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Research progress of magnetic resonance diffusion tensor imaging in glioma grading and genotype prediction
WU Xiaoyi  WU Yuankui 

Cite this article as: WU X Y, WU Y K. Research progress of magnetic resonance diffusion tensor imaging in glioma grading and genotype prediction[J]. Chin J Magn Reson Imaging, 2024, 15(6): 190-195. DOI:10.12015/issn.1674-8034.2024.06.030.


[Abstract] Glioma is the most common intracranial primary malignant tumor, and the treatment and prognosis of different grades and genotypes of glioma are obviously different. Diffusion tensor imaging (DTI) can reflect the pathological changes of the brain microstructure, especially the white matter tracts. Many scholars have studied the diagnostic value of DTI for glioma grading and genotyping. This article mainly reviews the research progress of quantitative metrics and radiomics model based on DTI which has been used to predict glioma grade and genotype, in order to provide imaging help for individualized diagnosis and treatment plans and prognosis prediction of glioma patients.
[Keywords] glioma；magnetic resonance imaging；diffusion tensor imaging；imaging omics；pathology；genotyping

WU Xiaoyi   WU Yuankui*  

Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

Corresponding author: WU Y K, E-mail: ripleyor@126.com

Conflicts of interest   None.

Received  2023-12-13
Accepted  2024-06-05
DOI: 10.12015/issn.1674-8034.2024.06.030
Cite this article as: WU X Y, WU Y K. Research progress of magnetic resonance diffusion tensor imaging in glioma grading and genotype prediction[J]. Chin J Magn Reson Imaging, 2024, 15(6): 190-195. DOI:10.12015/issn.1674-8034.2024.06.030.

