Diffusionkurtosis MRI in assesment of Ki-67/MIB-1 LI in Gliomas

Purpose: to assess correlation between Ki-67/MIB-1 LI and WHO grade brain gliomas and parameters of diffusion kurtosis MRI (DK-MRI) in the tumor.

Patients and methods. The study includes 84 patients with supratentorial brain gliomas (35 gliomas with low grade malignancy, 20 gliomas with the 3-rd grade and 29 gliomas with the 4-th grade of malignancy). The study appraised correlation links between absolute and normalized parameters of diffusion tensor (mean, axial and radial (MD, AD, RD), fractional and relative anisotropy (FA and RA) and diffusion kurtosis (mean, axial and radial (MK, AK, RK), kurtosis anisotropy (KA)) with Ki-67/MIB-1 LI and WHO glioma grade in the most malignant regions (p < 0.05, Spirman coefficient).

Results. DK-MRI parameters showed statistically significant correlation with Ki-67/MIB-1 LI and WHO glioma grades. Presence of oligodendroglioma (ODG) component in gliomas and oligoastrocytomas (OASs) did not affect the correlation between DK-MRI parameters and Ki-67/MIB-1 LI. However it affected correlation between DK-MRI parameters and WHO glioma grades. When studying correlation between parameters of DK-MRI and Ki-67/MIB-1 LI in IV grade gliomas maximum correlation was detected in case of normalised kurtosis anisotropy (KA).

Conclusion. DK-MRI proved high sensitivity in detecting structural changes in gliomas, which are observed when WHO grade and Ki-67/MIB-1 LI tumors change. DK-MRI parameters depend on WHO grade and Ki-67/MIB-1 LI gliomas. Presence of oligodendroglioma component in gliomas does not affect the correlation between DK-MRI parameters and Ki-67/MIB-1 LI, but affect the correlation between DK-MRI parameters and WHO glioma grade. Complex analysis of DK-MRI parameters in gliomas with due account for WHO glioma grade, Ki-67/MIB-1 LI and presence of oligodendroglioma component in the tumor carried out in our study made it possible to study in depth the dynamics of DK-MRI parameters during various pathological processes developing in the tumor.

1. Louis D.N., Ohgaki H., Wiestler O.D. et al. The 2016 WHO classification of tumours of the central nervous system: a summery. Acta Neuropathol. 2016; 131: 803–820.

2. Quinones-Hinojosa A., Sanai N., Smith J.S. et al. Techniques to assess the proliferative potential of brain tumors. J. Neurooncol. 2005; 74 (1): 19–30.

3. Prayson R.A. The utility of MIB-1/Ki-67 immunostaining in the evaluation of central nervous system neoplasms. Adv. Anat. Pathol. 2005; 12 (3): 144–148.

4. McKeever P.E., Ross D.A., Strawderman M.S. et al. A comparison of the predictive power for survival in gliomas provided by MIB-1, bromodeoxyuridine and proliferating cell nuclear antigen with histopathologic and clinical parameters. J. Neuropathol. Exp. Neurol. 1997; 56 (7): 798–805.

5. Hoshino T., Ahn D., Prados M.D. et al. Prognostic significance of the proliferative potential of intracranial gliomas measured by bromodeoxyuridine labeling. Int. J. Cancer. 1993; 53 (4): 550–555.

6. Wakimoto H., Aoyagi M., Nakayama T. et al. Prognostic significance of Ki-67 labeling indices obtained using MIB-1 monoclonal antibody in patients with supratentorial astrocytomas. Cancer. 1996; 77 (2): 373–380.

7. Johannessen A.L., Torp S.H. The clinical value of Ki-67/MIB-1 labeling index in human astrocytomas. Pathol. Oncol. Res. 2006; 12 (3): 143–147.

8. Saksena S., Jain R., Narang J. et al. Predicting survival in glioblastomas using diffusion tensor imaging metrics. J. Magn. Reson. Imaging. 2010; 32 (4): 788–795.

9. Sallinen P.K., Haapasalo H.K., Visakorpi T. et al. Prognostication of astrocytoma patient survival by Ki-67 (MIB-1), PCNA, and S-phase fraction using archival paraffinembedded samples. J. Pathol. 1994; 174 (4): 275–282.

