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Research progress in predicting molecular typing of glioma using magnetic resonance diffusion kurtosis imaging
ZHANG Jie  QIN Jiangbo  TAN Yan 

Cite this article as: ZHANG J, QIN J B, TAN Y. Research progress in predicting molecular typing of glioma using magnetic resonance diffusion kurtosis imaging[J]. Chin J Magn Reson Imaging, 2023, 14(12): 141-145. DOI:10.12015/issn.1674-8034.2023.12.025.

[Abstract] Glioma is the most common primary malignant tumor in the brain parenchyma. In 2021, the World Health Organization Central Nervous System (WHO CNS) further refined the classification of gliomas by using molecular typing for pathological grading and upgrading diagnosis, deepening the importance of molecular typing in the diagnosis and treatment of gliomas. At present, the gold standard for molecular typing diagnosis is pathological testing, but it has the disadvantages of an invasive operation, delayed diagnosis, and an expensive price. In recent years, with the development of diffusion kurtosis imaging (DKI) technology, more and more studies have shown that DKI plays an important role in the differential diagnosis, tumor grading, molecular typing, and prognostic treatment of gliomas. This article provides a review of the application of DKI technology in predicting molecular typing of gliomas, aiming to provide imaging indicators for predicting molecular typing of gliomas and precise clinical treatment of gliomas.
[Keywords] glioma;magnetic resonance imaging;diffusion kurtosis imaging;radiomics;molecular typing

ZHANG Jie1   QIN Jiangbo2   TAN Yan2*  

1 College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China

2 Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China

Corresponding author: TAN Y, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82071893, 82371941); Shanxi Provincial Basic Research Project (No. 202103021224405); Scientific Research Project for Overseas Students of Shanxi Province (No. 2023-186).
Received  2023-07-19
Accepted  2023-11-20
DOI: 10.12015/issn.1674-8034.2023.12.025
Cite this article as: ZHANG J, QIN J B, TAN Y. Research progress in predicting molecular typing of glioma using magnetic resonance diffusion kurtosis imaging[J]. Chin J Magn Reson Imaging, 2023, 14(12): 141-145. DOI:10.12015/issn.1674-8034.2023.12.025.

