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Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Mini-Review Article

Review of Magnetic Resonance Imaging and Post-processing for the Brain Tumor-related Epilepsy Study

Author(s): Reuben George, Li Sze Chow*, Kheng Seang Lim, Christine Audrey, Norlisah Ramli and Li-Kuo Tan

Volume 20, 2024

Published on: 02 August, 2023

Article ID: e260423216214 Pages: 16

DOI: 10.2174/1573405620666230426150015

open_access

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Abstract

20% of brain tumor patients present with seizures at the onset of diagnosis, while a further 25-40% develop epileptic seizures as the tumor progresses. Tumor-related epilepsy (TRE) is a condition in which the tumor causes recurring, unprovoked seizures. The occurrence of TRE differs between patients, along with the effectiveness of treatment methods. Therefore, determining the tumor properties that correlate with epilepsy can help guide TRE treatment. This article reviews the MRI sequences and image post-processing algorithms in the study of TRE. It focuses on epilepsy caused by glioma tumors because it is the most common type of malignant brain tumor and it has a high prevalence of epilepsy. In correlational TRE studies, conventional MRI sequences and diffusion-weighted MRI (DWI) are used to extract variables related to the tumor radiological characteristics, called imaging factors. Image post-processing is used to correlate the imaging factors with the incidence of epilepsy. The earlier studies of TRE used univariate and multivariate analysis to study the correlations between specific variables and incidence of epilepsy. Later, studies used voxel-based morphometry and voxel lesion-symptom mapping. Radiomics has been recently used to post-process the images for the study of TRE. This article will discuss the limitation of the existing imaging modalities and post-processing algorithms. It ends with some suggestions and challenges for future TRE studies.

Keywords: Keywords: Glioma, MRI, Tumor-related epilepsy, Voxel-based morphometry, Lesion symptom mapping, Radiomics.

