Vol 71, No 5 (2021)
Review paper
Published online: 2021-10-13

open access

Page views 6294
Article views/downloads 355
Get Citation

Connect on Social Media

Connect on Social Media

Significance of genetic and radiological examinations in diagnosis and therapy of brain glioma in adult patients

Gabriela Janus-Szymańska12, Łukasz Waszczuk3, Jagoda Jacków-Nowicka3
Nowotwory. Journal of Oncology 2021;71(5):328-334.


Molecular and imaging studies are applied along with histopathology in diagnosis and differential diagnosis of brain gliomas and they enable personalised clinical management. With knowledge of the patient’s clinical condition, a decision whether to observe the patient or proceed to immediate surgical treatment is made based on imaging results. On the other hand, knowledge of molecular predictive markers allows optimisation of chemotherapeutic decisions, e.g., introduction of personalised therapy (application of such drugs as temozolomide, bevacizumab, vemurafenib, dabrafenib and trametinib).

Article available in PDF format

View PDF Download PDF file


  1. Stupp R, Brada M, van den Bent MJ, et al. ESMO Guidelines Working Group. High-grade glioma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2014; 25 Suppl 3: iii93–ii101.
  2. Okła K, Wawruszak A, Bilska S. Gliomas – epidemiology, classification and etiology. Review and Research on Cancer Treatment. 2015; 1(1).
  3. McLean R, Lovely M, Vassall E. National Brain Tumor Society. 2004, 2005, 2007, 2009, 2010, 2012.
  4. Greenberg MS. Handbook of Neurosurgery; Description: 9th edition. Thieme, New York 2020.
  5. Verhaak R, Hoadley K, Purdom E, et al. Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010; 17(1): 98–110.
  6. Kan LK, Drummond K, Hunn M, et al. Potential biomarkers and challenges in glioma diagnosis, therapy and prognosis. BMJ Neurol Open. 2020; 2(2): e000069.
  7. Verhaak RGW, Hoadley KA, Purdom E, et al. Cancer Genome Atlas Research Network. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010; 17(1): 98–110.
  8. Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016; 131(6): 803–820.
  9. Wesseling P, Capper D. WHO 2016 Classification of gliomas. Neuropathol Appl Neurobiol. 2018; 44(2): 139–150.
  10. Tan AC, Ashley DM, López GY, et al. Management of glioblastoma: State of the art and future directions. CA Cancer J Clin. 2020; 70(4): 299–312.
  11. Lundy P, Domino J, Ryken T, et al. The role of imaging for the management of newly diagnosed glioblastoma in adults: a systematic review and evidence-based clinical practice guideline update. J Neurooncol. 2020; 150(2): 95–120.
  12. Fouke SJ, Benzinger T, Gibson D, et al. The role of imaging in the management of adults with diffuse low grade glioma: A systematic review and evidence-based clinical practice guideline. J Neurooncol. 2015; 125(3): 457–479.
  13. la Fougère C, Suchorska B, Bartenstein P, et al. Molecular imaging of gliomas with PET: opportunities and limitations. Neuro Oncol. 2011; 13(8): 806–819.
  14. Rapp M, Heinzel A, Galldiks N, et al. Diagnostic performance of 18F-FET PET in newly diagnosed cerebral lesions suggestive of glioma. J Nucl Med. 2013; 54(2): 229–235.
  15. Pyatigorskaya N, Sgard B, Bertaux M, et al. Can FDG-PET/MR help to overcome limitations of sequential MRI and PET-FDG for differential diagnosis between recurrence/progression and radionecrosis of high-grade gliomas? J Neuroradiol. 2021; 48(3): 189–194.
  16. Ellingson BM, Bendszus M, Boxerman J, et al. Jumpstarting Brain Tumor Drug Development Coalition Imaging Standardization Steering Committee. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol. 2015; 17(9): 1188–1198.
  17. Elhawary H, Liu H, Patel P, et al. Intraoperative real-time querying of white matter tracts during frameless stereotactic neuronavigation. Neurosurgery. 2011; 68(2): 506–16; discussion 516.
  18. Saconn PA, Shaw EG, Chan MD, et al. Use of 3.0-T MRI for stereotactic radiosurgery planning for treatment of brain metastases: a single-institution retrospective review. Int J Radiat Oncol Biol Phys. 2010; 78(4): 1142–1146.
  19. Omuro A, DeAngelis LM. Glioblastoma and other malignant gliomas: a clinical review. JAMA. 2013; 310(17): 1842–1850.
  20. Wang YY, Wang K, Li SW, et al. Patterns of Tumor Contrast Enhancement Predict the Prognosis of Anaplastic Gliomas with IDH1 Mutation. AJNR Am J Neuroradiol. 2015; 36(11): 2023–2029.
  21. Liu X, Almast J, Ekholm S. Lesions masquerading as acute stroke. J Magn Reson Imaging. 2013; 37(1): 15–34.
  22. Anderson MD, Colen RR, Tremont-Lukats IW. Imaging mimics of primary malignant tumors of the central nervous system (CNS). Curr Oncol Rep. 2014; 16(8): 399.
  23. Auffray-Calvier E, Toulgoat F, Daumas-Duport B, et al. Infectious and metabolic brain imaging. Diagn Interv Imaging. 2012; 93(12): 911–934.
  24. Neska-Matuszewska M, Bladowska J, Sąsiadek M, et al. Differentiation of glioblastoma multiforme, metastases and primary central nervous system lymphomas using multiparametric perfusion and diffusion MR imaging of a tumor core and a peritumoral zone-Searching for a practical approach. PLoS One. 2018; 13(1): e0191341.
  25. Hakyemez B, Erdogan C, Gokalp G, et al. Solitary metastases and high-grade gliomas: radiological differentiation by morphometric analysis and perfusion-weighted MRI. Clin Radiol. 2010; 65(1): 15–20.
  26. Osborn A. Extra-Axial Neoplasms, Cysts and Tumor-Like Lesions. Radiologic-Pathologic Correlations from Head to Toe. 2005: 27–33.
  27. White ML, Zhang Y, Kirby P, et al. Can tumor contrast enhancement be used as a criterion for differentiating tumor grades of oligodendrogliomas? AJNR Am J Neuroradiol. 2005; 26(4): 784–790.
  28. Lasocki A, Anjari M, Ӧrs Kokurcan S, et al. Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review. Neuroradiology. 2021; 63(3): 353–362.
  29. Pope WB, Sayre J, Perlina A, et al. MR imaging correlates of survival in patients with high-grade gliomas. AJNR Am J Neuroradiol. 2005; 26(10): 2466–2474.
  30. Smits M. Imaging of oligodendroglioma. Br J Radiol. 2016; 89(1060): 20150857.
  31. Yuh EL, Barkovich AJ, Gupta N. Imaging of ependymomas: MRI and CT. Childs Nerv Syst. 2009; 25(10): 1203–1213.
  32. Abrigo JM, Fountain DM, Provenzale JM, et al. Magnetic resonance perfusion for differentiating low-grade from high-grade gliomas at first presentation. Cochrane Database Syst Rev. 2018; 1: CD011551.
  33. Afra D, Osztie E, Sipos L, et al. Preoperative history and postoperative survival of supratentorial low-grade astrocytomas. Br J Neurosurg. 1999; 13(3): 299–305.
  34. Xiao HF, Chen ZY, Lou X, et al. Astrocytic tumour grading: a comparative study of three-dimensional pseudocontinuous arterial spin labelling, dynamic susceptibility contrast-enhanced perfusion-weighted imaging, and diffusion-weighted imaging. Eur Radiol. 2015; 25(12): 3423–3430.
  35. Zhang B, MacFadden D, Damyanovich AZ, et al. Development of a geometrically accurate imaging protocol at 3 Tesla MRI for stereotactic radiosurgery treatment planning. Phys Med Biol. 2010; 55(22): 6601–6615.
  36. Smirniotopoulos JG, Murphy FM, Rushing EJ, et al. Patterns of contrast enhancement in the brain and meninges. Radiographics. 2007; 27(2): 525–551.
  37. Law M, Young RJ, Babb JS, et al. Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology. 2008; 247(2): 490–498.
  38. Villanueva-Meyer J, Mabray M, Cha S. Current Clinical Brain Tumor Imaging. Neurosurgery. 2017; 81(3): 397–415.
  39. Griffith B, Jain R. Perfusion Imaging in Neuro-Oncology. Radiol Clin North Am. 2015; 53(3): 497–511.
  40. Castillo M, Smith JK, Kwock L. Correlation of myo-inositol levels and grading of cerebral astrocytomas. AJNR Am J Neuroradiol. 2000; 21(9): 1645–1649.
  41. Shahar T, Rozovski U, Marko NF, et al. Preoperative imaging to predict intraoperative changes in tumor-to-corticospinal tract distance: an analysis of 45 cases using high-field intraoperative magnetic resonance imaging. Neurosurgery. 2014; 75(1): 23–30.
  42. Ozdemir-Kaynak E, Qutub AA, Yesil-Celiktas O. Advances in Glioblastoma Multiforme Treatment: New Models for Nanoparticle Therapy. Front Physiol. 2018; 9: 170.
  43. Shergalis A, Bankhead A, Luesakul U, et al. Current Challenges and Opportunities in Treating Glioblastoma. Pharmacol Rev. 2018; 70(3): 412–445.
  44. Egaña L, Auzmendi-Iriarte J, Andermatten J, et al. Methylation of MGMT promoter does not predict response to temozolomide in patients with glioblastoma in Donostia Hospital. Sci Rep. 2020; 10(1): 18445.
  45. Shboul ZA, Chen J, M Iftekharuddin K. Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features. Sci Rep. 2020; 10(1): 3711.
  46. Park YW, Han K, Ahn SS, et al. Prediction of -Mutation and 1p/19q-Codeletion Status Using Preoperative MR Imaging Phenotypes in Lower Grade Gliomas. AJNR Am J Neuroradiol. 2018; 39(1): 37–42.
  47. Park YW, Han K, Ahn SS, et al. Whole-Tumor Histogram and Texture Analyses of DTI for Evaluation of -Mutation and 1p/19q-Codeletion Status in World Health Organization Grade II Gliomas. AJNR Am J Neuroradiol. 2018; 39(4): 693–698.
  48. Natsumeda M, Motohashi K, Igarashi 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(2): 641–647.
  49. Gupta A, Young RJ, Shah AD, et al. Pretreatment Dynamic Susceptibility Contrast MRI Perfusion in Glioblastoma: Prediction of EGFR Gene Amplification. Clin Neuroradiol. 2015; 25(2): 143–150.

Nowotwory. Journal of Oncology