The Value of Diffusion-weighted Magnetic Resonance Imaging in the Preoperative Evaluation of the Grade of Brain Gliomas

Objective. To compare the values obtained for the measured diffusion coefficient (MDC) of brain gliomas with cell density and Ki-67 proliferative activity index and to study whether diffusion-weighted MRI (DWMRI) can be used in the preoperative evaluation of the grade of glial tumors.

Material and methods. Diffusion-weighted images of 39 patients with brain gliomas were studied. MDC, cell density, and Ki-67 proliferative activity index were calculated for each tumor. The correlation between MDC values, cell density, and Ki-67 proliferative activity index was analyzed.

Results. Comparison of the mean values for MDC revealed a significant difference between grades I–II and III–IV tumors. There were statistically significant differences in the mean Ki-67 index between different grades of gliomas. Evaluation of the correlation between MCD and Ki-67 proliferative activity index demonstrated moderate and strong inverse correlations for low- and high-grade tumors, respectively.

Conclusion. The procedure using DW-MRI along with MDC calculation can be used as an additional noninvasive method for the preoperative estimation of the grade and proliferative potential of brain gliomas.

1. Kleihues P., Cavenee W.K. Pathology and genetics of tumors of the nervous system. Lyon: International Agency for Research on Cancer; 2000.

2. Byval’tsev V.A., Stepanov I.A., Belykh E.G. Tumor stem cells as a source of brain gliomas. Sibirskiy Medicinskiy Zurnal (Siberian Medical Journal). 2015; 3: 5–9 (in Russ.).

3. Ostrom Q.T., Bauchet L., Davis F.G., Deltour I., Fisher J.L., Langer C.E. et al. The epidemiology of glioma in adults: a “state of the science” review. Neuro-Oncology. 2014; 16 (7): 896–913.

4. Lau D., Magill S.T., Aghi M.K. Molecularly targeted therapies for recurrent glioblastoma: current and future targets. Neurosurgical Focus. 2014; 37 (6): E15.

5. Weller M., Cloughesy T., Perry J.R., Wick W. Standards of care for treatment of recurrent glioblastoma – are we there yet? Neuro-Oncology. 2013; 15 (1): 4–27.

6. Von Neubeck C., Seidlitz A., Kitzler H.H., Beuthien-Baumann B., Krause M. Glioblastoma multiforme: emerging treatments and stratification markers beyond new drugs. Br. J. Radiol. 2015; 88 (1053): 20150354.

7. Theeler B.J., Gilbert M.R. Advances in the treatment of newly diagnosed glioblastoma. BMC Medicine. 2015; 13: 293.

8. Kalpathy-Cramer J., Gerstner E.R., Emblem K.E., Andronesi O., Rosen B. Advanced magnetic resonance imaging of the physical processes in human glioblastoma. Cancer Research. 2014; 74 (17): 4622–37.

9. Zhou J., Tryggestad E., Wen Z., Lal B., Zhou T., Grossman R. et al. Differentiation between glioma and radiation necrosis using molecular magnetic resonance imaging of endogenous proteins and peptides. Nature Medicine. 2011; 17 (1): 130–4.

10. Byvaltsev V.A., Stepanov I.A., Kalinin A.A., Shashkov K.V. Diffusion-weighted magnetic resonance tomography in the diagnosis of intervertebral disk degeneration. Biomedical Engineering. 2016; 50 (4): 253–6.

11. Chen S.D., Hou P.F., Lou L., Jin X., Wang T.H., Xu J.L. The correlation between MR diffusion-weighted imaging and pathological grades on glioma. Eur. Rev. Med. Pharmacol. Sci. 2014; 18: 1904–9.

12. Hu Y.C., Yan L.F., Sun Q., Liu Z.C., Wang S.M., Han Y. et al. Comparison between ultra-high and conventional mono bvalue DWI for preoperative glioma grading. Biomed. Res. Int. 2013; 15 (3): 330–40.

13. Young R.J., Gupta A., Shah A.D., Graber J.J., Schweitzer A.D., Prager A. et al. Potential role of preoperative conventional MRI including diffusion measurements in assessing epidermal growth factor receptor gene amplification status in patients with glioblastoma. AJNR Am. J. Neuroradiol. 2013; 34 (12): 2271–7.

14. Sanverdi S.E., Ozgen B., Oguz K.K., Mut M., Dolgun A., Soylemezoglu F. et al. Is diffusion-weighted imaging useful in grading and differentiating histopathological subtypes of meningiomas? Eur. J. Radiol. 2012; 81 (9): 2389–95.

15. Pavlisa G., Rados M., Pazanin L., Padovan R.S., Ozretic D., Pavlisa G. Characteristics of typical and atypical meningiomas on ADC maps with respect to schwannomas. Clin. Imaging. 2008; 32 (1): 22–7.

