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:: Volume 21, Issue 4 (10-2023) ::
Int J Radiat Res 2023, 21(4): 769-777 Back to browse issues page
Predictive Biomarkers of Brain tumor Lesions through Correlation of Histopathological changes with Metabolites by Magnetic Resonance Spectroscopy
H. Smitha , V.N. Meena Devi , K.S. Sreekanth , J. Vinoo
Department of Physics, Noorul Islam Centre of Higher Education, Kumarakovil, Kanyakumari & Department of Physiology, Sree Gokulam Medical College & Research Foundation, Venjaramoodu.P.O. Trivandrum, India , smithavinod2000@yahoo.co.in
Abstract:   (518 Views)
Background: Brain tumors like intracranial metastases, meningioma, gliomas, etc are the most prevalent brain tumors. Magnetic Resonance Spectroscopy (MRS) helps in the differentiation of high grade, low grade brain tumors, brain neoplasms, etc. Materials and Methods: This study was conducted in the Radiology Department of one of the major tertiary health care centers in South Kerala. Patients suspected of brain tumors were subjected to both MRS and histopathological examinations after the surgery. A total of 69 patients were included. Histopathological findings were evaluated and grouped as benign, atypical, and anaplastic tumors and correlated with MRS findings. Statistical analysis was done by SPSS version 16. Friedman test was used for comparison. Results: In this study, MRS images of 69 brain tumor lesions were studied and compared for metabolic ratios and pathogenesis. MRS spectrum gives different peaks of specific metabolites of brain tumors like lipid, alanine, lactate, glycine, glutamate /glutamine, myoinositol, etc. Histopathological results also show different pathological findings. Conclusions:  Magnetic Resonance Spectroscopy has a wide range of sensitivity to and is evaluate the different metabolites of brain lesions. The quantification of tissue metabolites can potentially identify the pathological change, at the biochemical level which creates further therapeutic interventions.
Keywords: brain tumors, magnetic resonance spectroscopy, metabolites
Full-Text [PDF 1406 kb]   (314 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
1. 1. Aldape K, Brindle KM, Chesler L, et al. (2019) Challenges to curing primary brain tumors. Nature Reviews, Clinical oncology, 16(8): 509-520. [DOI:10.1038/s41571-019-0177-5] [PMID] []
2. Qi X, Jha SK, Jha NK, et al. (2022) Antioxidants in brain tumors: current therapeutic significance and future prospects. Mol Cancer, 21(204): 1-32. [DOI:10.1186/s12943-022-01668-9] [PMID] []
3. G Elshaikh BG, Omer H, Garelnabi MEM, et al. (2021) Incidence, diagnosis and treatment of brain tumours Buthayna. Journal of Research in Medical and Dental Science, 9(6): 340-347.
4. Cooper GM (2000) The Cell: A Molecular Approach. Sinauer Associates ,Sunderland ,Masschusetts.
5. Mohammed AA, Hamdan AN, Homoud AS (2019) Histopathological profile of brain tumors a 12-year retrospective study from Madinah, Saudi Arabia. Asian J Neurosurg, 14(4): 1106-1111. [DOI:10.4103/ajns.AJNS_185_19] [PMID] []
6. Bukhtoyarov O and Samarin D (2015) Pathogenesis of cancer: Cancer reparative trap. Journal of Cancer Therapy, 6: 399-412. [DOI:10.4236/jct.2015.65043]
7. Pichaivel M, Anbumani G, Theivendren P, Gopal M (2022) An overview of brain tumor. Brain tumors. Intechopen, 1(1): 1-212. [DOI:10.5772/intechopen.100806]
8. De Berardinis RJ and Chandel NS (2016) Fundamentals of cancer metabolism. Sci Adv, 2(5): 1-65. [DOI:10.1126/sciadv.1600200] [PMID] []
9. Shetty JK, Prasad KHL, Shruthi S (2022) Raghothaman a, challenges in the histopathologic diagnosis of brain tumors: An institutional experience in a series of cases. Journal of Health and Allied Sciences, 12(4): 412-416. [DOI:10.1055/s-0042-1742372]
10. Perkins A and Liu G (2016) Primary brain tumors in adults: Diagnosis and treatment. American Family Physician, 93(3): 1-9.
