Share：
Share this content in WeChat
X
[Chinese][PDF]6381
• Review •
Research progress in predicting molecular typing of lower grade glioma by functional magnetic resonance imaging
SHEN Pengxin  TAN Yan 

Cite this article as: SHEN P X, TAN Y. Research progress in predicting molecular typing of lower grade glioma by functional magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2023, 14(2): 168-173. DOI:10.12015/issn.1674-8034.2023.02.030.


[Abstract] Glioma is the most common intracranial malignant tumor with high recurrence rate and poor prognosis. Lower grade glioma refers to tumors classified into grade 2 and grade 3 by World Health Organization (WHO). Molecular classification of lower grade glioma has important guiding significance for its treatment and prognosis. Therefore, the diagnosis of molecular classification is of great important for clinical management of glioma. The genetic testing based on pathological tissue is the gold standard, which has certain invasiveness and hysteresis quality. In recent years, with the development of functional magnetic resonance imaging, more and more studies have clarified the value of functional magnetic resonance imaging in predicting molecular typing for lower grade glioma. This paper reviews the research progress of functional magnetic resonance imaging in predicting the molecular classification of lower grade gliomas in recent years.
[Keywords] lower grade glioma；molecular typing；magnetic resonance imaging；functional magnetic resonance imaging；diffusion tensor imaging；diffusion kurtosis imaging；intravoxel incoherent motion；mean apparent propagation factor magnetic resonance imaging；arterial spin labeling；dynamic susceptibility contrast；dynamic contrast-enhanced；amide proton transfer imaging

SHEN Pengxin1   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

*Correspondence to: Tan Y, E-mail: tanyan123456@sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82071893).
Received  2022-09-19
Accepted  2023-01-12
DOI: 10.12015/issn.1674-8034.2023.02.030
Cite this article as: SHEN P X, TAN Y. Research progress in predicting molecular typing of lower grade glioma by functional magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2023, 14(2): 168-173. DOI:10.12015/issn.1674-8034.2023.02.030.

[1]
SHEN Y, CHI H, XU K, et al. A Novel Classification Model for Lower-Grade Glioma Patients Based on Pyroptosis-Related Genes[J/OL]. Brain Sci, 2022, 12(6): 700 [2022-09-24]. https://doi.org/10.3390/brainsci12060700. DOI: 10.3390/brainsci12060700.
[2]
MOTOMURA K. Lower-grade Gliomas[J]. No Shinkei Geka, 2021, 49(3): 632-639. DOI: 10.11477/mf.1436204437.
[3]
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.
[4]
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.
[5]
ŚLEDZINSKA P, BEBYN M G, FURTAK J, et al. Prognostic and Predictive Biomarkers in Gliomas[J/OL]. Int J Mol Sci, 2021, 22(19): 10373 [2022-09-24]. https://doi.org/10.3390/ijms221910373. DOI: 10.3390/ijms221910373.
[6]
ÇELIK S, ÖVEN B B, DEMIR M K, et al. Magnetic resonance imaging criteria for prediction of isocitrate dehydrogenase (IDH) mutation status in patients with grade Ⅱ-Ⅲ astrocytoma and oligodendroglioma[J/OL]. Clin Neurol Neurosurg, 2021, 207: 106745 [2022-09-24]. https://doi.org/10.1016/j.clineuro.2021.106745. DOI: 10.1016/j.clineuro.2021.106745.
[7]
WU F, LI G Z, LIU H J, et al. Molecular subtyping reveals immune alterations in IDH wild-type lower-grade diffuse glioma[J]. J Pathol, 2020, 251(3): 272-283. DOI: 10.1002/path.5468.
[8]
LV L, ZHANG Y, ZHAO Y, et al. Effects of 1p/19q Codeletion on Immune Phenotype in Low Grade Glioma[J/OL]. Front Cell Neurosci, 2021, 15: 704344 [2022-09-24]. https://doi.org/10.3389/fncel.2021.704344. DOI: 10.3389/fncel.2021.704344.
