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Research progress of radiomics in predicting the genotyping of gliomas
DONG Qingrong  WANG Xiaochun  TAN Yan  ZHANG Hui 

Cite this article as: Dong QR, Wang XC, Tan Y, et al. Research progress of radiomics in predicting the genotyping of gliomas[J]. Chin J Magn Reson Imaging, 2021, 12(2): 88-90. DOI:10.12015/issn.1674-8034.2021.02.021.


[Abstract] Glioma is a common primary brain tumor in adults. Genotyping plays an important role in prognosis analysis and personalized treatment of glioma patients. Therefore, noninvasive prediction of glioma genotyping before operation has become a hot research topic. MRI-based radiomics has the potential to broadly characterize intratumoral heterogeneity, predict glioma related genotypes, and demonstrate a good supporting role in clinical guidance. This article reviews the progress of radiomics in predicting the genotypes of gliomas.
[Keywords] glioma；genotyping；radiomics；magnetic resonance imaging

DONG Qingrong1   WANG Xiaochun2   TAN Yan2   ZHANG Hui2*  

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

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

Zhang H, E-mail: zhanghui_mr@163.com

Conflicts of interest   None.

ACKNOWLEDGENTS This work was part of National Natural Science Foundation of China (No.81971593, 81771824).
Received  2020-08-24
Accepted  2021-01-12
DOI: 10.12015/issn.1674-8034.2021.02.021
Cite this article as: Dong QR, Wang XC, Tan Y, et al. Research progress of radiomics in predicting the genotyping of gliomas[J]. Chin J Magn Reson Imaging, 2021, 12(2): 88-90. DOI:10.12015/issn.1674-8034.2021.02.021.