[1]
OSTROM Q T, CIOFFI G, WAITE K, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014-2018[J]. Neuro Oncol, 2021, 23(12Suppl 2): iii1-ii105. DOI: 10.1093/neuonc/noab200.
[2]
GITTLEMAN H, LIM D, KATTAN M W, et al. An independently validated nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825[J]. Neuro Oncol, 2017, 19(5): 669-677. DOI: 10.1093/neuonc/now208.
[3]
SANA, N, BERGER M S. Glioma extent of resection and its impact on patient outcome[J]. Neurosurgery, 2008, 62(4): 753-764; discussion 264-266. DOI: 10.1227/01.neu.0000318159.21731.cf.
[4]
BRAT D J, VERHAAK R G, ALDAPE K D, et al. Cancer Genome Atlas Research Network. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas[J]. N Engl J Med, 2015, 372: 2481-2498. DOI: 10.1056/NEJMoa1402121.
[5]
FENG Z B, KONG D S, FENG S Y, et al. Research advances in effect of isocitrate dehydrogenase gene status on surgical strategies in diffuse gliomas[J]. Acad J Chin PLA Med Sch, 2023, 44(8): 928-932. DOI: 10.12435/j.issn.2095-5227.2023.007.
[6]
ZHENG L L, LUO C, LANG J Y, et al. Current status and new progress of targeted therapy and immunotherapy for high-grade glioma[J]. J Cancer Control Treat, 2023, 36(9): 806-814. DOI: 10.3969/j.issn.1674-0904.2023.09.014.
[7]
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.
[8]
PRICE S J, PEñA A, BURNET N G, et al. Tissue signature characterisation of diffusion tensor abnormalities in cerebral gliomas[J]. Eur Radiol, 2004, 14(10): 1909-1917. DOI: 10.1007/s00330-004-2381-6.
[9]
HUNG N DUY, MINH DUC N, VAN ANH N T, et al. Diagnostic performance of diffusion tensor imaging for preo-perative glioma grading[J]. Clin Ter, 2021, 172(4): 315-321. DOI: 10.7417/CT.2021.2335.
[10]
VIDYADHARAN S, PRABHAKAR RAO B, PERUMAL Y, et al. Deep learning classifies low- and high-grade glioma patients with high accuracy, sensitivity, and specificity based on their brain white matter networks derived from diffusion tensor imaging[J/OL]. Diagnostics (Basel), 202212(12): 3216 [2023-12-10]. https://pubmed.ncbi.nlm.nih.gov/36553224/. DOI: 10.3390/diagnostics12123216.
[11]
LI X Y, ZHANG X Y, GAO Y N, et al. The value of histogram analysis of diffusion tensor imaging in predicting ATRX gene status in lower-grade gliomas[J]. J Chin Clin Med Imaging, 2023, 34(3): 153-158. DOI: 10.12117/jccmi.2023.03.001.
[12]
JÜTTEN K, MAINZ V, GAUGGEL S, et al. Diffusion tensor imaging reveals microstructural heterogeneity of normal-appearing white matter and related cognitive dysfunction in glioma patients[J/OL]. Front Oncol, 2019, 9: 536 [2023-12-10]. https://pubmed.ncbi.nlm.nih.gov/31293974/. DOI: 10.3389/fonc.2019.00536.
[13]
LIN K, CIDAN W, QI Y, et al. Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton magnetic resonance spectroscopy and diffusion tensor imaging[J]. Med Phys, 2022, 49(7): 4419-4429. DOI: 10.1002/mp.15648.
[14]
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 [2023-12-10]. https://pubmed.ncbi.nlm.nih.gov/32004731/. DOI: 10.1016/j.ejrad.2019.108785.
[15]
ZHAO H, BAI Y, WANG M Y. Advances in multimodal magnetic resonance imaging in genotyping and prognostic evaluation of glioma[J]. Chin J Magn Reson Imaging, 2021, 12(9): 98-102. DOI: 10.12015/issn.1674-8034.2021.09.025.
[16]
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.
[17]
HARTMANN C, HENTSCHEL B, WICK W, et al. 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[J]. Acta Neuropathol, 2010 120(6): 707-718. DOI: 10.1007/s00401-010-0781-z.
[18]
CHEN R H, TAN Y. Research advances of radiomics in prognosis prediction of lower-grade gliomas[J]. Chin J Magn Reson Imaging, 2023, 14(3): 159-164. DOI: 10.12015/issn.1674-8034.2023.03.029.
[19]
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.
[20]
JENKINS R B, BLAIR H, BALLMAN K V, et al. A t(1;19)(q10;p10) mediates the combined deletions of 1p and 19q and predicts a better prognosis of patients with oligodendroglioma[J]. Cancer Res, 2006, 66(20): 9852-9861. DOI: 10.1158/0008-5472.CAN-06-1796.
[21]
ECKEL-PASSOW J E, LACHANCE D H, MOLINARO A M, et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors[J]. N Engl J Med, 2015 372(26): 2499-2508. DOI: 10.1056/NEJMoa1407279.
[22]
Kishida Y, Natsume A, Toda H, et al. Correlation between quantified promoter methylation and enzymatic activity of O6-methylguanine-DNA methyltransferase in glioblastomas[J]. Tumour Biol, 2012, 33(2): 373-381. DOI: 10.1007/s13277-012-0319-1.
[23]
