Accuracy of magnetic resonance imaging diagnosis and grading of gliomas


  • Dhanwantari Shukla Department of Neurosurgey, The Neuro City Hospital, Varanasi, Uttar Pradesh, India
  • Abhijeet R. Chandankhede Department of Neurosurgey, Jawaharlal Nehru Medical College, Sawangi, Wardha, Maharashtra, India
  • Prafulla K. Sahoo Department of Neurosurgey, Apollo Hospitals, Bhubaneswar, Odisha, India



Pre-operative MRI, Gliomas, Histopathological correlation, Sensitivity, Specificity, Diagnostic accuracy, Prediction


Background: Low- and high-grade gliomas differ in clinical presentation, natural history, treatment outcome, prognosis, survival pattern, histopathological, immunohistochemical and biomolecular profiles. Accurate pre-operative prediction of histopathological grade of gliomas remains challenging, and is critical for making optimum management plan and prognosticating the disease beforehand to determine the most cost-effective therapeutic choice with the best patient outcome. This prospective observational study on 54 patients aims to determine accuracy of pre-operative magnetic resonance imaging (MRI) in diagnosing and grading gliomas.

Methods: Pre-operative grading of MRI-suspected gliomas was done by assigning scores of 0-2 to 9 criteria – midline crossing, perilesional edema, signal heterogeneity, intra-tumoral hemorrhage, tumor border definition, cystic/necrotic changes, mass effect, contrast enhancement and diffusion restriction. Total scores of 0-5, 6-9 and 10-18 were considered radiologically low, intermediate and high grades respectively and correlation with World Health Organization (WHO) grades I+II, III and IV respectively was determined.

Results: MRI diagnosed 85.18% gliomas correctly. Pre-operative MR grading was 76-89% sensitive and 86-96% specific in predicting the histopathological grade of the gliomas. Signal heterogeneity and contrast enhancement had the highest whereas midline crossing and mass effect had the lowest correlation with histopathological grade.

Conclusions: Pre-operative MRI is highly specific and somewhat less sensitive tool for grading gliomas pre-operatively. The diagnostic yield is higher for LGGs and GBMs, compared to anaplastic gliomas, probably due to their mixed or intermediate features.



Schwartzbaum JA, Fisher JL, Aldape KD, Wrensch. Epidemiology and molecular pathology of glioma. Nature. 2006;2:494-503.

Louis DN, Ohgaki H, Wiestler OD, Cavenee WK. World Health Organization. Histological Classification of Tumours of the Central Nervous System. International Agency for Research on Cancer, France. 2016.

Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathologica. 2007;114:97-109.

Ohgaki H, Kleihues P. Epidemiology and etiology of gliomas. Acta Neuropathologica. 2005;109:93-108.

Bydder GM, Steiner RE, Young IR. Clinical NMR imaging of the brain: 140 cases. AJR Am J Roentgenol. 1982;139:215-36.

Just M, Thelen M. Tissue characterization with T1, T2, and proton density values: results in 160 patients with brain tumors. Radiology. 1988;169:779-85.

Law M, Yang S, Wang H, Babb JS, Johnson G, Cha S, et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. Am J Neuroradiol. 2003;24:1989-98.

Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, Wakasa K, Yamada R. The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol. 2001;22(6):1081-8.

Stall B, Zach L, Ning H, Ondos J, Arora B, Shankavaram U, Miller RW, Citrin D, Camphausen K. Comparison of T2 and FLAIR imaging for target delineation in high grade gliomas. Radiat Oncol. 2010;5:5.

Dean BL, Drayer BP, Bird CR. Gliomas: classification with MR imaging. Radiology. 1990;174:411-5.

Chishty IA, Rafique MZ, Hussain M, Akhtar W, Ahmed MN, Sajjad Z, Ali SZ. MRI characterization and histopathological correlation of primary intra-axial brain glioma. J Liaquat Uni Med Health Sci. 2010;9(2):64-9.

Suárez-García JG, Hernández-López JM, Moreno-Barbosa E, de Celis-Alonso B. A simple model for glioma grading based on texture analysis applied to conventional brain MRI. BioRxiv. 2020.

Ostrom QT, Bauchet L, Davis FG. The epidemiology of glioma in adults: a “state of the science” review. Neuro Oncol. 2014;16(7):896-913.

Consolidated Report of Hospital Based Cancer Registries 2004-2006: National Cancer Registry Programme (ICMR), Bangalore. 2009.

Dasgupta A, Gupta T, Jalali R. Indian data on central nervous tumors: A summary of published work. South Asian J Cancer. 2016;5:147-53.

Mukherjee R, Das TV, Roy K, Mukherjee J. Current Understanding of Epidemiology, Genetic Etiology and Treatment of Gliomas from Indian Population. Chemo Open Access. 2016;5:203.

Consolidated Report of Hospital Based Cancer Registries 2012-2016: National Cancer Registry Programme (ICMR), Bangalore, 2020.

Castillo M. History and evolution of brain tumor imaging: Insights through radiology. Radiology. 2014;273:111-25.

Felix R, Schörner W, Laniado M. Brain tumors: MR imaging with gadolinium-DTPA. Radiology. 1985;156(3):681-8.

Sanghvi DA. Recent advances in imaging of brain tumors. Indian J Cancer. 2009;46(2):82-7.

Upadhyay N, Waldman AD. Conventional MRI evaluation of gliomas. Br J Radiol. 2011;84:S107-11.

Earnest F, Kelly PJ, Scheithauer BW, Kall BA, Cascino TL, Ehman RL, et al. Cerebral astrocytomas: histopathologic correlation of MR and CT contrast enhancement with stereotactic biopsy. Radiology. 1988;166:823-7.

Tien RD, Felsberg GJ, Friedman H, Brown M, MacFall J. MR imaging of high-grade cerebral gliomas: Value of diffusion-weighted echoplanar pulse sequences. Am J Roentgenol. 1994;162:671-7.

Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, Wakasa K, Yamada R. The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol. 2001;22(6):1081-8.

Guo AC, Cummings TJ, Dash RC, Provenzale JM. Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology. 2002;224(1):177-83.

Hakyemez B, Erdogan C, Ercan I, Ergin N, Uysal S, Atahan S. High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol. 2005;60(4):493-502.

Zidan S, Tantawy HI, Makia MA. High grade gliomas: The role of dynamic contrast-enhanced susceptibility-weighted perfusion MRI and proton MR spectroscopic imaging in differentiating grade III from grade IV. Egypt. J Radiol Nucl Med. 2016;47(4):1565-73.

Aronen HJ, Gazit IE, Louis DN, Buchbinder BR, Pardo FS, Weisskoff RM, Harsh GR, Cosgrove GR, Halpern EF, Hochberg FH, et al. Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology. 1994;191(1):41-51.

Hartmann M, Heiland S, Harting I, Tronnier VM, Sommer C, Ludwig R, et al. Distinguishing of primary cerebral lymphoma from high-grade gliomas with perfusion-weighted magnetic resonance imaging. Neurosci Lett. 2003;338:119-22.

Law M, Young RJ, Babb JS. 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):490-8.

Zhang J, Liu H, Tong H, Wang S, Yang Y, Liu G, Zhang W. Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges. Contrast Media Mol Imaging. 2017;7064120.

Guzmán-De-Villoria JA, Mateos-Pérez JM, Fernández-García P, Castro E, Desco M. Added value of advanced over conventional magnetic resonance imaging in grading gliomas and other primary brain tumors. Cancer Imaging. 2014;14(1):35.

Goebell E, Paustenbach S, Vaeterlein O. Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor MR imaging. Radiology. 2006;239(1):217-22.

Geneidi EAS, Habib LA, Chalabi NA, Haschim MH. Potential role of quantitative MRI assessment in differentiating high from low-grade gliomas. Egypt J Radiol Nucl Med. 2016;47(1):243-53.

Hall WA, Martin A, Liu H, Truwit CL. Improving diagnostic yield in brain biopsy: Coupling spectroscopic targeting with real-time needle placement. J Magn Reson Imaging. 2001;13:12-5.

Senft C, Hattingen E, Pilatus U. Diagnostic value of proton magnetic resonance spectroscopy in the noninvasive grading of solid gliomas: comparison of maximum and mean choline values. Neurosurgery. 2009;65(5):908-13.

Howe FA, Barton SJ, Cudlip SA. Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med. 2003;49(2):223-32.

Caulo M, Panara V, Tortora D. Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology. 2014;272(2):494-503.

Nakamoto T, Takahashi W, Haga A, Takahashi S, Kiryu S, Nawa K, Ohta T, Ozaki S, Nozawa Y, Tanaka S, Mukasa A, Nakagawa K. Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis. Sci Rep. 2019;9(1):19411.

Chang SM, Parney IF, McDermott M, Barker FG 2nd, Schmidt MH, Huang W, Laws ER Jr, Lillehei KO, Bernstein M, Brem H, Sloan AE, Berger M; Glioma Outcomes Investigators. Perioperative complications and neurological outcomes of first and second craniotomies among patients enrolled in the Glioma Outcome Project. J Neurosurg. 2003;98(6):1175-81.






Original Research Articles