16. Wu X., Pertovaara H., Dastidar P., Vornanen M., Paavolainen L., Marjomäki V. et al. ADC measurements in diffuse large B-cell lymphoma and follicular lymphoma: a DWI and cellularity study. Eur. J. Radiol. 2013; 82 (4): 158–64.

17. Abercrombie M. Estimation of nuclear population from microtome sections. Anat. Rec. 1946; 94: 239–47.

18. Wong K., Young G.S., Makale M., Hu X., Yildirim N., Cui K. et al. Characterization of a human tumorsphere glioma orthotopic model using magnetic resonance imaging. J. NeuroOncology. 2011; 104 (2): 473–81.

19. De la Fuente M.I., Young R.J., Rubel J., Rosenblum M., Tisnado J., Briggs S. et al. Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma. Neuro-Oncology. 2016; 18 (2): 283–90.

20. He T., Qiu T., Wang X., Gui H., Wang X., Hu Q. et al. Multivoxel magnetic resonance spectroscopy identifies enriched foci of cancer stem-like cells in high-grade gliomas. OncoTarg. Ther. 2017; 10: 195–203.

21. Ranjith G., Parvathy R., Vikas V., Chandrasekharan K., Nair S. Machine learning methods for the classification of gliomas: initial results using features extracted from MR spectroscopy. Neuroradiol. J. 2015; 28 (2): 106–11.

22. Abhinav K., Yeh F-C., Mansouri A., Zadeh G., FernandezMiranda J.C. High-definition fiber tractography for the evaluation of perilesional white matter tracts in high-grade glioma surgery. Neuro-Oncology. 2015; 17 (9): 1199–209.

23. Farshidfar Z., Faeghi F., Mohseni M., Seddighi A., Kharrazi H.H., Abdolmohammadi J. Diffusion tensor tractography in the presurgical assessment of cerebral gliomas. Neuroradiol. J. 2014; 27 (1): 75–84.

24. Tonoyan A.S., Pronin I.N., Pitskhelauri D.I., Khachanova N.V., Fadeeva L.M., Pogosbekyan E.L. et al. Diffusion kurtosis imaging in diagnostics of brain glioma malignancy. Meditsinskaya Vizualizatsiya (Medical Visualisation). 2015; 1: 7–18 (in Russ.).

25. Serkov S.V., Pronin I.N., Fadeeva L.M., Golanov A.V., Rodionov P.V., Kornienko V.N. Diffusion weighted MRI in diagnostics of neoplasms of the posterior cranial fossa. Meditsinskaya Vizualizatsiya (Medical Visualisation). 2004; 2: 66–75 (in Russ.).

26. Hakyemez B., Yildirim N., Gokalp G., Erdogan C., Parlak M. The contribution of diffusion-weighted MR imaging to distinguishing typical from atypical meningiomas. Neuroradiology. 2006; 48 (8): 513–20.

27. Karaman A., Durur-Subasi I., Alper F., Araz O., Subasi M., Demirci E. et al. Correlation of diffusion MRI with the Ki-67 index in non-small cell lung cancer. Radiol. Oncol. 2015; 49 (3): 250–5.

28. Tang Y., Dundamadappa S.K., Thangasamy S., Flood T., Moser R., Smith T. et al. Correlation of apparent diffusion coefficient with Ki67 proliferation index in grading meningioma. Am. J. Roentgenol. 2014; 202 (6): 1303–8.

29. Ginat D.T., Mangla R., Yeaney G., Wang H.Z. Correlation of diffusion and perfusion MRI with Ki-67 in high-grade meningiomas. Am. J. Roentgenol. 2010; 195 (6): 1391–5.

30. Fatima Z., Motosugi U., Waqar A.B., Hori M., Ishigame K., Oishi N. et al. Associations among q-space MRI, diffusionweighted MRI and histopathological parameters in meningiomas. Eur. Radiol. 2013; 23 (8): 2258–63.

31. Barajas R.F. Jr., Phillips J.J, Parvataneni R., Molinaro A., Essock-Burns E., Bourne G. et al. Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR imaging. Neuro-Oncology. 2012; 14 (7): 942–54.

32. Pronin I.N., Tonoyan A.S., Shul’ts E.I., Fadeeva L.M., Zakharova N.E., Goryaynov S.A. et al. Diffusion kurtosis MRI in assesment of Ki-67/MIB-1 LI in gliomas. Meditsinskaya Vizualizatsiya (Medical Visualisation). 2016; 5: 6–17 (in Russ.).

33. Castro B.A., Aghi M.K. Bevacizumab for glioblastoma: current indications, surgical implications, and future directions. Neurosurgical Focus. 2014; 37 (6): E9.

34. Esteves S., Alves M., Castel-Branco M., Stummer W. A pilot cost-effectiveness analysis of treatments in newly diagnosed high-grade gliomas: the example of 5-aminolevulinic acid compared with white-light surgery. Neurosurgery. 2015; 76 (5): 552–62.