment course. Muruganandham et al. reported that an increase in mean or median normalized choline/NAA at mid-RT was associated with a higher rate of early progression (17). Correlations to OS were not reported in that study. In recurrent gliomas treated with radiosurgery, increase in choline predicted poor radiologic response and histologically-confirmed tumor recurrence that typically preceded the development of new contrast enhancement (24).

No associations were found, however, for changes of normalized creatine, choline/creatine or NAA/creatine from baseline to mid-RT with OS or PFS. This finding suggests that the early predictive value of creatine may be limited. Levels of creatine and the choline/creatine ratio are thought to be less sensitive measures of treatment response because creatine levels can be heterogeneous in tumors (15, 25-28). In our patients, creatine metrics were more predictive of survival later within the course of therapy from mid-RT to post-RT, when changes in normalized NAA/creatine were associated with OS.

Despite advances in therapy, survival of patients with glioblastoma remains limited (29). Modern management consists of individualized therapy based on known prognostic factors including age, performance status, and molecular markers such as MGMT promoter methylation status (6). Our data provides metabolic predictive factors that could enable adaptive, response-based changes early in therapy before tumor progression becomes clear on conventional MRI or as early as the fourth week of RT. Similar treatment response consisting of decrease in tumor choline/NAA and choline/creatine ratios is seen after initiation of systemic therapy, including as early as two weeks of bevacizumab and temozolomide (30) and one month of cediranib (16), suggesting a broader application for MRSI as a clinically useful tool to detect response early during therapy for glioblastoma. However, few prospective studies investigate whether early intensification of RT or systemic therapy based on early markers of response can improve outcomes. The ongoing SPECTRO-GLIO multicenter phase III trial intends to randomize 220 patients with glioblastoma to receive standard RT of 60 Gy or RT with dose-painting to 72 Gy of metabolic volumes of choline/NAA >2 and contrast-enhancing lesions or resection cavity (31). This study includes centralized analysis of MRSI data and contouring for the experimental arm with the hopes that strong quality controls will provide reliable results. Further studies of risk-adapted therapy based on metabolic outcomes could help prolong time to recurrence or neurological deterioration.

Refinements of MRSI techniques are of interest for future studies. Robustness of spectral acquisition could be a major challenge in the clinical application of 1H MRSI despite recent improvements in acquisition time and spatial resolution (18). No threshold values for metabolite variation have been found that perfectly distinguish all patients with progression or death. Nelson et al. have suggested to study the voxel-by-voxel variations in metabolite levels, rather than simply the mean or summarized values, as a means to improve sensitivity of MRSI (15). Because metabolic tumor volumes are at times different from volumes seen on conventional contrast-enhanced MRI, voxel-by-voxel MRSI data or use of diffusion tensor imaging using MRSI and perfusion imaging could improve target volume contouring during planning of RT (32). Myo-inositol levels measured with MRSI can help predict failure of antiangiogenic treatment in recurrent glioblastoma (33). Diffusion-weighted (34), dynamic contrast-enhanced (35), and chemical exchange saturation transfer (36) MRI sequences are useful for assessing early response to therapy and combination of these modalities with MRSI could further improve their predictive value (37, 38). Compared to positron emission tomography (PET), MRSI does not expose patients to ionizing radiation and offers higher spatial resolution. MRSI is more sensitive in differentiating gliomas from non-neoplastic lesions than 18F-6-L-fluorodihydroxyphenylalanine (18F-FDOPA) PET (39), but combinations of these modalities remain untested for early prediction of treatment response.