[9]
KHA Q H, LE V H, HUNG T N K, et al. Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas[J/OL]. Cancers, 2021, 13(21): 5398 [2022-09-24]. https://doi.org/10.3390/cancers13215398. DOI: 10.3390/cancers13215398.
[10]
MILLER J J, CAHILL D P. MGMT promoter methylation and hypermutant recurrence in IDH mutant lower-grade glioma[J]. Neuro Oncol, 2020, 22(11): 1553-1554. DOI: 10.1093/neuonc/noaa212.
[11]
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.
[12]
GALBRAITH K, SNUDERL M. Molecular Pathology of Gliomas[J]. Surg Pathol Clin, 2021, 14(3): 379-386. DOI: 10.1016/j.path.2021.05.003.
[13]
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.
[14]
LI Y, ZHANG W. Quantitative evaluation of diffusion tensor imaging for clinical management of glioma[J]. Neurosurg Rev, 2020, 43(3): 881-891. DOI: 10.1007/s10143-018-1050-1.
[15]
XIONG J, TAN W, WEN J, et al. Combination of diffusion tensor imaging and conventional MRI correlates with isocitrate dehydrogenase 1/2 mutations but not 1p/19q genotyping in oligodendroglial tumours[J]. Eur Radiol, 2016, 26(6): 1705-1715. DOI: 10.1007/s00330-015-4025-4.
[16]
FIGINI M, RIVA M, GRAHAM M, et al. Prediction of Isocitrate Dehydrogenase Genotype in Brain Gliomas with MRI: Single-Shell versus Multishell Diffusion Models[J]. Radiology, 2018, 289(3): 788-796. DOI: 10.1148/radiol.2018180054.
[17]
HUANG Z, LU C, LI G, et al. Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters[J]. Front Oncol, 2021, 11: 627202 [2022-09-24]. https://doi.org/10.3389/fonc.2021.627202. DOI: 10.3389/fonc.2021.627202.
[18]
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 [2022-09-24]. https://doi.org/10.1016/j.ejrad.2020.109466. DOI: 10.1016/j.ejrad.2020.109466.
[19]
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 [2022-09-24]. https://doi.org/10.1016/j.ejrad.2020.108985. DOI: 10.1016/j.ejrad.2020.108985.
[20]
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.
[21]
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.
[22]
WANG C, DONG H. Intravoxel incoherent motion magnetic resonance imaging in predicting IDH1 gene mutations in high-grade gliomas[J]. Acta Radiol, 2021, 62(10): 1412-1417. DOI: 10.1177/02841851211011914.
[23]
GU T, YANG T, HUANG J, et al. Evaluation of gliomas peritumoral diffusion and prediction of IDH1 mutation by IVIM-DWI[J]. Aging, 2021, 13(7): 9948-9959. DOI: 10.18632/aging.202751.
[24]
WANG X, CHEN X Z, SHI L, et al. Glioma grading and IDH1 mutational status: assessment by intravoxel incoherent motion MRI[J/OL]. Clin Radiol, 2019, 74(8): 651.e7-651.e14 [2022-09-24]. https://doi.org/10.1016/j.crad.2019.03.020. DOI: 10.1016/j.crad.2019.03.020.
[25]
FENG P P, WANG C C, DONG H B, et al. The value of intravoxel incoherent motion diffusion-weighted imaging in predicting IDH1 gene mutation of gliomas[J]. Zhejiang Med J, 2021, 43(2): 143-147, 234. DOI: 10.12056/j.issn.1006-2785.2021.43.2.2020-2380.
[26]
LI J J. Application Value of IVIM in Predicting IDH and 1p/19q Status in Gliomas[D]. Taiyuan: Shanxi Med Univ, 2021. DOI: 10.27288/d.cnki.gsxyu.2021.000499.
[27]
SUN Y, SU C, DENG K, et al. Mean apparent propagator-MRI in evaluation of glioma grade, cellular proliferation, and IDH-1 gene mutation status[J]. Eur Radiol, 2022, 32(6): 3744-3754. DOI: 10.1007/s00330-021-08522-4.