10. Tynninen O., Aronen H. J., Ruhala M. et al. MRI enhancement and microvascular density in gliomas. Correlation with tumor cell proliferation. Invest. Radiol. 1999; 34 (6): 427–434.

11. Raab P., Hattingen E., Franz K. et al. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology. 2010; 254 (3): 876–881.

12. Van Cauter S., Veraart J., Sijbers J. et al. Gliomas: diffusion kurtosis MR imaging in grading. Radiology. 2012; 263 (2): 492–501.

13. Van Cauter S., Keyzer F., Sima D.M. et al. Integrating diffusion kurtosis imaging, dynamic susceptibilityweighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro Oncol. 2014; 16 (7): 1010–1021.

14. Тоноян А. С., Пронин И. Н., Пицхелаури Д. И. и др. Диффузионно-куртозисная МРТ в диагностике злокачественности глиом головного мозга. Медицинская визуализация. 2015; 1: 7–18 Tonoyan A.S., Pronin I.N., Pitskhelauri D.I. et al. Diffusion kurtosis imaging in diagnostics of brain glioma malignancy. Meditsinskaya vizualizatsiya. 2015; 1: 7–18. (In Russian)

15. Tietze A., Hansen M.B., Ostergaard L. et al. Mean Diffusional Kurtosis in Patients with Glioma: Initial Results with a Fast Imaging Method in a Clinical Setting. Am. J. Neuroradiol. 2015; 36 (8): 1472–1478.

16. Beppu T., Inoue T., Shibata Y. et al. Measurement of fractional anisotropy using diffusion tensor MRI in supratentorial astrocytic tumors. J. Neurooncol. 2003; 63 (2): 109–116.

17. Inoue T., Ogasawara K., Beppu T. et al. Diffusion tensor imaging for preoperative evaluation of tumor grade in gliomas. Clin. Neurol. Neurosurg. 2005; 107 (3): 174–180.

18. Higano S., Yun X., Kumabe T. et al. Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology. 2006; 241 (3): 839–846.

19. Stadlbauer A., Ganslandt O., Buslei R. et al. Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology. 2006; 240 (3): 803–810.

20. Lee E.J., Lee S.K., Agid R. et al. Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. Am. J. Neuroradiol. 2008; 29 (10): 1872–1877.

21. Kinoshita M., Hashimoto N., Goto T. et al. Fractional anisotropy and tumor cell density of the tumor core show positive correlation in diffusion tensor magnetic resonance imaging of malignant brain tumors. Neuroimage. 2008; 43 (1): 29–35.

22. Yin Y., Tong D., Liu X. et al. Correlation of apparent diffusion coefficient with Ki-67 in the diagnosis of gliomas. Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2012; 34 (5): 503–508.

23. Fudaba H., Shimomura T., Abe T. et al. Comparison of multiple parameters obtained on 3T pulsed arterial spinlabeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading. Am. J. Neuroradiol. 2014; 35 (11): 2091–2098.

24. Alexiou G.A., Zikou A., Tsiouris S. et al. Correlation of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tc-Tetrofosmin brain SPECT with tumour grade and Ki-67 immunohistochemistry in glioma. Clin. Neurol. Neurosurg. 2014; 116: 41–45.

25. Тоноян А.С., Пронин И.Н., Пицхелаури Д.И. и др. Корреляция диффузионно-куртозисной МРТ с пролифе ративной активностью глиом головного мозга. Вопросы нейрохирургии. 2015; 79 (6): 5–14. Tonoyan A.S., Pronin I.N., Pitskhelauri D.I. et al. Correlation of DK-MRI and glioma's prolifaration activity. Voprosi Neurohirurgii. 2015; 79 (6): 5–14. (In Russian)

26. Jiang R., Jiang J., Zhao L. et al. Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget. 2015; 6 (39): 42380–42393.

27. Smith S.M., Jenkinson M., Woolrich M.W. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004; 23, Suppl. 1: S208–219.

28. Woolrich M.W., Jbabdi S., Patenaude B. et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage. 2009; 45 (1, Suppl.): S173–186.

