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Research progress of magnetic resonance radiomics in predicting the methylation status of MGMT promoter in glioma
LI Ding  WANG Xiaochun 

Cite this article as: LI D, WANG X C. Research progress of magnetic resonance radiomics in predicting the methylation status of MGMT promoter in glioma[J]. Chin J Magn Reson Imaging, 2023, 14(12): 146-150, 171. DOI:10.12015/issn.1674-8034.2023.12.026.


[Abstract] Glioma, accounting for approximately 75% of primary malignant tumors in the central nervous system (CNS), is the most common primary malignant CNS tumor and is associated with a poor prognosis. Temozolomide (TMZ) is a first-line chemotherapy drug used for glioma treatment, and its effectiveness is closely linked to the methylation status of O6-methylguanine-DNA methyltransferase (MGMT). Currently, the detection of MGMT promoter methylation status primarily relies on pyrosequencing, which is time-consuming and susceptible to tumor heterogeneity. In recent years, MRI radiomics and deep learning (DL) have been greatly developed, and the high-throughput data mining approach has partially addressed the issue of tumor heterogeneity. This article provides a review of the latest research progress and limitations of MRI radiomics and DL-based MRI radiomics, with the aim to inspire further research in order to achieve personalized and precise diagnosis and treatment for glioma patients.
[Keywords] glioma；O6-methylguanine-DNA methyltransferase；magnetic resonance imaging；radiomics；deep learning；temozolomide

LI Ding1   WANG Xiaochun2*  

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: WANG X C, E-mail: 2010xiaochun@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81971592).
Received  2023-08-27
Accepted  2023-12-02
DOI: 10.12015/issn.1674-8034.2023.12.026
Cite this article as: LI D, WANG X C. Research progress of magnetic resonance radiomics in predicting the methylation status of MGMT promoter in glioma[J]. Chin J Magn Reson Imaging, 2023, 14(12): 146-150, 171. DOI:10.12015/issn.1674-8034.2023.12.026.