HEGI M E, DISERENS A C, GORLIA T, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma[J]. N Engl J Med, 2005, 352(10): 997-1003. DOI: 10.1056/NEJMoa043331.
[24]
OSTROM Q T, SHOAF M L, CIOFFI G, et al. National-level overall survival patterns for molecularly-defined diffuse glioma types in the United States[J]. Neuro Oncol, 2023 25(4): 799-807. DOI: 10.1093/neuonc/noac198.
[25]
TERZI N K, YILMAZ I, OZ A B. The place and prognostic value of TERT promoter mutation in molecular classification in grade Ⅱ-Ⅲ Glial tumors and primary glioblastomas[J]. Turk Patoloji Derg, 2022, 38(2): 90-98. DOI: 10.5146/tjpath.2021.01555.
[26]
FAN X L, YANG Z C. The molecular classification of adult diffuse glioma: a review[J]. Journal of Beijing Normal University (Natural Science), 2023 59(1): 38-42. DOI: 10.12202/j.0476-0301.2022153.
[27]
ARITA H, ICHIMURA K. Prognostic significance of TERT promoter mutations in adult-type diffuse gliomas[J]. Brain Tumor Pathol, 2022 39: 121-129. DOI: 10.1007/s10014-021-00424-z.
[28]
WANG J, ZHU B L, LIN Z Y. Research progress of MRI diffusion tensor imaging in the diagnosis and grading of glioma[J]. Chinese Journal of Practical Nervous Diseases, 2023, 26(5): 641-645. DOI: 10.12083/SYSJ.221853.
[29]
ZHANG C, GAO Y. Research progress of magnetic resonance diffusion imaging in glioma grading and IDH genotype prediction[J]. Chin J Magn Reson Imaging, 2023, 14(7): 149-154. DOI: 10.12015/issn.1674-8034.2023.07.027.
[30]
KLAWITER E C, SCHMIDT R E, TRINKAUS K, et al. Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords[J]. Neuroimage, 2011, 55(4): 1454-1460. DOI: 10.1016/j.neuroimage.2011.01.007.
[31]
WHITE M L, ZHANG Y, YU F, et al. Diffusion tensor MR imaging of cerebral gliomas: evaluating fractional anisotropy characteristics[J]. AJNR Am J Neuroradiol, 2011, 32(2): 374-381. DOI: 10.3174/ajnr.A2267.
[32]
LIU X, TIAN W, KOLAR B, et al. MR diffusion tensor and perfusion-weighted imaging in preoperative grading of supratentorial nonenhancing gliomas[J]. Neuro Oncol, 2011, 13(4): 447-455. DOI: 10.1093/neuonc/noq197.
[33]
NOUE T, OGASAWARA K, BEPPU T, et al. Diffusion tensor imaging for preoperative evaluation of tumor grade in gliomas[J]. Clin Neurol Neurosurg, 2005, 107(3): 174-180. DOI: 10.1016/j.clineuro.2004.06.011.
[34]
ZENG S X, WANG C W, PENG S H. The application value of DTI in the differential diagnosis of grade Ⅱ and Ⅲ glioma[J]. J Clin Radiol, 2020, 39(7): 1253-1257. DOI: 10.13437/j.cnki.jcr.2020.07.004.
[35]
LIN H, XU Y, CHEN L, et al. Multiparametric and multiregional diffusion features help predict molecule information, grade and survival in lower-grade gliomas: a feasibility study[J/OL]. Br J Radiol, 2019, 92(1103): 20190324 [2023-12-10]. https://pubmed.ncbi.nlm.nih.gov/31386559/. DOI: 10.1259/bjr.20190324.
[36]
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.
[37]
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[J]. Surg Neurol, 2005, 63(1): 56-61; discussion 61. DOI: 10.1016/j.surneu.2004.02.034.
[38]
ZOU Q G, XU H B, LIU F, et al. In the assessment of supratentorial glioma grade: the combined role of multivoxel proton MR spectroscopy and diffusion tensor imaging[J]. Clin Radiol, 2011, 66(10): 953-960. DOI: 10.1016/j.crad.2011.05.001.
[39]
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.
[40]
FUDABA H, SHIMOMURA T, ABE T, et al. Comparison of multiple parameters obtained on 3T pulsed arterial spin-labeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading[J]. AJNR Am J Neuroradiol, 2014, 35(11): 2091-2098. DOI: 10.3174/ajnr.A4018.
[41]
SERVER A, GRAFF B A , JOSEFSEN R, et al. Analysis of diffusion tensor imaging metrics for gliomas grading at 3 T[J/OL]. Eur J Radiol, 2014, 83(3): e156-e165 [2023-12-10]. https://pubmed.ncbi.nlm.nih.gov/24457139/. DOI: 10.1016/j.ejrad.2013.12.023.
[42]
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]. J Neurosurg Pediatr, 2008, 1(4): 263-269. DOI: 10.3171/PED/2008/1/4/263.
[43]
GAO A, ZHANG H, YAN X, et al. Whole-tumor histogram analysis of multiple diffusion metrics for glioma genotyping[J]. Radiology, 2022, 302(3): 652-661. DOI: 10.1148/radiol.210820.
[44]
El-SEROUGY L, ABDEL RAZEK A A, EZZATZZAT A, et al. Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas[J]. Neuroradiol J, 2016, 29(5): 400-407. DOI: 10.1177/1971400916665382.
[45]
MILOUSHEV V Z, CHOW D S, FILIPPI C G. Meta-analysis of diffusion metrics for the prediction of tumor grade in gliomas[J]. AJNR Am J Neuroradiol, 2015, 36(2): 302-308. DOI: 10.3174/ajnr.A4097.