[1]
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68(6): 394-424.
[http://dx.doi.org/10.3322/caac.21492] [PMID: 30207593]
[2]
Miller KD, Ostrom QT, Kruchko C, et al. Brain and other central nervous system tumor statistics, 2021. CA Cancer J Clin 2021; 71(5): 381-406.
[http://dx.doi.org/10.3322/caac.21693] [PMID: 34427324]
[3]
Maschio M. Brain tumor-related epilepsy. Curr Neuropharmacol 2012; 10(2): 124-33.
[http://dx.doi.org/10.2174/157015912800604470] [PMID: 23204982]
[4]
Rosati A, Tomassini A, Pollo B, et al. Epilepsy in cerebral glioma: timing of appearance and histological correlations. J Neurooncol 2009; 93(3): 395-400.
[http://dx.doi.org/10.1007/s11060-009-9796-5] [PMID: 19183850]
[5]
Chandrashekhar TN, Mahadevan A, Vani S, et al. Pathological spectrum of neuronal/glioneuronal tumors from a tertiary referral neurological Institute. Neuropathology 2012; 32(1): 1-12.
[http://dx.doi.org/10.1111/j.1440-1789.2011.01206.x] [PMID: 21410777]
[6]
Forst DA, Nahed BV, Loeffler JS, Batchelor TT. Low-Grade Gliomas. Oncologist 2014; 19(4): 403-13.
[http://dx.doi.org/10.1634/theoncologist.2013-0345] [PMID: 24664484]
[7]
Wen PY, Kesari S. Malignant gliomas in adults. N Engl J Med 2008; 359(5): 492-507.
[http://dx.doi.org/10.1056/NEJMra0708126] [PMID: 18669428]
[8]
Fan X, Li Y, Shan X, et al. Seizures at presentation are correlated with better survival outcomes in adult diffuse glioma: A systematic review and meta-analysis. Seizure 2018; 59: 16-23.
[http://dx.doi.org/10.1016/j.seizure.2018.04.018] [PMID: 29727741]
[9]
Kerkhof M, Vecht CJ. Seizure characteristics and prognostic factors of gliomas. Epilepsia 2013; 54 (Suppl. 9): 12-7.
[http://dx.doi.org/10.1111/epi.12437] [PMID: 24328866]
[10]
Mastall M, Wolpert F, Gramatzki D, et al. Survival of brain tumour patients with epilepsy. Brain 2021; 144(11): 3322-7.
[http://dx.doi.org/10.1093/brain/awab188] [PMID: 33974079]
[11]
Wang Y, Qian T, You G, et al. Localizing seizure-susceptible brain regions associated with low-grade gliomas using voxel-based lesion-symptom mapping. Neuro-oncol 2015; 17(2): 282-8.
[http://dx.doi.org/10.1093/neuonc/nou130] [PMID: 25031032]
[12]
Cayuela N, Simó M, Majós C, et al. Seizure‐susceptible brain regions in glioblastoma: Identification of patients at risk. Eur J Neurol 2018; 25(2): 387-94.
[http://dx.doi.org/10.1111/ene.13518] [PMID: 29115706]
[13]
Soltani M, Bonakdar A, Shakourifar N, Babaei R, Raahemifar K. Efficacy of location-based features for survival prediction of patients with glioblastoma depending on resection status. Front Oncol 2021; 11: 661123.
[http://dx.doi.org/10.3389/fonc.2021.661123] [PMID: 34295809]
[14]
Cho H, Lee S, Kim J, Park H. Classification of the glioma grading using radiomics analysis. PeerJ 2018; 6(e5982): e5982.
[http://dx.doi.org/10.7717/peerj.5982] [PMID: 30498643]
[15]
Raghavendra U, Acharya UR, Adeli H. Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur Neurol 2019; 82(1-3): 41-64.
[http://dx.doi.org/10.1159/000504292] [PMID: 31743905]
[16]
Lee SB, Cho YJ, Jeon K, et al. Diagnosis of hippocampal sclerosis in children: Comparison of automated brain MRI volumetry and readers of varying experience. AJR Am J Roentgenol 2021; 217(1): 223-34.
[http://dx.doi.org/10.2214/AJR.20.23990] [PMID: 32903057]
[17]
Hu LS, Hawkins-Daarud A, Wang L, Li J, Swanson KR. Imaging of intratumoral heterogeneity in high-grade glioma. Cancer Lett 2020; 477: 97-106.
[http://dx.doi.org/10.1016/j.canlet.2020.02.025] [PMID: 32112907]
[18]
Pope WB, Brandal G. Conventional and advanced magnetic resonance imaging in patients with high-grade glioma. Q J Nucl Med Mol Imaging 2018; 62(3): 239-53.
[http://dx.doi.org/10.23736/S1824-4785.18.03086-8] [PMID: 29696946]
[19]
Ho ML, Rojas R, Eisenberg RL. Cerebral Edema. AJR Am J Roentgenol 2012; 199(3): W258-73.
[http://dx.doi.org/10.2214/AJR.11.8081] [PMID: 22915416]
[20]
Maldaun MVC, Suki D, Lang FF, et al. Cystic glioblastoma multiforme: Survival outcomes in 22 cases. J Neurosurg 2004; 100(1): 61-7.
[http://dx.doi.org/10.3171/jns.2004.100.1.0061] [PMID: 14743913]
[21]
Latini F, Axelson H, Fahlström M, et al. Role of preoperative assessment in predicting tumor-induced plasticity in patients with diffuse gliomas. J Clin Med 2021; 10(5): 1108.
[http://dx.doi.org/10.3390/jcm10051108] [PMID: 33799925]
[22]
Choi JY, Chang JW, Park YG, Kim TS, Lee BI, Chung SS. A retrospective study of the clinical outcomes and significant variables in the surgical treatment of temporal lobe tumor associated with intractable seizures. Stereotact Funct Neurosurg 2004; 82(1): 35-42.
[http://dx.doi.org/10.1159/000076659] [PMID: 15007218]
[23]
Toledo M, Sarria-Estrada S, Quintana M, et al. Epileptic features and survival in glioblastomas presenting with seizures. Epilepsy Res 2017; 130: 1-6.