[1]
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.
[2]
RIVERA A L, PELLOSKI C E, GILBERT M R, et al. MGMT promoter methylation is predictive of response to radiotherapy and prognostic in the absence of adjuvant alkylating chemotherapy for glioblastoma[J]. Neuro Oncol, 2010, 12(2): 116-121. DOI: 10.1093/neuonc/nop020.
[3]
CHER L M, HAFEEZ U. Biomarkers and smart intracranial devices for the diagnosis, treatment, and monitoring of high-grade gliomas: a review of the literature and future prospects[J/OL]. Neurooncol Adv, 2019, 1(1): vdz013 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/32642651/. DOI: 10.1093/noajnl/vdz013.
[4]
YAMASHITA S, YOKOGAMI K, MATSUMOTO F, et al. MGMT promoter methylation in patients with glioblastoma: is methylation-sensitive high-resolution melting superior to methylation-sensitive polymerase chain reaction assay?[J]. J Neurosurg, 2019, 130(3): 780-788. DOI: 10.3171/2017.11.JNS171710.
[5]
VILLALVA C, CORTES U, WAGER M, et al. O6-Methylguanine- methyltransferase (MGMT) promoter methylation status in glioma stem-like cells is correlated to temozolomide sensitivity under differentiation-promoting conditions[J]. Int J Mol Sci, 2012, 13(6): 6983-6994. DOI: 10.3390/ijms13066983.
[6]
CHEN X, YAN Y, ZHOU J, et al. Clinical prognostic value of isocitrate dehydrogenase mutation, O-6-methylguanine-DNA methyltransferase promoter methylation, and 1p19q co-deletion in glioma patients[J/OL]. Ann Transl Med, 2019, 7(20): 541 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/31807523/. DOI: 10.21037/atm.2019.09.126.
[7]
KIYOKAWA J, WAKIMOTO H. Preclinical and clinical development of oncolytic adenovirus for the treatment of malignant glioma[J]. Oncolytic Virother, 2019, 8: 27-37. DOI: 10.2147/OV.S196403.
[8]
ZHOU M, NIU C, JIA L, et al. The value of MGMT promote methylation and IDH-1 mutation on diagnosis of pseudoprogression in patients with high-grade glioma: A meta analysis[J/OL]. Medicine (Baltimore), 2019, 98(50): e18194 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/31852075/. DOI: 10.1097/MD.0000000000018194.
[9]
SAXENA S, JENA B, GUPTA N, et al. Role of artificial intelligence in radiogenomics for cancers in the era of precision medicine[J/OL]. Cancers (Basel), 2022, 14(12): 2860 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/35740526/. DOI: 10.3390/cancers14122860.
[10]
DO D T, YANG M R, LAM L H T, et al. Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach[J/OL]. Sci Rep, 2022, 12(1): 13412 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/35927323/. DOI: 10.1038/s41598-022-17707-w.
[11]
HAUBOLD J, HOSCH R, PARMAR V, et al. Fully automated MR based virtual biopsy of cerebral gliomas[J/OL]. Cancers (Basel), 2021, 13(24): 6186 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/34944806/. DOI: 10.3390/cancers13246186.
[12]
LU Y, PATEL M, NATARAJAN K, et al. Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma[J]. Magn Reson Imaging, 2020, 74: 161-170. DOI: 10.1016/j.mri.2020.09.017.
[13]
HAJIANFAR G, SHIRI I, MALEKI H, et al. Noninvasive O(6) methylguanine-DNA methyltransferase status prediction in glioblastoma multiforme cancer using magnetic resonance imaging radiomics features: Univariate and multivariate radiogenomics analysis[J/OL]. World Neurosurg, 2019, 132: e140-e161 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/31505292/. DOI: 10.1016/j.wneu.2019.08.232.
[14]
PEASE M, GERSEY Z C, AK M, et al. Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients[J]. J Neurooncol, 2022, 160(1): 253-263. DOI: 10.1007/s11060-022-04150-0.
[15]
XI Y B, GUO F, XU Z L, et al. Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma[J]. J Magn Reson Imaging, 2018, 47(5): 1380-1387. DOI: 10.1002/jmri.25860.
[16]
HUANG W Y, WEN L H, WU G, et al. Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis[J]. Cancer Sci, 2021, 112(7): 2835-2844. DOI: 10.1111/cas.14918.
[17]
SHA Y J, WANG X C, TAN Y, et al. The value of predicting the subtype of IDH mutation combining with MGMT promoter methylation in lower grade gliomas by radiomics based on preoperative MRI[J]. Chin J Magn Reson Imaging, 2022, 13(7): 6-11. DOI: 10.12015/issn.1674-8034.2022.07.002.
[18]
JIANG C, KONG Z, LIU S, et al. Fusion radiomics features from conventional MRI predict MGMT promoter methylation status in lower grade gliomas[J/OL]. Eur J Radiol, 2019, 121: 108714 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/31704598/. DOI: 10.1016/j.ejrad.2019.108714.
[19]
LI Z C, BAI H, SUN Q, et al. Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study[J]. Eur Radiol, 2018, 28(9): 3640-3650. DOI: 10.1007/s00330-017-5302-1.
[20]
ZHANG S, SUN H, SU X, et al. Automated machine learning to predict the co-occurrence of isocitrate dehydrogenase mutations and O(6)-methylguanine-DNA methyltransferase promoter methylation in patients with gliomas[J]. J Magn Reson Imaging, 2021, 54(1): 197-205. DOI: 10.1002/jmri.27498.
[21]
CHEN R, SMITH-COHN M, COHEN A L, et al. Glioma subclassifications and their clinical significance[J]. Neurotherapeutics, 2017, 14(2): 284-297. DOI: 10.1007/s13311-017-0519-x.
[22]
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.
[23]
BERGER T R, WEN P Y, LANG-ORSINI M, et al. World Health Organization 2021 Classification of Central Nervous System tumors and implications for therapy for adult-type gliomas: A review[J]. JAMA Oncol, 2022, 8(10): 1493-1501. DOI: 10.1001/jamaoncol.2022.2844.
[24]
SHA Y, YAN Q, TAN Y, et al. Prediction of the molecular subtype of IDH mutation combined with MGMT promoter methylation in gliomas via radiomics based on preoperative MRI[J/OL]. Cancers (Basel), 2023, 15(5): 1440 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/36900232/. DOI: 10.3390/cancers15051440.