[28]
WANG P, WENG L, XIE S, et al. Primary application of mean apparent propagator-MRI diffusion model in the grading of diffuse glioma[J/OL]. Eur J Radiol, 2021, 138: 109622 [2022-09-24]. https://doi.org/10.1016/j.ejrad.2021.109622. DOI: 10.1016/j.ejrad.2021.109622.
[29]
LUAN J, WU M, WANG X, et al. The diagnostic value of quantitative analysis of ASL, DSC-MRI and DKI in the grading of cerebral gliomas: a meta-analysis[J/OL]. Radiat Oncol, 2020, 15(1): 204 [2022-09-24]. https://doi.org/10.1186/s13014-020-01643-y. DOI: 10.1186/s13014-020-01643-y.
[30]
WANG N, XIE S Y, LIU H M, et al. Arterial Spin Labeling for Glioma Grade Discrimination: Correlations with IDH1 Genotype and 1p/19q Status[J]. Transl Oncol, 2019, 12(5): 749-756. DOI: 10.1016/j.tranon.2019.02.013.
[31]
BRENDLE C, HEMPEL J M, SCHITTENHELM J, et al. Glioma Grading and Determination of IDH Mutation Status and ATRX loss by DCE and ASL Perfusion[J]. Clin Neuroradiol, 2018, 28(3): 421-428. DOI: 10.1007/s00062-017-0590-z.
[32]
LU D, LI Y, LU H, et al. Histogram-based analysis of cerebral blood flow using arterial spin labeling MRI in de novo brain gliomas: relationship to histopathologic grade and molecular markers[J]. Neuroradiology, 2021, 63(5): 751-760. DOI: 10.1007/s00234-020-02625-3.
[33]
REN Y, ZHANG X, RUI W, et al. Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low-Grade Gliomas Using Multiparametric MR Radiomic Features[J]. J Magn Reson Imaging, 2019, 49(3): 808-817. DOI: 10.1002/jmri.26240.
[34]
FALK A, FAHLSTROM M, ROSTRUP E, et al. Discrimination between glioma grades II and III in suspected low-grade gliomas using dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging: a histogram analysis approach[J]. Neuroradiology, 2014, 56(12): 1031-1038. DOI: 10.1007/s00234-014-1426-z.
[35]
XING Z, YANG X, SHE D, et al. Noninvasive Assessment of IDH Mutational Status in World Health Organization Grade Ⅱ and Ⅲ Astrocytomas Using DWI and DSC-PWI Combined with Conventional MR Imaging[J]. AJNR Am J Neuroradiol, 2017, 38(6): 1138-1144. DOI: 10.3174/ajnr.A5171.
[36]
PAECH D, WINDSCHUH J, OBERHOLLENZER J, et al. Assessing the predictability of IDH mutation and MGMT methylation status in glioma patients using relaxation-compensated multipool CEST MRI at 7.0 T[J]. Neuro Oncol, 2018, 20(12): 1661-1671. DOI: 10.1093/neuonc/noy073.
[37]
AHN S H, AHN S S, PARK Y W, et al. Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study[J/OL]. Neuroradiol J, 2022, 19714009221098369 [2022-09-24]. https://doi.org/10.1177/19714009221098369. DOI: 10.1177/19714009221098369.
[38]
SUH C H, KIM H S, JUNG S C, et al. Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis[J]. Eur Radiol, 2019, 29(2): 745-758. DOI: 10.1007/s00330-018-5608-7.
[39]
ZHANG H W, LYU G W, HE W J, et al. DSC and DCE Histogram Analyses of Glioma Biomarkers, Including IDH, MGMT, and TERT, on Differentiation and Survival[J/OL]. Acad Radiol, 2020, 27(12): e263-e271 [2022-09-24]. https://doi.org/10.1016/j.acra.2019.12.010. DOI: 10.1016/j.acra.2019.12.010.
[40]
HU Y, CHEN Y, WANG J, et al. Non-Invasive Estimation of Glioma IDH1 Mutation and VEGF Expression by Histogram Analysis of Dynamic Contrast-Enhanced MRI[J/OL]. Front Oncol, 2020, 10: 593102 [2022-09-24]. https://doi.org/10.3389/fonc.2020.593102. DOI: 10.3389/fonc.2020.593102.