1
Garcia CR, Slone SA, Dolecek TA, et al. Primary central nervous system tumor treatment and survival in the United States, 2004-2015[J]. J Neurooncol, 2019, 144(1): 179-191. DOI: 10.1007/s11060-019-03218-8.
2
David AR, Patrick YW. Glioma in 2014: unravelling tumour heterogeneity-implications for therapy[J]. 2015, 12(2): 69-70. DOI: 10.1038/nrclinonc.2014.223.
3
Louis DN, 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
Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
5
Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges[J]. Theranostics, 2019, 9(5): 1303-1322. DOI: 10.7150/thno.30309.
6
Artzi M, Bressler I, Ben Bashat D. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis[J]. J Magn Reson Imaging, 2019, 50(2): 519-528. DOI: 10.1002/jmri.26643.
7
Takahara T, Hendrikse J, Yamashita T, et al. Diffusion-weighted MR neurography of the brachial plexus: feasibility study[J]. Radiology, 2008, 249(2): 653-660. DOI: 10.1148/radiol.2492071826.
8
Emblem KE, Pinho MC, Zollenr FG, et al. A generic support vector machine model for preoperative glioma survival associations[J]. Radiology, 2015, 275(1): 228-234. DOI: 10.1148/radiol.14140770.
9
Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways[J]. Nature, 2008, 455(7216): 1061-1068. DOI: 10.1038/nature07385.
10
Belden CJ, Valdes PA, Ran C, et al. Genetics of glioblastoma: a window into its imaging and histopathologic variability[J]. Radiographics, 2011, 31(6): 1717-1740. DOI: 10.1148/rg.316115512.
11
Aldape K, Zadeh G, Mansouri S, et al. Glioblastoma: pathology, molecular mechanisms and markers[J]. Acta Neuropathol, 2015, 129(6): 829-848. DOI: 10.1007/s00401-015-1432-1.
12
Bujko M, Kober P, Matyja E, et al. Prognostic value of IDH1 mutations identified with PCR-RFLP assay in glioblastoma patients[J]. Mol Diagn Ther, 2010, 14(3): 163-169. DOI: 10.1007/BF03256369.
13
Yan H, Parsons DW, Jin G, et al. IDH1 and IDH2 mutations in gliomas[J]. N Engl J Med, 2009, 360(8): 765-773. DOI: 10.1056/NEJMoa0808710.
14
Parsons DW, Jones S, Zhang X, et al. An integrated genomic analysis of human glioblastoma multiforme[J]. Science, 2008, 321(5897): 1807-1812. DOI: 10.1126/science.1164382.
15
Rohle D, Popovici-Muller J, Palaskas N, et al. An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells[J]. Science, 2013, 340(6132): 626-630. DOI: 10.1126/science.1236062.
16
Yu J, Shi Z, Lian Y, et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma[J]. Eur Radiol, 2017, 27(8): 3509-3522. DOI: 10.1007/s00330-016-4653-3.
17
Tan Y, Zhang ST, Wei JW, et al. A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery[J]. Eur Radiol, 2019, 29(7): 3325-3337. DOI: 10.1007/s00330-019-06056-4.
18
Wang Q, Li Q, Mi R, et al. Radiomics nomogram building from multiparametric MRI to predict grade in patients with glioma: A cohort study[J]. J Magn Reson Imaging, 2019, 49(3): 825-833. DOI: 10.1002/jmri.26265.
19
Kim M, Jung SY, Park JE, et al. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma[J]. Eur Radiol, 2020, 30(4): 2142-2151. DOI: 10.1007/s00330-019-06548-3.
20
Park CJ, Choi YS, Park YW, 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.
21
Lohmann P, Kocher M, Steger J, et al. Radiomics derived from amino-acid PET and conventional MRI in patients with high-grade gliomas[J]. Q J Nucl Med Mol Imaging, 2018, 62(3): 272-280. DOI: 10.23736/S1824-4785.18.03095-9.
22
Lohmann P, Lerche C, Bauer EK, et al. Predicting IDH genotype in gliomas using FET PET radiomics[J]. Sci Rep, 2018, 8(1): 13328. DOI: 10.1038/s41598-018-31806-7.
23
Arita H, Kinoshita M, Kawaguchi A, et al. Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas[J]. Sci Rep, 2018, 8(1): 11773. DOI: 10.1038/s41598-018-30273-4.
24
Alice P, Giulia P, Anna T, et al. Specific biomarkers are associated with docetaxeland gemcitabine-resistant NSCLC cell lines[J]. Transl Oncol, 2012, 56(6): 461-468. DOI: 10.1593/tlo.12256.
25
Drabycz S, Roldan G, Robles P, et al. An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging[J]. Neuroimage, 2010, 49(2): 1398-1405. DOI: 10.1016/j.neuroimage.2009.09.049.
26
Korfiatis P, Kline TL, Coufalova L, et al. MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas[J]. Med Phys, 2016, 43(6): 2835-2844. DOI: 10.1118/1.4948668.
27
Jiang C, Kong Z, Liu S, et al. Fusion radiomics features from conventional MRI predict MGMT promoter methylation status in lower grade gliomas[J]. Eur J Radiol, 2019, 121: 108714. DOI: 10.1016/j.ejrad.2019.108714.
28
Tan Y, Mu W, Wang XC, et al. Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: A preliminary study[J]. Eur J Radiol, 2020, 124: 108785. DOI: 10.1016/j.ejrad.2019.108785.
29
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.
30
Li ZC, 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.
31
Kaloshi G, Benouaich-Amiel A, Diakite F, et al. Temozolomide for low-grade gliomas: predictive impact of 1p/19q loss on response and outcome[J]. Neurology, 2007, 68(21): 1831-1836. DOI: 10.1212/01.wnl.0000262034.26310.a2.
32
Han Y, Xie Z, Zang Y, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas[J]. J Neurooncol, 2018, 140(2): 297-306. DOI: 10.1007/s11060-018-2953-y.
33
Park YW, Han K, Ahn SS, 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.
34
Lewis MA, Ganeshan B, Barnes A, et al. Filtration-histogram based magnetic resonance texture analysis (MRTA) for glioma IDH and 1p19q genotyping[J]. 2019, 113: 116-123. DOI: 10.1016/j.ejrad.2019.02.014.
35
Kanazawa T, Minami Y, Takahashi H, et al. Magnetic resonance imaging texture analyses in lower-grade gliomas with a commercially available software: correlation of apparent diffusion coefficient and T2 skewness with 1p/19q codeletion[J]. Neurosurgical Review, 2020, 43(4): 1211-1219. DOI: 10.1007/s10143-019-01157-6.
36
Kim D, Wang N, Ravikumar V, et al. Prediction of 1p/19q codeletion in diffuse glioma patients using pre-operative multiparametric magnetic resonance imaging[J]. Front Comput Neurosci, 2019, 13: 52. DOI: 10.3389/fncom.2019.00052.
37
Akkus Z, Ali I, Sedlar J, et al. Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence[J]. J Digit Imaging, 2017, 30(4): 469-476. DOI: 10.1007/s10278-017-9984-3.
38
Lu CF, Hsu FT, Hsieh KL, et al. Machine learning-based radiomics for molecular subtyping of gliomas[J]. Clin Cancer Res, 2018, 24(18): 4429-4436. DOI: 10.1158/1078-0432.CCR-17-3445.
39
Yang P, Cai J, Yan W, et al. Classification based on mutations of TERT promoter and IDH characterizes subtypes in grade II/III gliomas[J]. Neuro Oncol, 2016, 18(8): 1099-1108. DOI: 10.1093/neuonc/now021.
40
Koschmann C, Calinescu AA, Nunez FJ, et al. ATRX loss promotes tumor growth and impairs nonhomologous end joining DNA repair in glioma[J]. Sci Transl Med, 2016, 8(328): 328. DOI: 10.1126/scitranslmed.aac8228.
41
Jiao Y, Killela PJ, Reitman ZJ, et al. Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas[J]. Oncotarget, 2012, 3(7): 709-722. DOI: 10.18632/oncotarget.588.
42
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.
43
Chan AK, Yao Y, Zhang Z, et al. Combination genetic signature stratifies lower-grade gliomas better than histological grade[J]. Oncotarget, 2015, 6(25): 20885-20901. DOI: 10.18632/oncotarget.4928.
44
Zhang X, Tian Q, Wang L, et al. Radiomics strategy for molecular subtype stratification of lower-grade glioma: Detecting IDH and TP53 mutations based on multimodal MRI[J]. J Magn Reson Imaging, 2018, 48(4): 916-926. DOI: 10.1002/jmri.25960.

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