29. Jenkinson M., Beckmann C.F., Behrens T.E. et al., FSL. Neuroimage, 2012; 62 (2): 782–790.

30. Leemans A., Jones D.K. The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 2009; 61 (6): 1336–1349.

31. McGibney G., Smith M.R. An unbiased signal-to-noise ratio measure for magnetic resonance images. Med. Phys. 1993; 20 (4): 1077–1078.

32. Miller A.J., Joseph P.M. The use of power images to perform quantitative analysis on low SNR MR images. Magn. Reson. Imaging. 1993; 11 (7): 1051–1056.

33. Jensen J.H., Helpern J.A. MRI quantification of nonGaussian water diffusion by kurtosis analysis. NMR Biomed. 2010; 23 (7): 698–710.

34. Leemans A., Sijbers J., Jones D.K. Explore DTI: A graphical toolbox for processing, analyzing, and visualizing diffusion MR data, in Proceedings of the 17th Scientific Meeting, International Society for Magnetic Resonance in Medicine. Honolulu. 2009; 3537.

35. Tax C.M., Otte W.M., Max A. et al. REKINDLE: robust extraction of kurtosis INDices with linear estimation. Magn. Reson. Med. 2015; 73 (2): 794–808.

36. Yushkevich P.A., Piven J., Hazlett H.C. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006; 31 (3): 1116–1128.

37. Kleihues P., Ohgaki H. Primary and secondary glioblastomas: from concept to clinical diagnosis. Neuro Oncol. 1999; 1 (1): 44–51.

38. Falangola M.F., Jensen J.H., Babb J.S. et al. Age-related non-Gaussian diffusion patterns in the prefrontal brain. J. Magn. Reson. Imaging. 2008; 28 (6): 1345–1350.

39. Lobel U., Sedlacik J., Gullmar D. et al. Diffusion tensor imaging: the normal evolution of ADC, RA, FA, and eigenvalues studied in multiple anatomical regions of the brain. Neuroradiology. 2009; 51 (4): 253–263.

40. Kang X., Herron T.J., Woods D.L. Regional variation, hemispheric asymmetries and gender differences in pericortical white matter. Neuroimage. 2011; 56 (4): 2011–2023.

41. Latt J., Nilsson M., Wirestam R. et al. Regional values of diffusional kurtosis estimates in the healthy brain. J. Magn. Reson. Imaging. 2013; 37 (3): 610–618.

42. Тоноян А.С., Пронин И.Н., Пицхелаури Д.И. и др. Диффузионно-куртозисная магнитно-резонансная томография: новый метод характеристики структурной организации мозгового вещества (предварительные результаты у здоровых добровольцев). Радиология-практика. 2015; 1: 57–67. Tonoyan A.S., Pronin I.N., Pitskhelauri D.I. et al. Diffusion Kurtosis Magnetic Resonance Imaging: a New Method of Brain Microstructure Characterization (Preliminary Results in Healthy Volunteers). Radiologiya-practika. 2015; 1: 57–67. (In Russian)

43. Тоноян А.С., Пронин И.Н., Пицхелаури Д.И. и др. Корреляция диффузионно-куртозисной МРТ с пролифер тивной активностью глиом головного мозга. Вопросы нейрохирургии. 2015; 79 (6): 5–14. Tonoyan A.S., Pronin I.N., Pitskhelauri D.I. et al. correlation of DK-MRI and glioma's prolifaration activity. Voprosi Neurohirurgii. 2015; 79 (6): 5–14. (In Russian)

44. Kiss R., Dewitte O., Decaestecker C. et al. The combined determination of proliferative activity and cell density in the prognosis of adult patients with supratentorial high-grade astrocytic tumors. Am. J. Clin. Pathol. 1997; 107 (3): 321–331.

45. Sugahara T., Korogi Y., Kochi M. et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J. Magn. Reson. Imaging. 1999; 9 (1): 53–60.

46. Kono K., Inoue Y., Nakayama K. et al. The role of diffusionweighted imaging in patients with brain tumors. Am. J. Neuroradiol. 2001; 22 (6): 1081–1088.