This study has some limitations. Inclusion of patients with anaplastic gliomas and lack of molecular classification with IDH status introduces heterogeneity within the population. Subgroup analysis of patients exclusively with glioblastoma, however, demonstrated results consistent with the overall group. Indeed, MRSI response to treatment in low-grade gliomas in previous studies also appears similar to our results in high-grade glioma (40). To our knowledge, this is the largest prospective trial including patients with anaplastic glioma evaluating the early predictive value of MRSI. Our study used both 1.5 T and 3 T scanners for MRSI. Differences in relaxation effects from difference in magnetic field strengths and choice of TE were addressed by normalization of tumor data to corresponding anatomical regions of contralateral normal brain. Other metabolites such as lactate and lipids were not evaluated and should be the focus of further study. Most patients received concurrent temozolomide with RT in this study. Our data cannot be generalized to patients receiving other agents such as bevacizumab.

In summary, this prospective study identifies specific thresholds for changes in metabolic metrics measured on 1H MRS that are associated with survival of patients with high-grade glioma. Threshold cut-off values for changes in normalized choline metrics predicted OS and PFS during the mid-course of multimodality therapy. Association with OS was found for changes in metabolic metrics from baseline to the mid-course of multimodality therapy but not to two months post-multimodality therapy, demonstrating the added value of performing early (mid-course) MRSI. Our findings suggest a clinical role for MRSI and warrant further validation in a multi-institutional setting. Early assessment of treatment response and survival prediction with MRSI could enable risk-adapted local or systemic therapy to help improve outcomes for these patients.

Acknowledgements

The Authors would like to thank Juliette Jordan for coordinating patient visits and MRSI scans for this project. This project was funded by the Alberta Cancer Foundation (IGAR 2005.I10).

Footnotes

  • Authors’ Contributions

    Conception and design were carried out by W.R. and S.P.; data collection and analysis were conducted by M.W., K.W., A.Y., H.Q, S.G. and S.P.; interpretation of the data involved all authors; handling of patients was performed by W.R., A.M., and S.P.; the original draft was written by M.W. and S.P.; the draft was reviewed and edited by all Authors.

  • Conflicts of Interest

    The Authors declare that they have no conflicts of interest.

  • Received March 3, 2022.
  • Revision received March 21, 2022.
  • Accepted March 22, 2022.
  • Copyright © 2022 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