OSTROM Q T, PRICE M, NEFF C, et al. CBTRUS statistical report: Primary Brain and other central nervous system tumors diagnosed in the United States in 2015-2019[J/OL]. Neuro Oncol, 2022, 24(Suppl 5): v1-v95 [2023-07-12]. DOI: 10.1093/neuonc/noac202.
LOUIS D N, PERRY A, REIFENBERGER G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary[J]. Acta Neuropathol, 2016, 131(6): 803-820. DOI: 10.1007/s00401-016-1545-1.
LOUIS D N, PERRY A, WESSELING P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
CHU X, WU P, YAN H, et al. Comparison of brain microstructure alterations on diffusion kurtosis imaging among Alzheimer's disease, mild cognitive impairment, and cognitively normal individuals[J/OL]. Front Aging Neurosci, 2022, 14: 919143 [2023-07-12]. DOI: 10.3389/fnagi.2022.919143.
LI Y, KIM M M, WAHL D R, et al. Survival prediction analysis in glioblastoma with diffusion kurtosis imaging[J/OL]. Front Oncol, 2021, 11: 690036 [2023-07-12]. DOI: 10.3389/fonc.2021.690036.
XIE S H, LANG R, LI B, et al. Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging (DKI) and mean apparent propagator (MAP) MRI[J]. Neuroradiology, 2023, 65(1): 55-64. DOI: 10.1007/s00234-022-03000-0.
XU C, LI C, XING C, et al. Efficacy of MR diffusion kurtosis imaging for differentiating low-grade from high-grade glioma before surgery: A systematic review and meta-analysis[J/OL]. Clin Neurol Neurosurg, 2022, 220: 107373 [2023-07-12]. DOI: 10.1016/j.clineuro.2022.107373.
MENDEZ A M, FANG L K, MERIWETHER C H, et al. Diffusion Breast MRI: Current Standard and Emerging Techniques[J/OL]. Front Oncol, 2022, 12: 844790 [2023-07-12]. DOI: 10.3389/fonc.2022.844790.
YU Y, LIANG Y. A concise continuous time random-walk diffusion model for characterization of non-exponential signal decay in magnetic resonance imaging[J]. Magn Reson Imaging, 2023, 103: 84-91. DOI: 10.1016/j.mri.2023.07.007.
JENSEN J H, HELPERN J A, RAMANI A, et al. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging[J]. Magn Reson Med, 2005, 53(6): 1432-1440. DOI: 10.1002/mrm.20508.
JENSEN J H, HELPERN J A. MRI quantification of non-Gaussian water diffusion by kurtosis analysis[J]. Nmr Biomed, 2010, 23(7): 698-710. DOI: 10.1002/nbm.1518.
HAOPENG P, XUEFEI D, YAN R, et al. Diffusion kurtosis imaging differs between primary central nervous system lymphoma and high-grade glioma and is correlated with the diverse nuclear-to-cytoplasmic ratio: a histopathologic, biopsy-based study[J]. Eur Radiol, 2020, 30(4): 2125-2137. DOI: 10.1007/s00330-019-06544-7.
WANG X, GAO W, LI F, et al. Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis of the patients with high-grade gliomas[J]. Magn Reson Imaging, 2019, 63: 131-136. DOI: 10.1016/j.mri.2019.08.001.
GUPTA P, VYAS S, SALAN T, et al. Whole brain atlas-based diffusion kurtosis imaging parameters for evaluation of minimal hepatic encephalopathy[J]. Neuroradiol J, 2022, 35(1): 67-76. DOI: 10.1177/19714009211026924.
DONG Z, CUI H. Epigenetic modulation of metabolism in glioblastoma[J]. Semin Cancer Biol, 2019, 57: 45-51. DOI: 10.1016/j.semcancer.2018.09.002.
HAO Z, HU S, LIU Z, et al. Circular RNAs: Functions and prospects in glioma[J]. J Mol Neurosci, 2019, 67(1): 72-81. DOI: 10.1007/s12031-018-1211-2.
GOVINDARAJAN V, SHAH A H, DI L, et al. Systematic review of epigenetic therapies for treatment of IDH-mutant glioma[J]. World Neurosurg, 2022, 162: 47-56. DOI: 10.1016/j.wneu.2022.03.051.
UDDIN M S, MAMUN A A, ALGHAMDI B S, et al. Epigenetics of glioblastoma multiforme: From molecular mechanisms to therapeutic approaches[J]. Semin Cancer Biol, 2022, 83: 100-120. DOI: 10.1016/j.semcancer.2020.12.015.
UCKERMANN O, YAO W, JURATLI T A, et al. IDH1 mutation in human glioma induces chemical alterations that are amenable to optical Raman spectroscopy[J]. J Neurooncol, 2018, 139(2): 261-268. DOI: 10.1007/s11060-018-2883-8.
LAPOINTE S, PERRY A, BUTOWSKI N A. Primary brain tumours in adults[J]. Lancet, 2018, 392(10145): 432-446. DOI: 10.1016/S0140-6736(18)30990-5.
LIU X, LIU M, CAO B, et al. Relationship between IDH1/2 and TERT promoter mutation and the prognosis of human glioma patients[J]. Pak J Med Sci, 2023, 39(3): 843-847. DOI: 10.12669/pjms.39.3.7149.
HAN S, LIU Y, CAI S J, et al. IDH mutation in glioma: molecular mechanisms and potential therapeutic targets[J]. Br J Cancer, 2020, 122(11): 1580-1589. DOI: 10.1038/s41416-020-0814-x.
PATEL S H, BANSAL A G, YOUNG E B, et al. Extent of surgical resection in lower-grade gliomas: Differential impact based on molecular subtype[J]. AJNR Am J Neuroradiol, 2019, 40(7): 1149-1155. DOI: 10.3174/ajnr.A6102.
XU Z, KE C, LIU J, et al. Diagnostic performance between MR amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction at 3 T[J/OL]. Eur J Radiol, 2021, 134: 109466 [2023-07-12]. DOI: 10.1016/j.ejrad.2020.109466.
TAN Y, ZHANG H, WANG X, et al. Comparing the value of DKI and DTI in detecting isocitrate dehydrogenase genotype of astrocytomas[J]. Clin Radiol, 2019, 74(4): 314-320. DOI: 10.1016/j.crad.2018.12.004.
ZHAO J, WANG Y L, LI X B, et al. Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status[J]. J Neurooncol, 2019, 141(1): 195-203. DOI: 10.1007/s11060-018-03025-7.