11. Weinberg BD, Kuruva M, Shim H, Mullins ME (2021) Clinical applications of magnetic resonance spectroscopy in brain tumors: From diagnosis to treatment. Radiologic clinics of North America, 59(3): 349-362. [DOI:10.1016/j.rcl.2021.01.004] [PMID] []
12. Franco P, Würtemberger U, Dacca K, et al. (2020) Spectroscopic prediction of brain tumors (SPORT): study protocol of a prospective imaging trial.BMC Med Imaging, 20(123): 1-7. [DOI:10.1186/s12880-020-00522-y] [PMID] []
13. Tognarelli JM, Dawood M, Shariff MI, et al. (2015) Magnetic resonance spectroscopy: Principles and techniques: Lessons for clinicians. J Clin Exp Hepatol, 5(4): 320-328. [DOI:10.1016/j.jceh.2015.10.006] [PMID] []
14. Onyambu CK, Wajihi MN, Odhiambo AO (2021) Clinical application of magnetic resonance spectroscopy in diagnosis of intracranial mass lesions. Radiology Research and Practice, 2021(1):1-10. [DOI:10.1155/2021/6673585]
15. Gujar SK , Maheshwari S, Björkman-Burtscher I, Sundgren PC (2005) Magnetic Resonance Spectroscopy. J Neuroophthalmol, 25(3): 217-26. [DOI:10.1097/01.wno.0000177307.21081.81] [PMID]
16. Ciurleo R, Di Lorenzo G, Bramanti P, Marino S (2014) Magnetic resonance spectroscopy: An in vivo molecular imaging biomarker for parkinson's disease? BioMed Research International, 2014(1): 1-10. [DOI:10.1155/2014/519816] [PMID] []
17. Marie SKN and Shinjo SMO (2011) Metabolism and brain cancer. Clinics, 66(S1): 33-43. [DOI:10.1590/S1807-59322011001300005] [PMID] []
18. Sarah L and Olaf A (2017) Pathology and pathogenesis of pituitary adenomas and other sellar lesions. Endotext (internet), 1-45.
19. Zheng X, Li S, Zhang WH, Yang H (2015) Metabolic abnormalities in pituitary adenoma patients: a novel therapeutic target and prognostic factor. Diabetes Metab Syndr Obes, 8: 357-361. [DOI:10.2147/DMSO.S86319] [PMID] []
20. Karki KT and Koirala SJ (2017) An unusual variant of cerebellopontine angle schwannoma in a young Nepalese girl journal of clinical & experimental pathology. Clin Exp Pathol, 7(2): c307.
21. xMcManaman JL, Zabaronick W, Schaack J, Orlicky DJ (2003) Lipid droplet targeting domains of adipophilin. Journal of Lipid Research, 44(4): 668-673. [DOI:10.1194/jlr.C200021-JLR200] [PMID]
22. Ashish V, Ishan K, Nimisha V, et al. (2016) Magnetic resonance spectroscopy Revisiting the biochemical and molecular milieu of brain tumors. BBA Clinical, 5: 170-178. [DOI:10.1016/j.bbacli.2016.04.002] [PMID] []
23. Akira H, Tomohiro K, Kei N, et al. (2019) Treatment strategies based on histological targets against invasive and resistant glioblastoma. Journal of Oncology, 2019: 1-10. [DOI:10.1155/2019/2964783] [PMID] []
24. Zhou W and Wahl D (2019). Metabolic abnormalities in glioblastoma and metabolic strategies to overcome treatment resistance. Cancers, 11(1231): 1-26. [DOI:10.3390/cancers11091231] [PMID] []
25. Catherine JL , Anh NT , Sarah ES , (2019) The pro-tumorigenic effects of metabolic alterations in glioblastoma including brain tumor initiating cells. BiochimBiophysActa Rev Cancer, 1869(2): 175-188. [DOI:10.1016/j.bbcan.2018.01.004] [PMID] []
26. Arunpreet SK, Mariam A, Arundeep K, Jonathan W (2016) Lactate levels with glioblastoma multiforme. Proceedings (Baylor University. Medical Center), 29(3): 313-314. [DOI:10.1080/08998280.2016.11929449] [PMID] []
27. Candece LG, Richard AP, Wei M, LiuAR (2010)The Pathobiology of Glioma Tumors. Pathol, 5: 33-50. [DOI:10.1146/annurev-pathol-121808-102109] [PMID] []
28. Strickland M and Stoll EA (2017) Metabolic reprogramming in glioma, a stoll, frontiers in cell and development. BiologyFront. Cell Dev Biol, 5(43): 1-32. [DOI:10.3389/fcell.2017.00043] [PMID] []