References

  1. ↵
    1. Wen PY,
    2. Macdonald DR,
    3. Reardon DA,
    4. Cloughesy TF,
    5. Sorensen AG,
    6. Galanis E,
    7. Degroot J,
    8. Wick W,
    9. Gilbert MR,
    10. Lassman AB,
    11. Tsien C,
    12. Mikkelsen T,
    13. Wong ET,
    14. Chamberlain MC,
    15. Stupp R,
    16. Lamborn KR,
    17. Vogelbaum MA,
    18. van den Bent MJ and
    19. Chang SM
    : Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28(11): 1963-1972, 2010. PMID: 20231676. DOI: 10.1200/JCO.2009.26.3541
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Huang RY,
    2. Rahman R,
    3. Ballman KV,
    4. Felten SJ,
    5. Anderson SK,
    6. Ellingson BM,
    7. Nayak L,
    8. Lee EQ,
    9. Abrey LE,
    10. Galanis E,
    11. Reardon DA,
    12. Pope WB,
    13. Cloughesy TF and
    14. Wen PY
    : The impact of T2/FLAIR evaluation per RANO criteria on response assessment of recurrent glioblastoma patients treated with bevacizumab. Clin Cancer Res 22(3): 575-581, 2016. PMID: 26490307. DOI: 10.1158/1078-0432.CCR-14-3040
    OpenUrlAbstract/FREE Full Text
  3. ↵
    1. Chang K,
    2. Beers AL,
    3. Bai HX,
    4. Brown JM,
    5. Ly KI,
    6. Li X,
    7. Senders JT,
    8. Kavouridis VK,
    9. Boaro A,
    10. Su C,
    11. Bi WL,
    12. Rapalino O,
    13. Liao W,
    14. Shen Q,
    15. Zhou H,
    16. Xiao B,
    17. Wang Y,
    18. Zhang PJ,
    19. Pinho MC,
    20. Wen PY,
    21. Batchelor TT,
    22. Boxerman JL,
    23. Arnaout O,
    24. Rosen BR,
    25. Gerstner ER,
    26. Yang L,
    27. Huang RY and
    28. Kalpathy-Cramer J
    : Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement. Neuro Oncol 21(11): 1412-1422, 2019. PMID: 31190077. DOI: 10.1093/neuonc/noz106
    OpenUrlCrossRefPubMed
  4. ↵
    1. Boxerman JL,
    2. Zhang Z,
    3. Safriel Y,
    4. Larvie M,
    5. Snyder BS,
    6. Jain R,
    7. Chi TL,
    8. Sorensen AG,
    9. Gilbert MR and
    10. Barboriak DP
    : Early post-bevacizumab progression on contrast-enhanced MRI as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 Central Reader Study. Neuro Oncol 15(7): 945-954, 2013. PMID: 23788270. DOI: 10.1093/neuonc/not049
    OpenUrlCrossRefPubMed
  5. ↵
    1. Gupta T and
    2. Sarin R
    : Poor-prognosis high-grade gliomas: evolving an evidence-based standard of care. Lancet Oncol 3(9): 557-564, 2002. PMID: 12217793. DOI: 10.1016/s1470-2045(02)00853-7
    OpenUrlCrossRefPubMed
  6. ↵
    1. Stupp R,
    2. Hegi ME,
    3. Mason WP,
    4. van den Bent MJ,
    5. Taphoorn MJ,
    6. Janzer RC,
    7. Ludwin SK,
    8. Allgeier A,
    9. Fisher B,
    10. Belanger K,
    11. Hau P,
    12. Brandes AA,
    13. Gijtenbeek J,
    14. Marosi C,
    15. Vecht CJ,
    16. Mokhtari K,
    17. Wesseling P,
    18. Villa S,
    19. Eisenhauer E,
    20. Gorlia T,
    21. Weller M,
    22. Lacombe D,
    23. Cairncross JG,
    24. Mirimanoff RO, European Organisation for Research and Treatment of Cancer Brain Tumour and Radiation Oncology Groups and National Cancer Institute of Canada Clinical Trials Group
    : Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10(5): 459-466, 2009. PMID: 19269895. DOI: 10.1016/S1470-2045(09)70025-7
    OpenUrlCrossRefPubMed
  7. ↵
    1. Chung C,
    2. Metser U and
    3. Ménard C
    : Advances in magnetic resonance imaging and positron emission tomography imaging for grading and molecular characterization of glioma. Semin Radiat Oncol 25(3): 164-171, 2015. PMID: 26050586. DOI: 10.1016/j.semradonc.2015.02.002
