1
|
Louis DN, Perry A, Reifenberger G, von
Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD,
Kleihues P and Ellison DW: The 2016 world health organization
classification of tumors of the central nervous system: A summary.
Acta Neuropathol. 131:803–820. 2016.PubMed/NCBI View Article : Google Scholar
|
2
|
Louis DN, Perry A, Wesseling P, Brat DJ,
Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM,
Reifenberger G, et al: The 2021 WHO classification of tumors of the
central nervous system: A summary. Neuro-Oncology. 23:1231–1251.
2021.PubMed/NCBI View Article : Google Scholar
|
3
|
Ostrom QT, Gittleman H, Fulop J, Liu M,
Blanda R, Kromer C, Wolinsky Y, Kruchko C and Barnholtz-Sloan JS:
CBTRUS statistical report: Primary brain and central nervous system
tumors diagnosed in the United States in 2008-2012. Neuro Oncol. 17
(Suppl):iv1–iv62. 2015.PubMed/NCBI View Article : Google Scholar
|
4
|
Della Monica R, Cuomo M, Buonaiuto M,
Costabile D, Franca RA, Del Basso De Caro M, Catapano G, Chiariotti
L and Visconti R: MGMT and whole-genome DNA methylation impacts on
diagnosis, prognosis and therapy of glioblastoma multiforme. Int J
Mol Sci. 23(7148)2022.PubMed/NCBI View Article : Google Scholar
|
5
|
Teske N, Karschnia P, Weller J, Siller S,
Dorostkar MM, Herms J, von Baumgarten L, Tonn JC and Thon N:
Extent, pattern, and prognostic value of MGMT promotor methylation:
Does it differ between glioblastoma and IDH-wildtype/TERT-mutated
astrocytoma? J Neurooncol. 156:317–327. 2022.PubMed/NCBI View Article : Google Scholar
|
6
|
Min TL, Allen JW, Velazquez Vega JE, Neill
SG and Weinberg BD: MRI imaging characteristics of glioblastoma
with concurrent gain of chromosomes 19 and 20. Tomography.
7:228–237. 2021.PubMed/NCBI View Article : Google Scholar
|
7
|
Pease M, Gersey ZC, Ak M, Elakkad A,
Kotrotsou A, Zenkin S, Elshafeey N, Mamindla P, Kumar VA, Kumar AJ,
et al: Pre-operative MRI radiomics model non-invasively predicts
key genomic markers and survival in glioblastoma patients. J
Neurooncol. 160:253–263. 2022.PubMed/NCBI View Article : Google Scholar
|
8
|
Zhang M, Chen HZ, Cui YY, Zhang ZZ and Ma
XD: The associations between preoperative conventional MRI features
and genetic biomarker status in newly diagnosed GBMs: A clinical
summary and prognostic analysis. Turk Neurosurg. 31:880–887.
2021.PubMed/NCBI View Article : Google Scholar
|
9
|
Do DT, Yang MR, Lam LHT, Le NQK and Wu YW:
Improving MGMT methylation status prediction of glioblastoma
through optimizing radiomics features using genetic algorithm-based
machine learning approach. Sci Rep. 12(13412)2022.PubMed/NCBI View Article : Google Scholar
|
10
|
Verduin M, Primakov S, Compter I, Woodruff
HC, van Kuijk SMJ, Ramaekers BLT, te Dorsthorst M, Revenich EGM,
ter Laan M, Pegge SAH, et al: Prognostic and predictive value of
integrated qualitative and quantitative magnetic resonance imaging
analysis in glioblastoma. Cancers (Basel). 13(722)2021.PubMed/NCBI View Article : Google Scholar
|
11
|
Safaei R, Mojtahedi H, Hanaei S, Razavi A,
Esmaeili M, Sadr M, Rezaei A, Edalatfar M, Kashani HK,
Sadeghi-Naini M, et al: MGMT gene rs1625649 polymorphism in Iranian
patients with brain glioblastoma: A case control study. Avicenna J
Med Biotechnol. 15:48–52. 2023.PubMed/NCBI View Article : Google Scholar
|
12
|
Kitange GJ, Mladek AC, Carlson BL,
Schroeder MA, Pokorny JL, Cen L, Decker PA, Wu W, Lomberk GA, Gupta
SK, et al: Inhibition of histone deacetylation potentiates the
evolution of acquired temozolomide resistance linked to MGMT
upregulation in glioblastoma xenografts. Clin Cancer Res.
18:4070–4079. 2012.PubMed/NCBI View Article : Google Scholar
|
13
|
Blakstad H, Brekke J, Rahman MA, Arnesen
VS, Miletic H, Brandal P, Lie SA, Chekenya M and Goplen D: Survival
in a consecutive series of 467 glioblastoma patients: Association
with prognostic factors and treatment at recurrence at two
independent institutions. PLoS One. 18(e0281166)2023.PubMed/NCBI View Article : Google Scholar
|
14
|
Kurdi M, Shafique Butt N, Baeesa S,
Alghamdi B, Maghrabi Y, Bardeesi A, Saeedi R, Al-Sinani T, Alghanmi
N, Bari MO, et al: The impact of IDH1 mutation and MGMT promoter
methylation on recurrence-free interval in glioblastoma patients
treated with radiotherapy and chemotherapeutic agents. Pathol Oncol
Res. 27(1609778)2021.PubMed/NCBI View Article : Google Scholar
|
15
|
Haque W, Thong E, Andrabi S, Verma V,
Butler BE and the BS: Prognostic and predictive impact of MGMT
promoter methylation in grade 3 gliomas. J Clin Neurosci.
