Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status
Few studies have applied diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) for the comprehensive assessment of gliomas [tumour grade, isocitrate dehydrogenase-1 (IDH-1) mutation status and tumour proliferation rate (Ki-67)]. This study describes the efficacy of DKI and DTI to comprehensively evaluate gliomas, compares their results.
Fifty-two patients (18 females; median age, 47.5 years) with pathologically proved gliomas were prospectively included. All cases underwent DKI examination. DKI (mean kurtosis: MK, axial kurtosis: Ka, radial kurtosis: Kr) and DTI (mean diffusivity: MD, fractional anisotropy: FA) maps of each metric was derived. Three ROIs were manually drawn.
MK, Ka, Kr and FA were significantly higher in HGGs than in LGGs, whereas MD was significantly lower in HGGs than in LGGs (P < 0.01). ROC analysis demonstrated that MK (specificity: 100% sensitivity: 79%) and Ka (specificity: 96% sensitivity: 82%) had the same and highest (AUC: 0.93) diagnostic value. Moreover, MK, Ka, and Kr were significantly higher in grade III than II gliomas (P ≦ 0.01). Further, DKI and DTI can significantly identify IDH-1 mutation status (P ≦ 0.03). Ka (sensitivity: 74%, specificity: 75%, AUC: 0.72) showed the highest diagnostic value. In addition, DKI metrics and MD showed significant correlations with Ki-67 (P ≦ 0.01) and Ka had the highest correlation coefficient (rs = 0.72).
Compared with DTI, DKI has great advantages for the comprehensive assessment of gliomas. Ka might serve as a promising imaging index in predicting glioma grading, tumour cell proliferation rate and IDH-1 gene mutation status.
KeywordsGlioma Diffusion Magnetic resonance imaging Isocitrate dehydrogenase Ki-67 label index
This study was funded by National Natural Science Foundation of China (CN) (Grant No. 81201074) and Fundamental Research Funds for the Central Universities (Grant No. 13ykpy14).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This study was approved by the Research Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University according to the ethical guidelines for human research and is compliant with the Health Insurance Portability and Accountability Act (HIPAA).
Written informed consent was obtained from adult patients or their legal guardians.
- 1.Ostrom QT, Gittleman H, Fulop J, Liu M, Blanda R, Kromer C, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS (2015) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro Oncol 17:v1–v62. https://doi.org/10.1093/neuonc/nov189 CrossRefGoogle Scholar
- 3.Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820. https://doi.org/10.1007/s00401-016-1545-1 CrossRefGoogle Scholar
- 4.Hartmann C, Hentschel B, Wick W, Capper D, Felsberg J, Simon M, Westphal M, Schackert G, Meyermann R, Pietsch T, Reifenberger G, Weller M, Loeffler M, von Deimling A (2010) Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol 120:707–718. https://doi.org/10.1007/s00401-010-0781-z CrossRefGoogle Scholar
- 5.van den Bent MJ, Hartmann C, Preusser M, Ströbel T, Dubbink HJ, Kros JM, von Deimling A, Boisselier B, Sanson M, Halling KC, Diefes KL, Aldape K, Giannini C (2013) Interlaboratory comparison of IDH mutation detection. J Neurooncol 112:173–178. https://doi.org/10.1007/s11060-013-1056-z CrossRefGoogle Scholar
- 9.McGirt MJ, Woodworth GF, Coon AL, Frazier JM, Amundson E, Garonzik I, Olivi A, Weingart JD (2005) Independent predictors of morbidity after image-guided stereotactic brain biopsy: a risk assessment of 270 cases. J Neurosurg 102:897–901. https://doi.org/10.3171/jns.2005.102.5.0897 CrossRefGoogle Scholar
- 10.Romano A, Calabria LF, Tavanti F, Minniti G, Rossi-Espagnet MC, Coppola V, Pugliese S, Guida D, Francione G, Colonnese C, Fantozzi LM, Bozzao A (2013) Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: correlation with MGMT promoter methylation status. Eur Radiol 23:513–520. https://doi.org/10.1007/s00330-012-2601-4 CrossRefGoogle Scholar
- 11.Ahn SS, Shin NY, Chang JH, Kim SH, Kim EH, Kim DW, Lee SK (2014) Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging. J Neurosurg 121:367–373. https://doi.org/10.3171/2014.5.JNS132279 CrossRefGoogle Scholar
- 16.Kickingereder P, Sahm F, Radbruch A, Wick W, Heiland S, A Deimling, Bendszus M, Wiestler B (2015) IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Sci Rep 5:16238. https://doi.org/10.1038/srep16238 CrossRefGoogle Scholar
- 17.Lee S, Choi SH, Ryoo I, Yoon TJ, Kim TM, Lee SH, Park CK, Kim JH, Sohn CH, Park SH, Kim IH (2015) Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging. J Neurooncol 121:141–150. https://doi.org/10.1007/s11060-014-1614-z CrossRefGoogle Scholar
- 18.Xiong J, Tan WL, Pan JW, Wang Y, Yin B, Zhang J, Geng DY (2016) Detecting isocitrate dehydrogenase gene mutations in oligodendroglial tumors using diffusion tensor imaging metrics and their correlations with proliferation and microvascular density. J Magn Reson Imaging 43:45–54. https://doi.org/10.1002/jmri.24958 CrossRefGoogle Scholar
- 20.Hempel JM, Schittenhelm J, Brendle C, Bender B, Bier G, Skardelly M, Tabatabai G, Castaneda Vega S, Ernemann U, Klose U (2017) Histogram analysis of diffusion kurtosis imaging estimates for in vivo assessment of 2016 WHO glioma grades: a cross-sectional observational study. Eur J Radiol 95:202–211. https://doi.org/10.1016/j.ejrad.2017.08.008 CrossRefGoogle Scholar
- 22.Alexiou GA, Zikou A, Tsiouris S, Goussia A, Kosta P, Papadopoulos A, Voulgaris S, Kyritsis AP, Fotopoulos AD, Argyropoulou MI (2014) Correlation of diffusion tensor, dynamic susceptibility contrast MRI and (99 m) Tc-Tetrofosmin brain SPECT with tumour grade and Ki-67 immunohistochemistry in glioma. Clin Neurol Neurosurg 116:41–45. https://doi.org/10.1016/j.clineuro.2013.11.003 CrossRefGoogle Scholar
- 27.Zhao J, Li JB, Wang JY, Wang YL, Liu DW, Li XB, Song YK, Tian YS, Yan X, Li ZH, He SF, Huang XL, Jiang L, Yang ZY, Chu JP (2018) Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status. NeuroImage 19:174–181. https://doi.org/10.1016/j.nicl.2018.04.011 CrossRefGoogle Scholar
- 28.White NS, McDonald CR, Farid N, Kuperman JM, Kesari S, Dale AM (2013) Improved conspicuity and delineation of high-grade primary and metastatic brain tumors using “restriction spectrum imaging”: quantitative comparison with high B-value DWI and ADC. AJNR Am J Neuroradiol 34:958–964.CrossRefGoogle Scholar
- 29.Cha S (2006) Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol 27:475–487Google Scholar
- 30.Cai J, Zhang C, Zhang W, Wang G, Yao K, Wang Z, Li G, Qian Z, Li Y, Jiang T, Jiang C (2016) ATRX, IDH1-R132H and Ki-67 immunohistochemistry as a classification scheme for astrocytic tumors. Oncoscience 3:258–265. https://doi.org/10.18632/oncoscience.317.eCollection2016 Google Scholar