    OpenUrlCrossRefPubMed
  8. ↵
    1. Deviers A,
    2. Ken S,
    3. Filleron T,
    4. Rowland B,
    5. Laruelo A,
    6. Catalaa I,
    7. Lubrano V,
    8. Celsis P,
    9. Berry I,
    10. Mogicato G,
    11. Cohen-Jonathan Moyal E and
    12. Laprie A
    : Evaluation of the lactate-to-N-acetyl-aspartate ratio defined with magnetic resonance spectroscopic imaging before radiation therapy as a new predictive marker of the site of relapse in patients with glioblastoma multiforme. Int J Radiat Oncol Biol Phys 90(2): 385-393, 2014. PMID: 25104068. DOI: 10.1016/j.ijrobp.2014.06.009
    OpenUrlCrossRefPubMed
  9. ↵
    1. Dowling C,
    2. Bollen AW,
    3. Noworolski SM,
    4. McDermott MW,
    5. Barbaro NM,
    6. Day MR,
    7. Henry RG,
    8. Chang SM,
    9. Dillon WP,
    10. Nelson SJ and
    11. Vigneron DB
    : Preoperative proton MR spectroscopic imaging of brain tumors: correlation with histopathologic analysis of resection specimens. AJNR Am J Neuroradiol 22(4): 604-612, 2001. PMID: 11290466.
    OpenUrlAbstract/FREE Full Text
  10. ↵
    1. McKnight TR,
    2. von dem Bussche MH,
    3. Vigneron DB,
    4. Lu Y,
    5. Berger MS,
    6. McDermott MW,
    7. Dillon WP,
    8. Graves EE,
    9. Pirzkall A and
    10. Nelson SJ
    : Histopathological validation of a three-dimensional magnetic resonance spectroscopy index as a predictor of tumor presence. J Neurosurg 97(4): 794-802, 2002. PMID: 12405365. DOI: 10.3171/jns.2002.97.4.0794
    OpenUrlCrossRefPubMed
  11. ↵
    1. Ozturk-Isik E,
    2. Pirzkall A,
    3. Lamborn KR,
    4. Cha S,
    5. Chang SM and
    6. Nelson SJ
    : Spatial characteristics of newly diagnosed grade 3 glioma assessed by magnetic resonance metabolic and diffusion tensor imaging. Transl Oncol 5(1): 10-18, 2012. PMID: 22348171. DOI: 10.1593/tlo.11208
    OpenUrlCrossRefPubMed
  12. ↵
    1. Li X,
    2. Jin H,
    3. Lu Y,
    4. Oh J,
    5. Chang S and
    6. Nelson SJ
    : Identification of MRI and 1H MRSI parameters that may predict survival for patients with malignant gliomas. NMR Biomed 17(1): 10-20, 2004. PMID: 15011246. DOI: 10.1002/nbm.858
    OpenUrlCrossRefPubMed
    1. Li Y,
    2. Lupo JM,
    3. Parvataneni R,
    4. Lamborn KR,
    5. Cha S,
    6. Chang SM and
    7. Nelson SJ
    : Survival analysis in patients with newly diagnosed glioblastoma using pre- and postradiotherapy MR spectroscopic imaging. Neuro Oncol 15(5): 607-617, 2013. PMID: 23393206. DOI: 10.1093/neuonc/nos334
    OpenUrlCrossRefPubMed
  13. ↵
    1. Nelson SJ,
    2. Li Y,
    3. Lupo JM,
    4. Olson M,
    5. Crane JC,
    6. Molinaro A,
    7. Roy R,
    8. Clarke J,
    9. Butowski N,
    10. Prados M,
    11. Cha S and
    12. Chang SM
    : Serial analysis of 3D H-1 MRSI for patients with newly diagnosed GBM treated with combination therapy that includes bevacizumab. J Neurooncol 130(1): 171-179, 2016. PMID: 27535746. DOI: 10.1007/s11060-016-2229-3
    OpenUrlCrossRefPubMed
  14. ↵
    1. Nelson SJ,
    2. Kadambi AK,
    3. Park I,
    4. Li Y,
    5. Crane J,
    6. Olson M,
    7. Molinaro A,
    8. Roy R,
    9. Butowski N,
    10. Cha S and
    11. Chang S
    : Association of early changes in 1H MRSI parameters with survival for patients with newly diagnosed glioblastoma receiving a multimodality treatment regimen. Neuro Oncol 19(3): 430-439, 2017. PMID: 27576874. DOI: 10.1093/neuonc/now159
    OpenUrlCrossRefPubMed
  15. ↵
    1. Andronesi OC,
    2. Esmaeili M,
    3. Borra RJH,
    4. Emblem K,
    5. Gerstner ER,