[41]
LI S H, SHEN N X, WU D, et al. A Comparative Study Between Tumor Blood Vessels and Dynamic Contrast-enhanced MRI for Identifying Isocitrate Dehydrogenase Gene 1 (IDH1) Mutation Status in Glioma[J]. Curr Med Sci, 2022, 42(3): 650-657. DOI: 10.1007/s11596-022-2563-y.
[42]
GUPTA M, GUPTA A, YADAV V, et al. Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI[J]. Neuroradiology, 2021, 63(8): 1227-1239. DOI: 10.1007/s00234-021-02636-8.
[43]
YANG X, LIN Y, XING Z, et al. Predicting 1p/19q codeletion status using diffusion-, susceptibility-, perfusion-weighted, and conventional MRI in IDH-mutant lower-grade gliomas[J]. Acta Radiol, 2021, 62(12): 1657-1665. DOI: 10.1177/0284185120973624.
[44]
LATYSHEVA A, EMBLEM K E, BRANDAL P, et al. Dynamic susceptibility contrast and diffusion MR imaging identify oligodendroglioma as defined by the 2016 WHO classification for brain tumors: histogram analysis approach[J]. Neuroradiology, 2019, 61(5): 545-555. DOI: 10.1007/s00234-019-02173-5.
[45]
HEMPEL J M, SCHITTENHELM J, KLOSE U, et al. In Vivo Molecular Profiling of Human Glioma: Cross-Sectional Observational Study Using Dynamic Susceptibility Contrast Magnetic Resonance Perfusion Imaging[J]. Clin Neuroradiol, 2019, 29(3): 479-491. DOI: 10.1007/s00062-018-0676-2.
[46]
VAN SANTWIJK L, KOUWENBERG V, MEIJER F, et al. A systematic review and meta-analysis on the differentiation of glioma grade and mutational status by use of perfusion-based magnetic resonance imaging[J/OL]. Insights Imaging, 2022, 13(1): 102 [2022-09-24]. https://doi.org/10.1186/s13244-022-01230-7. DOI: 10.1186/s13244-022-01230-7.
[47]
PARK Y W, AHN S S, PARK C J, et al. Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas[J]. Eur Radiol, 2020, 30(12): 6475-6484. DOI: 10.1007/s00330-020-07090-3.
[48]
SOTIRIOS B, DEMETRIOU E, TOPRICERNU C C, et al. The role of APT imaging in gliomas grading: A systematic review and meta-analysis[J/OL]. Eur J Radiol, 2020, 133: 109353 [2022-09-24]. https://doi.org/10.1016/j.ejrad.2020.109353. DOI: 10.1016/j.ejrad.2020.109353.
[49]
JIANG S, ZOU T, EBERHART C G, et al. Predicting IDH mutation status in grade Ⅱ gliomas using amide proton transfer-weighted (APTw) MRI[J]. Magn Reson Med, 2017, 78(3): 1100-1109. DOI: 10.1002/mrm.26820.
[50]
ZHANG H, CUI Y Y, FENG J, et al. Diagnostic and Predictive Efficacy of Amide Proton Transfer MRI in Tumor Grading, Isocitrate Dehydrogenase Status and the Expression Level of Ki-67 and p53 in Gliomas[J]. Chin J Med Imaging, 2020, 28(5): 327-332. DOI: 10.3969/j.issn.1005-5185.2020.05.002.
[51]
GUO H, LIU J, HU J, et al. Diagnostic performance of gliomas grading and IDH status decoding A comparison between 3D amide proton transfer APT and four diffusion-weighted MRI models[J]. J Magn Reson Imaging, 2022, 56(6): 1834-1844. DOI: 10.1002/jmri.28211.

PREV Progress in assessment of unstable state risk of unruptured cerebral aneurysms by multimodal MRI
NEXT Research progress of MRI-based texture analysis in high-grade gliomas
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn