Proton magnetic resonance spectroscopy (1h-magnetic resonance spectroscopy) in assessing metabolic changes in the perifocal zone in high-grade gliomas (literature review)

Background. High-grade gliomas are characterized by infiltrative growth with possible presence of tumor cells in the perifocal zone, which creates difficulties in determining the true tumor boundaries when using standard MRI diagnostic methods.
Objective: To present a review of current publications on the application of proton magnetic resonance spectroscopy (¹H-MRS) in assessing metabolic changes in the perifocal zone in high-grade gliomas.
Materials and methods. A systematic analysis of publications in PubMed, Scopus, eLibrary and Google Scholar databases for the period 2015-2024 was conducted using keywords: "¹H-MRS", "high-grade glioma", "glioblastoma", "peritumoral zone", "metabolic changes".
Results. Proton MR spectroscopy allows detection of metabolic disorders in the perifocal zone of gliomas, including increased choline (Cho) levels, decreased N-acetylaspartate (NAA) and appearance of lactate (Lac). The most significant prognostic markers are elevated Cho/NAA ratios >1.99 and Cho/Cr ratios >1.73, which correlate with early tumor recurrence and presence of infiltrating tumor cells in noncontrast enhancing zones.
Conclusion. Integration of ¹H-MRS into the clinical protocol for evaluating high-grade gliomas improves diagnostic accuracy, enhances surgical and radiation therapy planning, and allows optimization of personalized treatment approaches for patients

1. Louis D.N., Perry A., Wesseling P., Brat D.J., Cree I.A., Figarella-Branger D. et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncology. 2021;23(8):1231-1251. https://doi.org/10.1093/neuonc/noab106

2. Shukla G., Alexander G.S., Bakas S., Nikam R., Talekar K., Palmer J.D. et al. Advanced magnetic resonance imaging in glioblastoma: a review. Chinese Clinical Oncology. 2017;6(4):40. https://doi.org/doi: 10.21037/cco.2017.06.28

3. Zakharova N.E., Batalov A.I., Pogosbekyan E.L., Goryaynov S.A., Fadeeva L.M., Bykanov A.E., Tyurina A.N., Chekhonin I.V., Galstyan S.A., Pronin I.N., Usachev D.Yu. Magnetic Resonance Imaging in Studies of Perifocal Zone of Brain Gliomas (a Literature Review). Radiology - Practice. 2024;(1):20-36. (In Russ.) https://doi.org/10.52560/2713-0118-2024-1-20-36

4. Zhukova T.V., Shanko Yu.G., Beletsky A.V., Ivanova N.E., Zabrodskaya Yu.M., Sitovskaya D.A., Zrelov A.A., Shirinsky A.A. Pathomorphological assessment of the peritumorous zone of highly malignant gliomas as a criterion for minimizing the volume of surgical intervention (original research). Neurology and Neurosurgery. Eastern Europe. 2022; 12(1):17-28. (In Russ.) https://doi.org/10.34883/PI.2022.12.1.043

5. Zhukova T.V., Shan’ko Yu.G., Beletskiy A.V., Shirinskiy A.A., Ryabtseva S.N., Ivanova N.E., Nechaeva A.S., Potemkina Ye.G., Vorob’yeva O.M., Sitovskaya D.A., Zrelov A.A., Zabrodskaya Yu.M. Determination of the volume of surgical intervention for high-grade gliomas, taking into account the characteristics of the peritumorous zone. Russian Neurosurgical Journal named after Professor A. L. Polenov. 2021;13(4):29-37. (In Russ.)

6. Torp S.H., Solheim O., Skjulsvik A.J. The WHO 2021 Classification of Central Nervous System tumours: a practical update on what neurosurgeons need to know - a minireview. Acta Neurochirurgica. 2022;164(9):2453-2464. https://doi.org/10.1007/s00701-022-05301-y

7. Garanina N.V., Dolgushin M.B., Lapteva M.G., Fadeeva L.M., Sashin D.V. Peculiarities of the Application of Diffusion-Curtosis MRI in the Differential Diagnosis of Glial Brain Tumors and Solitary Metastasis. Journal of oncology: diagnostic radiology and radiotherapy. 2023;6(1):26-40. (In Russ.) https://doi.org/10.37174/2587-7593-2023-6-1-26-40

8. Cui Y., Zeng W., Jiang H., Ren X., Lin S., Fan Y. et al. Ratio in Postoperative Peritumoral Edema Zone Is Associated With Earlier Recurrence of Glioblastoma. Frontiers in Neurology. 2020;4(11):592155. https://doi.org/10.3389/fneur.2020.592155

9. Liu H., Zhang Q., Niu S., Liu H. Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C-Means Algorithm. Contrast Media & Molecular Imaging. 2022;2022(1):331512. https://doi.org/10.1155/2022/3315121

10. Egerton A. The potential of 1H-MRS in CNS drug development. Psychopharmacology. 2021;238(5):1241-1254. https://doi.org/10.1007/s00213-019-05344-7

11. Aida N. 1H-MR spectroscopy of the early developmental brain, neonatal encephalopathies, and neurometabolic disorders. Magnetic Resonance in Medical Sciences. – 2022;21(1):9-28. https://doi.org/10.2463/mrms.rev.2021-0055