    6. Pinho MC,
    7. Plotkin SR,
    8. Chi AS,
    9. Eichler AF,
    10. Dietrich J,
    11. Ivy SP,
    12. Wen PY,
    13. Duda DG,
    14. Jain R,
    15. Rosen BR,
    16. Sorensen GA and
    17. Batchelor TT
    : Early changes in glioblastoma metabolism measured by MR spectroscopic imaging during combination of anti-angiogenic cediranib and chemoradiation therapy are associated with survival. NPJ Precis Oncol 1: 20, 2017. PMID: 29202103. DOI: 10.1038/s41698-017-0020-3
    OpenUrlCrossRefPubMed
  16. ↵
    1. Muruganandham M,
    2. Clerkin PP,
    3. Smith BJ,
    4. Anderson CM,
    5. Morris A,
    6. Capizzano AA,
    7. Magnotta V,
    8. McGuire SM,
    9. Smith MC,
    10. Bayouth JE and
    11. Buatti JM
    : 3-Dimensional magnetic resonance spectroscopic imaging at 3 Tesla for early response assessment of glioblastoma patients during external beam radiation therapy. Int J Radiat Oncol Biol Phys 90(1): 181-189, 2014. PMID: 24986746. DOI: 10.1016/j.ijrobp.2014.05.014
    OpenUrlCrossRefPubMed
  17. ↵
    1. Cao Y,
    2. Tseng CL,
    3. Balter JM,
    4. Teng F,
    5. Parmar HA and
    6. Sahgal A
    : MR-guided radiation therapy: transformative technology and its role in the central nervous system. Neuro Oncol 19(suppl_2): ii16-ii29, 2017. PMID: 28380637. DOI: 10.1093/neuonc/nox006
    OpenUrlCrossRefPubMed
  18. ↵
    1. Quon H,
    2. Brunet B,
    3. Alexander A,
    4. Murtha A,
    5. Abdulkarim B,
    6. Fulton D,
    7. Smerdely M,
    8. Johnson M,
    9. Urtasun R,
    10. Patel S,
    11. Ghosh S and
    12. Roa W
    : Changes in serial magnetic resonance spectroscopy predict outcome in high-grade glioma during and after postoperative radiotherapy. Anticancer Res 31(10): 3559-3565, 2011. PMID: 21965778.
    OpenUrlAbstract/FREE Full Text
  19. ↵
    1. Oh J,
    2. Henry RG,
    3. Pirzkall A,
    4. Lu Y,
    5. Li X,
    6. Catalaa I,
    7. Chang S,
    8. Dillon WP and
    9. Nelson SJ
    : Survival analysis in patients with glioblastoma multiforme: predictive value of choline-to-N-acetylaspartate index, apparent diffusion coefficient, and relative cerebral blood volume. J Magn Reson Imaging 19(5): 546-554, 2004. PMID: 15112303. DOI: 10.1002/jmri.20039
    OpenUrlCrossRefPubMed
  20. ↵
    1. Alimenti A,
    2. Delavelle J,
    3. Lazeyras F,
    4. Yilmaz H,
    5. Dietrich PY,
    6. de Tribolet N and
    7. Lövblad KO
    : Monovoxel 1H magnetic resonance spectroscopy in the progression of gliomas. Eur Neurol 58(4): 198-209, 2007. PMID: 17823533. DOI: 10.1159/000107940
    OpenUrlCrossRefPubMed
  21. ↵
    1. Crain ID,
    2. Elias PS,
    3. Chapple K,
    4. Scheck AC,
    5. Karis JP and
    6. Preul MC
    : Improving the utility of 1H-MRS for the differentiation of glioma recurrence from radiation necrosis. J Neurooncol 133(1): 97-105, 2017. PMID: 28555423. DOI: 10.1007/s11060-017-2407-y
    OpenUrlCrossRefPubMed
  22. ↵
    1. McKnight TR,
    2. Noworolski SM,
    3. Vigneron DB and
    4. Nelson SJ
    : An automated technique for the quantitative assessment of 3D-MRSI data from patients with glioma. J Magn Reson Imaging 13(2): 167-177, 2001. PMID: 11169821. DOI: 10.1002/1522-2586(200102)13:2<167::aid-jmri1026>3.0.co;2-k
    OpenUrlCrossRefPubMed
  23. ↵
    1. Graves EE,
    2. Nelson SJ,
    3. Vigneron DB,
    4. Verhey L,
    5. McDermott M,
    6. Larson D,
    7. Chang S,
    8. Prados MD and
    9. Dillon WP
    : Serial proton MR spectroscopic imaging of recurrent malignant gliomas after gamma knife radiosurgery. AJNR Am J Neuroradiol 22(4): 613-624, 2001. PMID: 11290467.