29. Baumann F, Leukel P, Doerfelt A, et al. (2009) Lactate promotes glioma migration by TGF-beta2-dependent regulation of matrix metalloproteinase-2. Neuro Onco, 11(4): 368-80. [DOI:10.1215/15228517-2008-106] [PMID] []
30. Pohchoo S, Vairavan N, Aditya TH, et al. (2018) Quantification and visualization of lipid landscape in glioma using in -and opposed-phase imaging. NeuroimageClinical, 20: 531-536. [DOI:10.1016/j.nicl.2018.08.003] [PMID] []
31. Grabovetskyi S (2015) Proton magnetic resonance spectroscopy of focal intracranial lesions: Role in clinical practice, J Cancer PrevCurr Res, 2(5): 1-16. [DOI:10.15406/jcpcr.2015.02.00052]
32. Malcolm TIM, Hodson DJ, Macintyre EA, Turner SD (2016) Challenging perspectives on the cellular origins of lymphoma. Open Biol, 6(9): 1-12. [DOI:10.1098/rsob.160232] [PMID] []
33. Ambrosio MR, Piccaluga PP, Ponzoni M, et al. (2012) The alteration of lipid metabolism in Burkitt lymphoma identifies a novel marker: Adipophilin. PloS one, 7(8): 1-7. [DOI:10.1371/journal.pone.0044315] [PMID] []
34. McManaman JL, Zabaronick W, Schaack J, Orlicky DJ (2003) Lipid droplet targeting domains of adipophilin. Journal of Lipid Research, 44(4): 668-673. [DOI:10.1194/jlr.C200021-JLR200] [PMID]
35. Alena H and Peter BB (2010) Imaging of Brain Tumors: MR spectroscopy and metabolic imaging neuroimaging. Clin N Am, 20(3): 293-310. [DOI:10.1016/j.nic.2010.04.003] [PMID] []
36. Ishida M, Fukami T, Nitta N, et al. (2013) Case report xanthomatous meningioma: a case report with review of the literature.Int J Clin Exp Pathol, 6(10): 2242-224.
37. Melike P and Arie P (2013) Neuropathology of brain metastasis.Surgical Neurological International, 4(4): S245-55. [DOI:10.4103/2152-7806.111302] [PMID] []
38. Xiangjian L, Xu Z, Can C, et al. (2018) The implications of signaling lipids in cancer metastasis. Experimental& Molecular Medicine, 50(9): 1-10. [DOI:10.1038/s12276-018-0150-x] [PMID] []
39. Pieter W, Martin van den B, Arie P (2015) Oligodendroglioma: pathology, molecular mechanisms, and markers. ActaNeuropathologica, 129(6): 809-827. [DOI:10.1007/s00401-015-1424-1] [PMID] []
40. Lina MA, Tommy BA, Soma G, et al. (2015) Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information. Metabolites, 5: 502-520. [DOI:10.3390/metabo5030502] [PMID] []
41. Koeller KK and Sandberg GD (2002) Cerebral intraventricular neoplasms: Radiologic-pathologic correlation. Radiographics, 22(6): 1473-505. [DOI:10.1148/rg.226025118] [PMID]
42. Marie SK and Shinjo SM (2011) Metabolism and brain cancer. Clinics (Sao Paulo), 66(1): 33-43. [DOI:10.1590/S1807-59322011001300005] [PMID] []
43. Oz G, Alger JR, Barker PB, et al. (2014) Clinical proton MR spectroscopy in central nervous system disorders. Radiology, 270(3): 658-79. [DOI:10.1148/radiol.13130531] [PMID] []
44. John MLA (2014) Chapter 41 - Tuberculosis of the Central Nervous System Neurology and General Medicine: Fifth Edition
45. Sandra B, Marie S, Claudia H, et al. (2017) Lactate oxidation facilitates the growth of Mycobacterium tuberculosis in human macrophages. Scientific Reports, 7(6484): 1-12. [DOI:10.1038/s41598-017-05916-7] [PMID] []
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Smitha H, Meena Devi V, Sreekanth K, Vinoo J. Predictive Biomarkers of Brain tumor Lesions through Correlation of Histopathological changes with Metabolites by Magnetic Resonance Spectroscopy. Int J Radiat Res 2023; 21 (4) :769-777
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Volume 21, Issue 4 (10-2023) Back to browse issues page
International Journal of Radiation Research
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