12. Pellegatta S., Valletta L., Corbetta C., Patanè M., Zucca I., Riccardi Sirtori F. et al. Effective immuno-targeting of the IDH1 mutation R132H in a murine model of intracranial glioma. Acta neuropathologica communications. 2015;3(4):1-12. https://doi.org/10.1186/s40478-014-0180-0

13. Padelli F., Mazzi F., Erbetta A., Chiapparini L., Doniselli F.M., Palermo S. et al. In vivo brain MR spectroscopy in gliomas: clinical and preclinical chances. Clinical and Translational imaging. 2022;10(5):495-515. https://doi.org/10.1007/s40336-022-00502-y

14. Fairgrieve-Park L., Fallone C.J., Yahya A. Long TE PRESS and STEAM for measuring the triglyceride glycerol CH₂ protons at 3T. NMR in Biomedicine. 2019;32(2):e4021. https://doi.org/10.1002/nbm.4021

15. Tal A., Gonen O. Spectroscopic localization by simultaneous acquisition of the double-spin and stimulated echoes. Magnetic Resonance in Medicine. 2015;73(1):31–43. https://doi.org/10.1002/mrm.25112

16. Ballestín A., Armocida D., Ribecco V., Seano G. Peritumoral brain zone in glioblastoma: Biological, clinical and mechanical features. Frontiers in Immunology. 2024;15:1347877. https://doi.org/10.3389/fimmu.2024.1347877

17. Cordova J.S., Shu H-K.G., Liang Z., Gurbani S.S., Cooper L.A.D., Holder C.A. et al. Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients. Neuro-oncology. 2016;18(8):1180–9. https://doi.org/10.1093/neuonc/now036

18. Tarnawski R., Sokol M., Pieniazek P., Maciejewski B., Walecki J., Miszczyk L. et al. 1H-MRS in vivo predicts the early treatment outcome of postoperative radiotherapy for malignant gliomas. International Journal of Radiation Oncology, Biology, Physics. 2002;52(5):1271–6. https://doi.org/10.1016/S0360-3016(01)02769-9

19. Tolia M., Verganelakis D., Tsoukalas N., Kyrgias G., Papathanasiou M., Mosa E. et al. Prognostic value of MRS metabolites in postoperative irradiated high grade gliomas. BioMed Research International. 2015;(2015):341042. https://doi.org/10.1155/2015/341042

20. Ruiz-Rodado V., Brender J.R., Cherukuri M.K., Gilbert M.R., Larion M. Magnetic resonance spectroscopy for the study of cns malignancies. Progress in Nuclear Magnetic Resonance Spectroscopy. 2021;122:23-41. https://doi.org/10.1016/j.pnmrs.2020.11.001

21. Ikeguchi R., Shimizu Y., Abe K., Shimizu S., Maruyama T., Nitta M. et al. Proton magnetic resonance spectroscopy differentiates tumefactive demyelinating lesions from gliomas. Multiple Sclerosis and Related Disorders. 2018;26:77-84. https://doi.org/10.1016/j.msard.2018.08.025

22. Lu W., Jin F., Zou Y., Liu Y., Gao P., Zhao Y. et al. 1H-MRS parameters in non-enhancing peritumoral regions can predict the recurrence of glioblastoma. Scientific Reports. 2024;14 - 292958. https://doi.org/10.1038/s41598-024-80610-z

23. Crain I.D., Elias P.S., Chapple K., Schek A.C., Karis J.P., Preul Mark C. Improving the utility of 1H-MRS for the differentiation of glioma recurrence from radiation necrosis. Journal of Neuro-oncology. 2017;133(1):97-105. https://doi.org/10.1007/s11060-017-2407-y

24. Feng A., Yuan P., Huang T., Li L., Lyu J. Distinguishing tumor recurrence from radiation necrosis in treated glioblastoma using multiparametric MRI. Academic Radiology. 2022;29(9):1320-1331. https://doi.org/10.1016/j.acra.2021.11.008.

25. Henning A. Proton and multinuclear MR spectroscopy in the human brain at ultra-high field: a review. NeuroImage. 2018;168:181-198. https://doi.org/10.1016/j.neuroimage.2017.06.046.

26. Aamir M., Rahman Z., Bhatti U.A., Abro W.A., Bhutto J.A., He Z. An automated deep learning framework for brain tumor classification using MRI imagery. Scientific Reports. 2025;15(1):17593. https://doi.org/10.1038/s41598-025-02209-2.

27. Hangel G., Schmitz-Abecassis B., Sollmann N., Pinto J., Arzanforoosh F., Barkhof F. et al., Advanced MR Techniques for Preoperative Glioma Characterization: Part 2. Journal of Magnetic Resonance Imaging. 2023;57(4):1676-1695. https://doi.org/10.1002/jmri.28663.

28. Maslov N.E., Trufanov G.E., Moiseenko V.M., Valenkova D.A., Efimtsev A.Yu., Plakhotina N.A., Sidorina A.S. Radiogenomic approach to glial tumors imaging under conditions of initial diagnostic measures: adaptation principles development. Bulletin of the Medical Institute "REAVIZ" (Rehabilitation, Doctor and Health). 2024;14(1):168-176. (In Russ.) https://doi.org/10.20340/vmi-rvz.2024.1.MIM.3