    OpenUrlAbstract/FREE Full Text
  24. ↵
    1. Li Y,
    2. Lupo JM,
    3. Polley MY,
    4. Crane JC,
    5. Bian W,
    6. Cha S,
    7. Chang S and
    8. Nelson SJ
    : Serial analysis of imaging parameters in patients with newly diagnosed glioblastoma multiforme. Neuro Oncol 13(5): 546-557, 2011. PMID: 21297128. DOI: 10.1093/neuonc/noq194
    OpenUrlCrossRefPubMed
    1. Wright AJ,
    2. Fellows G,
    3. Byrnes TJ,
    4. Opstad KS,
    5. McIntyre DJ,
    6. Griffiths JR,
    7. Bell BA,
    8. Clark CA,
    9. Barrick TR and
    10. Howe FA
    : Pattern recognition of MRSI data shows regions of glioma growth that agree with DTI markers of brain tumor infiltration. Magn Reson Med 62(6): 1646-1651, 2009. PMID: 19785020. DOI: 10.1002/mrm.22163
    OpenUrlCrossRefPubMed
    1. Pirzkall A,
    2. McGue C,
    3. Saraswathy S,
    4. Cha S,
    5. Liu R,
    6. Vandenberg S,
    7. Lamborn KR,
    8. Berger MS,
    9. Chang SM and
    10. Nelson SJ
    : Tumor regrowth between surgery and initiation of adjuvant therapy in patients with newly diagnosed glioblastoma. Neuro Oncol 11(6): 842-852, 2009. PMID: 19229057. DOI: 10.1215/15228517-2009-005
    OpenUrlCrossRefPubMed
  25. ↵
    1. Pirzkall A,
    2. Li X,
    3. Oh J,
    4. Chang S,
    5. Berger MS,
    6. Larson DA,
    7. Verhey LJ,
    8. Dillon WP and
    9. Nelson SJ
    : 3D MRSI for resected high-grade gliomas before RT: tumor extent according to metabolic activity in relation to MRI. Int J Radiat Oncol Biol Phys 59(1): 126-137, 2004. PMID: 15093908. DOI: 10.1016/j.ijrobp.2003.08.023
    OpenUrlCrossRefPubMed
  26. ↵
    1. Ostrom QT,
    2. Cioffi G,
    3. Gittleman H,
    4. Patil N,
    5. Waite K,
    6. Kruchko C and
    7. Barnholtz-Sloan JS
    : CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012-2016. Neuro Oncol 21(Suppl 5): v1-v100, 2019. PMID: 31675094. DOI: 10.1093/neuonc/noz150
    OpenUrlCrossRefPubMed
  27. ↵
    1. Ratai EM,
    2. Zhang Z,
    3. Snyder BS,
    4. Boxerman JL,
    5. Safriel Y,
    6. McKinstry RC,
    7. Bokstein F,
    8. Gilbert MR,
    9. Sorensen AG and
    10. Barboriak DP
    : Magnetic resonance spectroscopy as an early indicator of response to anti-angiogenic therapy in patients with recurrent glioblastoma: RTOG 0625/ACRIN 6677. Neuro Oncol 15(7): 936-944, 2013. PMID: 23645534. DOI: 10.1093/neuonc/not044
    OpenUrlCrossRefPubMed
  28. ↵
    1. Laprie A,
    2. Ken S,
    3. Filleron T,
    4. Lubrano V,
    5. Vieillevigne L,
    6. Tensaouti F,
    7. Catalaa I,
    8. Boetto S,
    9. Khalifa J,
    10. Attal J,
    11. Peyraga G,
    12. Gomez-Roca C,
    13. Uro-Coste E,
    14. Noel G,
    15. Truc G,
    16. Sunyach MP,
    17. Magné N,
    18. Charissoux M,
    19. Supiot S,
    20. Bernier V,
    21. Mounier M,
    22. Poublanc M,
    23. Fabre A,
    24. Delord JP and
    25. Cohen-Jonathan Moyal E
    : Dose-painting multicenter phase III trial in newly diagnosed glioblastoma: the SPECTRO-GLIO trial comparing arm A standard radiochemotherapy to arm B radiochemotherapy with simultaneous integrated boost guided by MR spectroscopic imaging. BMC Cancer 19(1): 167, 2019. PMID: 30791889. DOI: 10.1186/s12885-019-5317-x
    OpenUrlCrossRefPubMed
  29. ↵
    1. Li C,
    2. Wang S,
    3. Yan JL,
    4. Torheim T,
    5. Boonzaier NR,
    6. Sinha R,
    7. Matys T,
    8. Markowetz F and
    9. Price SJ
    : Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging. J Neurosurg 132(5): 1465-1472, 2019. PMID: 31026822. DOI: 10.3171/2018.12.JNS182926
    OpenUrlCrossRefPubMed
  30. ↵
    1. El-Abtah ME,
    2. Wenke MR,
    3. Talati P,
    4. Fu M,
    5. Kim D,
    6. Weerasekera A,
    7. He J,
    8. Vaynrub A,
    9. Vangel M,
    10. Rapalino O,
    11. Andronesi O,
    12. Arrillaga-Romany I,
    13. Forst DA,
    14. Yen YF,
    15. Rosen B,
    16. Batchelor TT,
    17. Gonzalez RG,
    18. Dietrich J,
    19. Gerstner ER and
    20. Ratai EM
    : Myo-inositol levels measured with MR spectroscopy can help predict failure of antiangiogenic treatment in recurrent glioblastoma. Radiology 302(2): 410-418, 2022. PMID: 34751617. DOI: 10.1148/radiol.2021210826
    OpenUrlCrossRefPubMed
  31. ↵
    1. Mardor Y,
    2. Pfeffer R,
    3. Spiegelmann R,
    4. Roth Y,
    5. Maier SE,
    6. Nissim O,
    7. Berger R,
    8. Glicksman A,
    9. Baram J,
    10. Orenstein A,
    11. Cohen JS and
    12. Tichler T
    : Early detection of response to radiation therapy in patients with brain malignancies using conventional and high b-value diffusion-weighted magnetic resonance imaging. J Clin Oncol 21(6): 1094-1100, 2003. PMID: 12637476. DOI: 10.1200/JCO.2003.05.069
    OpenUrlAbstract/FREE Full Text
  32. ↵
    1. Patel P,
    2. Baradaran H,
    3. Delgado D,
    4. Askin G,
    5. Christos P,
    6. John Tsiouris A and
    7. Gupta A
    : MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro Oncol 19(1): 118-127, 2017. PMID: 27502247. DOI: 10.1093/neuonc/now148
    OpenUrlCrossRefPubMed
  33. ↵
    1. Mehrabian H,
    2. Myrehaug S,
    3. Soliman H,
    4. Sahgal A and
    5. Stanisz GJ
    : Evaluation of glioblastoma response to therapy with chemical exchange saturation transfer. Int J Radiat Oncol Biol Phys 101(3): 713-723, 2018. PMID: 29893279. DOI: 10.1016/j.ijrobp.2018.03.057
    OpenUrlCrossRefPubMed
  34. ↵
    1. Zeng QS,
    2. Li CF,
    3. Liu H,
    4. Zhen JH and
    5. Feng DC
    : Distinction between recurrent glioma and radiation injury using magnetic resonance spectroscopy in combination with diffusion-weighted imaging. Int J Radiat Oncol Biol Phys 68(1): 151-158, 2007. PMID: 17289287. DOI: 10.1016/j.ijrobp.2006.12.001
    OpenUrlCrossRefPubMed
  35. ↵
    1. Lotumolo A,
    2. Caivano R,
    3. Rabasco P,
    4. Iannelli G,
    5. Villonio A,
    6. D’ Antuono F,
    7. Gioioso M,
    8. Zandolino A,
    9. Macarini L,
    10. Guglielmi G and
    11. Cammarota A
    : Comparison between magnetic resonance spectroscopy and diffusion weighted imaging in the evaluation of gliomas response after treatment. Eur J Radiol 84(12): 2597-2604, 2015. PMID: 26391231. DOI: 10.1016/j.ejrad.2015.09.005
    OpenUrlCrossRefPubMed
  36. ↵
    1. Morana G,
    2. Piccardo A,
    3. Puntoni M,
    4. Nozza P,
    5. Cama A,
    6. Raso A,
    7. Mascelli S,
    8. Massollo M,
    9. Milanaccio C,
    10. Garrè ML and
    11. Rossi A
    : Diagnostic and prognostic value of 18F-DOPA PET and 1H-MR spectroscopy in pediatric supratentorial infiltrative gliomas: a comparative study. Neuro Oncol 17(12): 1637-1647, 2015. PMID: 26405202. DOI: 10.1093/neuonc/nov099
    OpenUrlCrossRefPubMed
  37. ↵
    1. Kim MM,
    2. Lawrence TS and
    3. Cao Y
    : Advances in magnetic resonance and positron emission tomography imaging: assessing response in the treatment of low-grade glioma. Semin Radiat Oncol 25(3): 172-180, 2015. PMID: 26050587. DOI: 10.1016/j.semradonc.2015.02.003
    OpenUrlCrossRefPubMed
PreviousNext
Back to top