[46]
GOEBELL E, PAUSTENBACH S, VAETERLEIN O, et al. Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor MR imaging[J]. Radiology, 2006, 239(1): 217-222. DOI: 10.1148/radiol.2383050059.
[47]
LIN K, CIDAN W, QI Y, et al. Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton magnetic resonance spectroscopy and diffusion tensor imaging[J]. Med Phys, 2022, 49(7): 4419-4429. DOI: 10.1002/mp.15648.
[48]
WANG P, XIE S, WU Q, et al. Model incorporating multiple diffusion MRI features: development and validation of a radiomics-based model to predict adult-type diffuse gliomas grade[J]. Eur Radiol, 2023, 33(12): 8809-8820. DOI: 10.1007/s00330-023-09861-0.
[49]
ZHANG Z, XIAO J, WU S, et al. Deep convolutional radiomic features on diffusion tensor images for classification of glioma grades[J]. J Digit Imaging, 2020, 33(4): 826-837. DOI: 10.1007/s10278-020-00322-4.
[50]
TAN W L, HUANG W Y, YIN B, et al. Can diffusion tensor imaging noninvasively detect IDH1 gene mutations in astrogliomas? A retrospective study of 112 cases[J]. AJNR Am J Neuroradiol, 2014, 35(5): 920-927. DOI: 10.3174/ajnr.A3803.
[51]
XIONG J, TAN W L, PAN J W, et al. Detecting isocitrate dehydrogenase gene mutations in oligodendroglial tumors using diffusion tensor imaging metrics and their correlations with proliferation and microvascular density[J]. J Magn Reson Imaging, 2016, 43(1): 45-54. DOI: 10.1002/jmri.24958.
[52]
LUO Z Q, LIU X X, LI J R, et al. Prediction of world health organization grade Ⅱ/Ⅲ diffuse gliomas IDH status by histogram analysis of diffusion tensor imaging[J]. J Clin Radiol, 2021, 40(5): 870-874. DOI: 10.13437/j.cnki.jcr.2021.05.008.
[53]
KOIVUNEN P, LEE S, DUNCAN C G, et al. Transformation by the (R)-enantiomer of 2-hydroxyglutarate linked to EGLN activation[J]. Nature, 2012, 483(7390): 484-488. DOI: 10.1038/nature10898.
[54]
AUGELLI R, CICERI E, GHIMENTON C, et al. Magnetic resonance diffusion-tensor imaging metrics in High Grade Gliomas: Correlation with IDH1 gene status in WHO 2016 era[J]. Eur J Radiol, 2019, 116: 174-179. DOI: 10.1016/j.ejrad.2019.04.020.
[55]
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.
[56]
YUZKAN S, MUTLU S, HAN M, et al. Predicting isocitrate dehydrogenase mutation status of Grade 2-4 gliomas with diffusion tensor imaging (DTI) parameters derived from model-based DTI and model-free Q-sampling imaging reconstructions[J/OL]. World Neurosurg, 2023, 28: S1878-8750(23)00882-3 [2023-12-10]. https://pubmed.ncbi.nlm.nih.gov/37390902/. DOI: 10.1016/j.wneu.2023.06.099.
[57]
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.
[58]
PARK C J, CHOI Y S, PARK Y W, et al. Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status[J]. Neuroradiology, 2020, 62(3): 319-326. DOI: 10.1007/s00234-019-02312-y.
[59]
ALIOTTA E, NOURZADEH H, BATCHALA P P, et al. Molecular subtype classification in lower-grade glioma with accelerated DTI[J]. AJNR Am J Neuroradiol, 2019, 40(9): 1458-1463. DOI: 10.3174/ajnr.A6162.
[60]
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/OL]. Front Oncol, 2021, 11: 627202 [2023-12-10]. https://pubmed.ncbi.nlm.nih.gov/33777772/. DOI: 10.3389/fonc.2021.627202.
[61]
PARK Y W, HAN K, AHN S S, et al. Whole-tumor histogram and texture analyses of DTI for evaluation of IDH1-mutation and 1p/19q-codeletion status in World Health Organization Grade II Gliomas[J]. AJNR Am J Neuroradiol, 2018, 39(4): 693-698. DOI: 10.3174/ajnr.A5569.
[62]
MOON W J, CHOI J W, ROH H G, et al. Imaging parameters of high grade gliomas in relation to the MGMT promoter methylation status: the CT, diffusion tensor imaging, and perfusion MR imaging[J]. Neuroradiology, 2012, 54(6): 555-563. DOI: 10.1007/s00234-011-0947-y.
[63]
AHN S S, SHIN N Y, CHANG J H, et al. Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging[J]. J Neurosurg, 2014, 121(2): 367-373. DOI: 10.3171/2014.5.JNS132279.
[64]
LATYSHEVA A, GEIER O M, HOPE T R, et al. Diagnostic utility of Restriction Spectrum Imaging in the characterization of the peritumoral brain zone in glioblastoma: Analysis of overall and progression-free survival[J/OL]. Eur J Radiol, 2020, 132: 109289 [2023-12-10]. https://pubmed.ncbi.nlm.nih.gov/33002815/. DOI: 10.1016/j.ejrad.2020.109289.
[65]
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.

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