Grading of oligodendroglial tumors of the brain with apparent diffusion coefficient,magnetic resonance spectroscopy,and dynamic susceptibility contrast imaging

Abstract

Purpose

We explored whether advanced magnetic resonance (MR) imaging techniques could grade oligodendrogliomas.

Methods

Forty patients (age 9–61 years) with oligodendroglial tumors were selected. There were 23 patients with World Health Organization grade II (group 1) and 17 patients with grade III (group 2) tumors. Apparent diffusion coefficient (ADC) maps were calculated by b values of 0 and 1000 s/mm². Dynamic susceptibility contrast (DSC) images were obtained during the first pass of a bolus of gadolinium-based contrast. These data were post-processed and cerebral blood volume (CBV) maps and permeability (PS) were calculated. MR spectroscopy was acquired after drawing a region of interest on the tumor using two-dimensional chemical shift imaging. Statistical analysis was performed using SPSS software.

Results

When the rPS_max was combined with the rCBV_max, there was a significant difference between the two groups (p ≤ 0.03) with area under the curve of 0.742 (95% CI: 0.412–0.904). rCBV, rADC, choline/creatine, and choline/NAA alone were able to differentiate between the two groups; however, they did not show any statistical difference with p values of ≤ 0.121, ≤ 0.722, and ≤ 0.582, respectively. A CBV PS product threshold of 0.53 provided a sensitivity of 80% and a specificity of 83.3% in detection of grade III tumors.

Conclusion

Combined rCBV_max and rPS_max can be utilized to grade oligodendrogliomas. ADC values, relative cerebral blood volume (rCBV), and MR spectroscopy alone can be utilized to differentiate between the two groups of oligodendrogliomas but without statistical significance.

Keywords

Apparent diffusion coefficient cerebral blood volume time to echo

Get full access to this article

View all access options for this article.

References

Claus

Black

. Survival rates and patterns of care for patients diagnosed with supratentorial low-grade gliomas. Cancer 2006; 106: 1358–1363.

McCarthy

Propp

Davis

et al.

Time trends in oligodendroglial and astrocytic tumor incidence. Neuroepidemiology 2008; 30: 34–44.

Louis

Perry

Burger

et al.

International Society of Neuropathology–Haarlem consensus guidelines for nervous system tumor classification and grading. Brain Pathol 2014; 24: 429–435.

Lebrun

Fontaine

Ramaioli

et al.

Long-term outcome of oligodendrogliomas. Neurology 2004; 62: 1783–1787.

Koeller

Rushing

. Oligodendroglioma and its variants: Radiologic-pathologic correlation. Radiographics 2005; 25: 1669–1688.

van den Bent

. Anaplastic oligodendroglioma and oligoastrocytoma. Neurol Clin 2007; 25: 1089–1109.

Ding

Ling

Chen

et al.

Comparison of cerebral blood volume and permeability in preoperative grading of intracranial glioma using CT perfusion imaging. Neuroradiology 2006; 48: 773–781.

Khalid

Carone

Dumrongpisutikul

et al.

Imaging characteristics of oligodendrogliomas that predict grade. AJNR Am J Neuroradiol 2012; 33: 852–857.

Spampinato

Smith

Kwock

et al.

Cerebral blood volume measurements and proton MR spectroscopy in grading of oligodendroglial tumors. AJR Am J Roentgenol 2007; 188: 204–212.

10.

Whitmore

Krejza

Kapoor

et al.

Prediction of oligodendroglial tumor subtype and grade using perfusion weighted magnetic resonance imaging. J Neurosurg 2007; 107: 600–609.

11.

Jansen

Schwartz

Graute

et al.

Prediction of oligodendroglial histology and LOH 1p/19q using dynamic [18F] FET-PET imaging in intracranial WHO grade II and III gliomas. Neuro Oncol 2012; 14: 1473–1480.

12.

Ginat

Mangla

Yeaney

et al.

Diffusion-weighted imaging for differentiating benign from malignant skull lesions and correlation with cell density. AJR Am J Roentgenol 2012; 198: W597–W601.

13.

Haselhorst

Kappos

Bilecen

et al.

Dynamic susceptibility contrast MR imaging of plaque development in multiple sclerosis: Application of an extended blood-brain barrier leakage correction. J Magn Reson Imaging 2000; 11: 495–505.

14.

Mangla

Singh

Ziegelitz

et al.

Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma. Radiology 2010; 256: 575–584.

15.

Weisskoff RM, Boxerman JL, Sorensen AG, et al. Simultaneous blood volume and permeability mapping using a single Gd-based contrast injection. In: Proceedings of the second annual meeting of the Society of Magnetic Resonance, San Francisco, CA, USA, 6 August 1994, pp.6-12.

16.

Law

Yang

Wang

et al.

Glioma grading: Sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 2003; 24: 1989–1998.

17.

Burger

. Tumors of the central nervous system, Washington, DC: American Registry of Pathology, 2007.

18.

Goyal

Kumar

Gupta

et al.

Usefulness of enhancement-perfusion mismatch in differentiation of CNS lymphomas from other enhancing malignant tumors of the brain. Quant Imaging Med Surg 2017; 7: 511–519.

19.

Jain

Griffith

Alotaibi

et al.

Glioma angiogenesis and perfusion imaging: Understanding the relationship between tumor blood volume and leakiness with increasing glioma grade. AJNR Am J Neuroradiol 2015; 36: 2030–2035.

20.

Lev

Ozsunar

Henson

et al.

Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: Confounding effect of elevated rCBV of oligodendroglimoas. AJNR Am J Neuroradiol 2004; 25: 214–221.

21.

Osborn

. Osborn’s brain imaging pathology anatomy, Salt Lake City, UT: Amirsys Inc, 2012.

22.

Al-Okaili

Krejza

Wang

et al.

Advanced MR imaging techniques in the diagnosis of intraaxial brain tumors in adults. Radiographics 2006; 26(Suppl 1): S173–S189.

23.

Young

. Advanced MRI of adult brain tumors. Neurol Clin 2007; 25: 947–973.

24.

See

et al.

Comparison of magnetic resonance spectroscopy and perfusion-weighted imaging in presurgical grading of oligodendroglial tumors. Neurosurgery 2005; 56: 919–926.

25.

Fellah

Caudal

De Paula

et al.

Multimodal MR imaging (diffusion, perfusion, and spectroscopy): Is it possible to distinguish oligodendroglial tumor grade and 1p/19q codeletion in the pretherapeutic diagnosis?

AJNR Am J Neuroradiol 2013; 34: 1326–1333.

26.

Chawla

Wang

Wolf

et al.

Arterial spin-labeling and MR spectroscopy in the differentiation of gliomas. AJNR Am J Neuroradiol 2007; 28: 1683–1689.

27.

White

Zhang

Kirby

et al.

Can tumor contrast enhancement be used as a criterion for differentiating tumor grades of oligodendrogliomas?

AJNR Am J Neuroradiol 2005; 26: 784–790.

28.

Jenkinson

du Plessis

Smith

et al.

Histological growth patterns and genotype in oligodendroglial tumours: Correlation with MRI features. Brain Engl 2006; 129(Pt 7): 1884–1891.

29.

Megyesi

Kachur

Lee

et al.

Imaging correlates of molecular signatures in oligodendrogliomas. Clin Cancer Res 2004; 10: 4303–4306.

30.

Chawla

Krejza

Vossough

et al.

Differentiation between oligodendroglioma genotypes using dynamic susceptibility contrast perfusion-weighted imaging and proton MR spectroscopy. AJNR Am J Neuroradiol 2013; 34: 1542–1549.

31.

Kapoor

Gocke

Chawla

et al.

Magnetic resonance perfusion-weighted imaging defines angiogenic subtypes of oligodendroglioma according to 1p19q and EGFR status. J Neurooncol 2009; 92(3): 373–386.