In this issue

Anticancer Research: 42 (5)
Anticancer Research
Vol. 42, Issue 5
May 2022
  • Table of Contents
  • Table of Contents (PDF)
  • Index by author
  • Back Matter (PDF)
  • Ed Board (PDF)
  • Front Matter (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on Anticancer Research.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Early Metabolic Changes in 1H-MRSI Predictive for Survival in Patients With Newly Diagnosed High-grade Glioma
(Your Name) has sent you a message from Anticancer Research
(Your Name) thought you would like to see the Anticancer Research web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
10 + 6 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
Early Metabolic Changes in 1H-MRSI Predictive for Survival in Patients With Newly Diagnosed High-grade Glioma
MICHAEL H. WANG, WILSON ROA, KEITH WACHOWICZ, ATIYAH YAHYA, ALBERT MURTHA, JOHN AMANIE, JONATHAN CHAINEY, HARVEY QUON, SUNITA GHOSH, SAMIR PATEL
Anticancer Research May 2022, 42 (5) 2665-2673; DOI: 10.21873/anticanres.15744

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Reprints and Permissions
Share
Early Metabolic Changes in 1H-MRSI Predictive for Survival in Patients With Newly Diagnosed High-grade Glioma
MICHAEL H. WANG, WILSON ROA, KEITH WACHOWICZ, ATIYAH YAHYA, ALBERT MURTHA, JOHN AMANIE, JONATHAN CHAINEY, HARVEY QUON, SUNITA GHOSH, SAMIR PATEL
Anticancer Research May 2022, 42 (5) 2665-2673; DOI: 10.21873/anticanres.15744
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Patients and Methods
    • Results
    • Discussion
    • Acknowledgements
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • Bone Toxicity Case Report Combining Encorafenib, Cetuximab and WNT974 in a Phase I Trial
  • Assessment of Breakthrough Cancer Pain Among Female Patients With Cancer: Knowledge, Management and Characterization in the IOPS-MS Study
  • Low-dose Apalutamide in Non-metastatic Castration-resistant Prostate Cancer: A Case Series
Show more Clinical Studies

Similar Articles

Keywords

  • prospective study
  • predictive biomarker
  • high-grade glioma
  • glioblastoma
  • magnetic resonance spectroscopic imaging
Anticancer Research

© 2025 Anticancer Research

Powered by HighWire