85:115–121. 2021.PubMed/NCBI View Article : Google Scholar
|
16
|
Davnall F, Yip CS, Ljungqvist G, Selmi M,
Ng F, Sanghera B, Ganeshan B, Miles KA, Cook GJ and Goh V:
Assessment of tumor heterogeneity: An emerging imaging tool for
clinical practice? Insights Imaging. 3:573–589. 2012.PubMed/NCBI View Article : Google Scholar
|
17
|
Ladenhauf VK, Galijasevic M, Kerschbaumer
J, Freyschlag CF, Nowosielski M, Birkl-Toeglhofer AM, Haybaeck J,
Gizewski ER, Mangesius S and Grams AE: Peritumoral ADC values
correlate with the MGMT methylation status in patients with
glioblastoma. Cancers (Basel). 15(1384)2023.PubMed/NCBI View Article : Google Scholar
|
18
|
Qureshi SA, Hussain L, Ibrar U,
Alabdulkreem E, Nour MK, Alqahtani MS, Nafie FM, Mohamed A,
Mohammed GP and Duong TQ: Radiogenomic classification for MGMT
promoter methylation status using multi-omics fused feature space
for least invasive diagnosis through mpMRI scans. Sci Rep.
13(3291)2023.PubMed/NCBI View Article : Google Scholar
|
19
|
Eccles SA and Welch DR: Metastasis: Recent
discoveries and novel treatment strategies. Lancet. 369:1742–1757.
2007.PubMed/NCBI View Article : Google Scholar
|
20
|
Saxena S, Jena B, Mohapatra B, Gupta N,
Kalra M, Scartozzi M, Saba L and Suri JS: Fused deep learning
paradigm for the prediction of O6-methylguanine-DNA
methyltransferase genotype in glioblastoma patients: A
neuro-oncological investigation. Comput Biol Med.
153(106492)2023.PubMed/NCBI View Article : Google Scholar
|
21
|
Choi HJ, Choi SH, You SH, Yoo RE, Kang KM,
Yun TJ, Kim JH, Sohn CH, Park CK and Park SH: MGMT promoter
methylation status in initial and recurrent glioblastoma:
Correlation study with DWI and DSC PWI features. AJNR Am J
Neuroradiol. 42:853–860. 2021.PubMed/NCBI View Article : Google Scholar
|
22
|
Yaltirik CK, Yilmaz SG, Ozdogan S, Bilgin
EY, Barut Z, Ture U and Isbir T: Determination of IDH1, IDH2, MGMT,
TERT and ATRX gene mutations in glial tumors. In Vivo.
36:1694–1702. 2022.PubMed/NCBI View Article : Google Scholar
|
23
|
Ius T, Pignotti F, Della Pepa GM, Bagatto
D, Isola M, Battistella C, Gaudino S, Pegolo E, Chiesa S, Arcicasa
M, et al: Glioblastoma: From volumetric analysis to molecular
predictors. J Neurosurg Sci. 66:173–186. 2022.PubMed/NCBI View Article : Google Scholar
|
24
|
Sanai N, Alvarez-Buylla A and Berger MS:
Neural stem cells and the origin of gliomas. N Engl J Med.
353:811–822. 2005.PubMed/NCBI View Article : Google Scholar
|
25
|
Li HY, Sun CR, He M, Yin LC, Du HG and
Zhang JM: Correlation between tumor location and clinical
properties of glioblastomas in frontal and temporal lobes. World
Neurosurg. 112:e407–e414. 2018.PubMed/NCBI View Article : Google Scholar
|
26
|
Drabycz S, Roldán G, Robles P, Adler D,
Mcintyre J, Magliocco A, Cairncross JG and Mitchell JR: An analysis
of image texture, tumor location, and MGMT promoter methylation in
glioblastoma using magnetic resonance imaging. Neuroimage.
49:1398–1405. 2010.PubMed/NCBI View Article : Google Scholar
|
27
|
Kickingereder P, Bonekamp D, Nowosielski
M, Kratz A, Sill M, Burth S, Wick A, Eidel O, Schlemmer HP,
Radbruch A, et al: Radiogenomics of glioblastoma: Machine
learning-based classification of molecular characteristics by using
multiparametric and multiregional MR imaging features. Radiology.
281:907–918. 2016.PubMed/NCBI View Article : Google Scholar
|
28
|
Egger J, Kapur T, Fedorov A, Pieper S,
Miller JV, Veeraraghavan H, Freisleben B, Golby AJ, Nimsky C and
Kikinis R: GBM volumetry using the 3D slicer medical image
computing platform. Sci Rep. 3(1364)2013.PubMed/NCBI View Article : Google Scholar
|