CHU J P, SONG Y K, TIAN Y S, et al. Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability[J]. Eur Radiol, 2021, 31(2): 729-739. DOI: 10.1007/s00330-020-07204-x.
ZHANG J, CHEN X, CHEN D, et al. Grading and proliferation assessment of diffuse astrocytic tumors with monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging and diffusion kurtosis imaging[J]. Eur J Radiol, 2018, 109: 188-195. DOI: 10.1016/j.ejrad.2018.11.003.
MARTIN K C, MA C, YIP S. From theory to practice: Implementing the WHO 2021 classification of adult diffuse gliomas in neuropathology diagnosis[J/OL]. Brain Sci, 2023, 13(5): 817 [2023-07-12]. DOI: 10.3390/brainsci13050817.
YANG Z, LING F, RUAN S, et al. Clinical and prognostic implications of 1p/19q, IDH, BRAF, MGMT promoter, and TERT promoter alterations, and expression of Ki-67 and p53 in human gliomas[J]. Cancer Manag Res, 2021, 13: 8755-8765. DOI: 10.2147/CMAR.S336213.
XU J, LIU F, LI Y, et al. A 1p/19q codeletion-associated immune signature for predicting lower grade glioma prognosis[J]. Cell Mol Neurobiol, 2022, 42(3): 709-722. DOI: 10.1007/s10571-020-00959-3.
FAMILIARI P, LAPOLLA P, PICOTTI V, et al. Role of 1p/19q codeletion in diffuse low-grade glioma tumour prognosis[J]. Anticancer Res, 2023, 43(6): 2659-2670. DOI: 10.21873/anticanres.16432.
HEMPEL J M, BISDAS S, SCHITTENHELM J, et al. In vivo molecular profiling of human glioma using diffusion kurtosis imaging[J]. J Neurooncol, 2017, 131(1): 93-101. DOI: 10.1007/s11060-016-2272-0.
WANG X, LI F, WANG D, et al. Diffusion kurtosis imaging combined with molecular markers as a comprehensive approach to predict overall survival in patients with gliomas[J/OL]. Eur J Radiol, 2020, 128: 108985 [2023-07-12]. DOI: 10.1016/j.ejrad.2020.108985.
HAQUE W, TEH C, BUTLER E B, et al. Prognostic and predictive impact of MGMT promoter methylation status in high risk grade II glioma[J]. J Neurooncol, 2022, 157(1): 137-146. DOI: 10.1007/s11060-022-03955-3.
DAHLROT R H, LARSEN P, BOLDT H B, et al. Posttreatment effect of MGMT methylation level on glioblastoma survival[J]. J Neuropathol Exp Neurol, 2019, 78(7): 633-640. DOI: 10.1093/jnen/nlz032.
DELLA MONICA R, CUOMO M, BUONAIUTO M, et al. MGMT and whole-genome DNA methylation impacts on diagnosis, prognosis and therapy of glioblastoma multiforme[J/OL]. Int J Mol Sci, 2022, 23(13): 7148 [2023-07-12]. DOI: 10.3390/ijms23137148.
WANG X C, TAN Y, ZHANG H, et al. Diffusion kurtosis imaging reflects GFAP, TopoIIalpha, and MGMT expression in astrocytomas[J]. Neurol India, 2021, 69(1): 119-125. DOI: 10.4103/0028-3886.310109.
SLEDZINSKA P, BEBYN M G, FURTAK J, et al. Prognostic and predictive biomarkers in gliomas[J/OL]. Int J Mol Sci, 2021, 22(19): 10373 [2023-07-12]. DOI: 10.3390/ijms221910373.
OHBA S, KUWAHARA K, YAMADA S, et al. Correlation between IDH, ATRX, and TERT promoter mutations in glioma[J]. Brain Tumor Pathol, 2020, 37(2): 33-40. DOI: 10.1007/s10014-020-00360-4.
PEKMEZCI M, RICE T, MOLINARO A M, et al. Adult infiltrating gliomas with WHO 2016 integrated diagnosis: additional prognostic roles of ATRX and TERT[J]. Acta Neuropathol, 2017, 133(6): 1001-1016. DOI: 10.1007/s00401-017-1690-1.
ARITA H, ICHIMURA K. Prognostic significance of TERT promoter mutations in adult-type diffuse gliomas[J]. Brain Tumor Pathol, 2022, 39(3): 121-129. DOI: 10.1007/s10014-021-00424-z.
ANAND N, HUSAIN N, VARSHNEY R, et al. Molecular classification and stratification of adult diffuse gliomas: A tertiary care center study[J/OL]. J Carcinog, 2021, 20: 20 [2023-07-12]. DOI: 10.4103/jcar.jcar_17_21.
XIE Y, TAN Y, YANG C, et al. Omics-based integrated analysis identified ATRX as a biomarker associated with glioma diagnosis and prognosis[J]. Cancer Biol Med, 2019, 16(4): 784-796. DOI: 10.20892/j.issn.2095-3941.2019.0143.
LV X S, ZHAO H M, DONG Q R, et al. Application value of DKI in predicting IDH and TERT molecular subtypes of glioma[J]. Chin J CT & MRI, 2023, 21(3): 6-8. DOI: 10.3969/j.issn.1672-5131.2023.03.003.
MCCAGUE C, RAMLEE S, REINIUS M, et al. Introduction to radiomics for a clinical audience[J]. Clin Radiol, 2023, 78(2): 83-98. DOI: 10.1016/j.crad.2022.08.149.
AFTAB K, AAMIR F B, MALLICK S, et al. Radiomics for precision medicine in glioblastoma[J]. J Neurooncol, 2022, 156(2): 217-231. DOI: 10.1007/s11060-021-03933-1.
KUMAR A, JHA A K, AGARWAL J P, et al. Machine-learning-based radiomics for classifying glioma grade from magnetic resonance images of the brain[J/OL]. J Pers Med, 2023, 13(6): 920 [2023-07-12]. DOI: 10.3390/jpm13060920.
BISDAS S, SHEN H, THUST S, et al. Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study[J/OL]. Sci Rep, 2018, 8(1): 6108 [2023-07-12]. DOI: 10.1038/s41598-018-24438-4.
TAN Y, MU W, WANG X C, et al. Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: A preliminary study[J/OL]. Eur J Radiol, 2020, 124: 108785 [2023-07-12]. DOI: 10.1016/j.ejrad.2019.108785.
PAN T, SU C Q, TANG W T, et al. Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas[J]. Acta Radiol, 2023, 64(9): 2552-2560. DOI: 10.1177/02841851231180291.

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