[25]
WIRSCHING H G, GALANIS E, WELLER M. Glioblastoma[J]. Handb Clin Neurol, 2016, 134: 381-397. DOI: 10.1016/B978-0-12-802997-8.00023-2.
[26]
COHEN A L, HOLMEN S L, COLMAN H. IDH1 and IDH2 mutations in gliomas[J/OL]. Curr Neurol Neurosci Rep, 2013, 13(5): 345 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/23532369/. DOI: 10.1007/s11910-013-0345-4.
[27]
YAN H, PARSONS D W, JIN G, et al. IDH1 and IDH2 mutations in gliomas[J]. N Engl J Med, 2009, 360(8): 765-773. DOI: 10.1056/NEJMoa0808710.
[28]
DRAKE-PEREZ M, BOTO J, FITSIORI A, et al. Clinical applications of diffusion weighted imaging in neuroradiology[J]. Insights Imaging, 2018, 9(4): 535-547. DOI: 10.1007/s13244-018-0624-3.
[29]
SUNWOO L, CHOI S H, PARK C K, et al. Correlation of apparent diffusion coefficient values measured by diffusion MRI and MGMT promoter methylation semiquantitatively analyzed with MS-MLPA in patients with glioblastoma multiforme[J]. J Magn Reson Imaging, 2013, 37(2): 351-358. DOI: 10.1002/jmri.23838.
[30]
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.
[31]
CINDIL E, SENDUR H N, CERIT M N, et al. Prediction of IDH mutation status in high-grade gliomas using DWI and high T1-weight DSC-MRI[J/OL]. Acad Radiol, 2022, 29 (Suppl 3): S52-S62 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/33685792/. DOI: 10.1016/j.acra.2021.02.002.
[32]
WEI J, YANG G, HAO X, et al. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication[J]. Eur Radiol, 2019, 29(2): 877-888. DOI: 10.1007/s00330-018-5575-z.
[33]
KIHIRA S, TSANKOVA N M, BAUER A, et al. Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion[J/OL]. Neurooncol Adv, 2021, 3(1): vdab051 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/34056604/. DOI: 10.1093/noajnl/vdab051.
[34]
HE J, REN J, NIU G, et al. Multiparametric MR radiomics in brain glioma: models comparation to predict biomarker status[J/OL]. BMC Med Imaging, 2022, 22(1): 137 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/35931979/. DOI: 10.1186/s12880-022-00865-8.
[35]
CHEN S X, XU Y, YE M P, et al. Prediction of MGMT promoter methylation in gliomas with different radiomics models based on MRI[J]. Chin J Magn Reson Imaging, 2022, 13(3): 1-5, 36. DOI: 10.12015/issn.1674-8034.2022.03.001.
[36]
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.
[37]
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 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/33307459/. DOI: 10.1016/j.ejrad.2020.109466.
[38]
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 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/32361603/. DOI: 10.1016/j.ejrad.2020.108985.
[39]
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 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/32004731/. DOI: 10.1016/j.ejrad.2019.108785.
[40]
YANG Y, YANG Y, WU X, et al. Adding DSC PWI and DWI to BT-RADS can help identify postoperative recurrence in patients with high-grade gliomas[J]. J Neurooncol, 2020, 146(2): 363-371. DOI: 10.1007/s11060-019-03387-6.
[41]
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.
[42]
CRISI G, FILICE S. Predicting MGMT promoter methylation of glioblastoma from dynamic susceptibility contrast perfusion: A radiomic approach[J]. J Neuroimaging, 2020, 30(4): 458-462. DOI: 10.1111/jon.12724.
[43]
DUNN J, BABORIE A, ALAM F, et al. Extent of MGMT promoter methylation correlates with outcome in glioblastomas given temozolomide and radiotherapy[J]. Br J Cancer, 2009, 101(1): 124-131. DOI: 10.1038/sj.bjc.6605127.
[44]
KORFIATIS P, KLINE T L, LACHANCE D H, et al. Residual deep convolutional neural network predicts MGMT methylation status[J]. J Digit Imaging, 2017, 30(5): 622-628. DOI: 10.1007/s10278-017-0009-z.
[45]
SINGH S P, WANG L, GUPTA S, et al. 3D deep learning on medical images: A review[J/OL]. Sensors (Basel), 2020, 20(18): 5097 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/32906819/. DOI: 10.3390/s20185097.
[46]
ZHONG S, REN J X, YU Z P, et al. Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics[J]. J Neurosurg, 2022: 1-10. DOI: 10.3171/2022.10.JNS22801.
[47]
YOGANANDA C G B, SHAH B R, NALAWADE S S, et al. MRI-based deep-learning method for determining glioma MGMT promoter methylation status[J]. AJNR Am J Neuroradiol, 2021, 42(5): 845-852. DOI: 10.3174/ajnr.A7029.
[48]
CHANG P, GRINBAND J, WEINBERG B D, et al. Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas[J]. AJNR Am J Neuroradiol, 2018, 39(7): 1201-1207. DOI: 10.3174/ajnr.A5667.
[49]
CHEN X, ZENG M, TONG Y, et al. Automatic prediction of MGMT status in glioblastoma via deep learning-based MR image analysis[J/OL]. Biomed Res Int, 2020, 2020: 9258649 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/33029531/. DOI: 10.1155/2020/9258649.
[50]
CHEN S, XU Y, YE M, et al. Predicting MGMT promoter methylation in diffuse gliomas using deep learning with radiomics[J/OL]. J Clin Med, 2022, 11(12): 3445 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/35743511/. DOI: 10.3390/jcm11123445.
[51]
CALABRESE E, RUDIE J D, RAUSCHECKER A M, et al. Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma[J/OL]. Neurooncol Adv, 2022, 4(1): vdac060 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/35611269/. DOI: 10.1093/noajnl/vdac060.
[52]
SAXENA S, JENA B, MOHAPATRA B, et al. Fused deep learning paradigm for the prediction of O6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation[J/OL]. Comput Biol Med, 2023, 153: 106492 [2023-08-27]. https://pubmed.ncbi.nlm.nih.gov/36621191/. DOI: 10.1016/j.compbiomed.2022.106492.

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