47. Zimmerman R.D. Is there a role for diffusion-weighted imaging in patients with brain tumors or is the “bloom off the rose”? Am. J. Neuroradiol. 2001; 22 (6): 1013–1014.

48. Lam W.W., Poon W.S., Metreweli C. Diffusion MR imaging in glioma: does it have any role in the pre-operation determination of grading of glioma? Clin. Radiol. 2002; 57 (3): 219–225.

49. Tropine A., Vucurevic G., Delani P. et al. Contribution of diffusion tensor imaging to delineation of gliomas and glioblastomas. J. Magn. Reson. Imaging. 2004; 20 (6): 905–912.

50. Beppu T., Inoue T., Shibata Y. et al. Fractional anisotropy value by diffusion tensor magnetic resonance imaging as a predictor of cell density and proliferation activity of glioblastomas. Surg. Neurol. 2005; 63 (1): 56–61.

51. Yuan W., Holland S.K., Jones B.V. et al. Characterization of abnormal diffusion properties of supratentorial brain tumors: a preliminary diffusion tensor imaging study. J. Neurosurg. Pediatr. 2008; 1 (4): 263–269.

52. Wang S., Kim S., Chawla S. et al. Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging. Neuroimage. 2009; 44 (3): 653–660.

53. Kang Y., Choi S.H., Kim Y.J. et al. Gliomas: Histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imagingcorrelation with tumor grade. Radiology. 2011; 261 (3): 882–890.

54. Liu X., Tian W., Kolar B. et al. MR diffusion tensor and perfusion-weighted imaging in preoperative grading of supratentorial nonenhancing gliomas. Neuro Oncol. 2011; 13 (4): 447–455.

55. White M.L., Zhang Y., Yu.F. et al. Diffusion tensor MR imaging of cerebral gliomas: evaluating fractional anisotropy characteristics. Am. J. Neuroradiol. 2011; 32 (2): 374–381.

56. Hilario A., Ramos A., Perez-Nunez A. et al. The added value of apparent diffusion coefficient to cerebral blood volume in the preoperative grading of diffuse gliomas. Am. J. Neuroradiol. 2012; 33 (4): 701–707.

57. Ma L., Song Z.J. Differentiation between low-grade and high-grade glioma using combined diffusion tensor imaging metrics. Clin. Neurol. Neurosurg. 2013; 115 (12): 2489–2495.

58. Server A., Graff B.A., Josefsen R. et al. Analysis of diffusion tensor imaging metrics for gliomas grading at 3T. Eur. J. Radiol. 2014; 83 (3): e156–165.

59. Тоноян А.С., Пронин И.Н., Пицхелаури Д.И. и др. Диффузионно-куртозисная магнитно-резонансная томография – новый метод оценки негауссовской диффузии в нейрорадиологии. Медицинская физика. 2014; 4: 57–63. Tonoyan A.S., Pronin I.N., Pitskhealuri D.I. et al. Diffusion kurtosis magnetic resonance imaging – a new method of non-Gaussian diffusion assessment in neuroradiology. Meditsinskaya Fizika. 2014; 4: 57–63. (In Russian)

60. Goebell E., Paustenbach S., Vaeterlein O. et al. Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor MR imaging. Radiology. 2006; 239 (1): 217–222.

61. Schiffer D., Dutto A., Cavalla P. et al. Prognostic factors in oligodendroglioma. Can. J. Neurol. Sci. 1997; 24 (4): 313–319.

62. Schiffer D., Dutto A., Cavalla P. et al. The prognostic role of vessel productive changes and vessel density in oligodendroglioma. J. Neurooncol. 1999; 44 (2): 99–107.

63. Cha S., Tihan T., Crawford F. et al. Differentiation of lowgrade oligodendrogliomas from low-grade astrocytomas by using quantitative blood-volume measurements derived from dynamic susceptibility contrast-enhanced MR imaging. Am. J. Neuroradiol. 2005; 26 (2): 266–273.

64. Jenkinson M.D., Plessis D.G., Smith T.S. et al. Cellularity and apparent diffusion coefficient in oligodendroglial tumours characterized by genotype. J. Neurooncol. 2010; 96 (3): 385–392.