[http://dx.doi.org/10.1016/j.eplepsyres.2016.12.013] [PMID: 28073027]
[24]
Berendsen S, Varkila M, Kroonen J, et al. Prognostic relevance of epilepsy at presentation in glioblastoma patients. Neuro-oncol 2016; 18(5): 700-6.
[http://dx.doi.org/10.1093/neuonc/nov238] [PMID: 26420896]
[25]
Isolan GR, Marth V, Frizon L, et al. Surgical treatment of drug-resistant epilepsy caused by gliomas in eloquent areas: Experience report. Arq Neuropsiquiatr 2019; 77(11): 797-805.
[http://dx.doi.org/10.1590/0004-282x20190160] [PMID: 31826136]
[26]
Winters R, Winters A, Amedee RG. Statistics: A brief overview. Ochsner J 2010; 10(3): 213-6.
[PMID: 21603381]
[27]
du Prel JB, Röhrig B, Hommel G, Blettner M. Choosing statistical tests: Part 12 of a series on evaluation of scientific publications. Dtsch Arztebl Int 2010; 107(19): 343-8.
[PMID: 20532129]
[28]
Vargason T, Howsmon D, McGuinness D, Hahn J. On the use of multivariate methods for analysis of data from biological networks. Processes (Basel) 2017; 5(4): 36.
[http://dx.doi.org/10.3390/pr5030036] [PMID: 30406024]
[29]
Chang EF, Potts MB, Keles GE, et al. Seizure characteristics and control following resection in 332 patients with low-grade gliomas. J Neurosurg 2008; 108(2): 227-35.
[http://dx.doi.org/10.3171/JNS/2008/108/2/0227] [PMID: 18240916]
[30]
Chang EF, Christie C, Sullivan JE, et al. Seizure control outcomes after resection of dysembryoplastic neuroepithelial tumor in 50 patients. J Neurosurg Pediatr 2010; 5(1): 123-30.
[http://dx.doi.org/10.3171/2009.8.PEDS09368] [PMID: 20043747]
[31]
Pallud J, Audureau E, Blonski M, et al. Epileptic seizures in diffuse low-grade gliomas in adults. Brain 2014; 137(2): 449-62.
[http://dx.doi.org/10.1093/brain/awt345] [PMID: 24374407]
[32]
Yang P, Liang T, Zhang C, et al. Clinicopathological factors predictive of postoperative seizures in patients with gliomas. Seizure 2016; 35: 93-9.
[http://dx.doi.org/10.1016/j.seizure.2015.12.013] [PMID: 26808114]
[33]
Huang H, Yang G, Zhang W, et al. A deep multi-task learning framework for brain tumor segmentation. Front Oncol 2021; 11: 690244.
[http://dx.doi.org/10.3389/fonc.2021.690244] [PMID: 34150660]
[34]
Bech KT, Seyedi JF, Schulz M, Poulsen FR, Pedersen CB. The risk of developing seizures before and after primary brain surgery of low- and high-grade gliomas. Clin Neurol Neurosurg 2018; 169: 185-91.
[http://dx.doi.org/10.1016/j.clineuro.2018.04.024] [PMID: 29709882]
[35]
Ko A, Kim SH, Kim SH, et al. Epilepsy surgery for children with low-grade epilepsy-associated tumors: Factors associated with seizure recurrence and cognitive function. Pediatr Neurol 2019; 91: 50-6.
[http://dx.doi.org/10.1016/j.pediatrneurol.2018.10.008] [PMID: 30477743]
[36]
Akeret K, Serra C, Rafi O, et al. Anatomical features of primary brain tumors affect seizure risk and semiology. Neuroimage Clin 2019; 22: 101688.
[http://dx.doi.org/10.1016/j.nicl.2019.101688] [PMID: 30710869]
[37]
Yu Z, Zhang N, Hameed NUF, et al. The analysis of risk factors and survival outcome for chinese patients with epilepsy with high-grade glioma. World Neurosurg 2019; 125: e947-57.
[http://dx.doi.org/10.1016/j.wneu.2019.01.213] [PMID: 30763739]
[38]
Ius T, Pauletto G, Tomasino B, et al. Predictors of postoperative seizure outcome in low grade glioma: From volumetric analysis to molecular stratification. Cancers (Basel) 2020; 12(2): 397.
[http://dx.doi.org/10.3390/cancers12020397] [PMID: 32046310]
[39]
Jiang H, Liu B, Deng G, et al. Short-term outcomes and predictors of post-surgical seizures in patients with supratentorial low-grade gliomas. J Clin Neurosci 2020; 72: 163-8.
[http://dx.doi.org/10.1016/j.jocn.2019.12.034] [PMID: 31937499]
[40]
Zeng L, Mei Q, Li H, Ke C, Yu J, Chen J. A survival analysis of surgically treated incidental low-grade glioma patients. Sci Rep 2021; 11(1): 8522.
[http://dx.doi.org/10.1038/s41598-021-88023-y] [PMID: 33875775]
[41]
Huang C, Chi X, Hu X, et al. Predictors and mechanisms of epilepsy occurrence in cerebral gliomas: What to look for in clinicopathology. Exp Mol Pathol 2017; 102(1): 115-22.
[http://dx.doi.org/10.1016/j.yexmp.2017.01.005] [PMID: 28087392]
[42]
Blumenthal DT, Aisenstein O, Ben-Horin I, et al. Calcification in high grade gliomas treated with bevacizumab. J Neurooncol 2015; 123(2): 283-8.
[http://dx.doi.org/10.1007/s11060-015-1796-z] [PMID: 25939440]
[43]
Chau W, McIntosh AR. The Talairach coordinate of a point in the MNI space: How to interpret it. Neuroimage 2005; 25(2): 408-16.
[http://dx.doi.org/10.1016/j.neuroimage.2004.12.007] [PMID: 15784419]
[44]
Conde-Blanco E, Pascual-Diaz S, Carreño M, et al. Volumetric and shape analysis of the hippocampus in temporal lobe epilepsy with GAD65 antibodies compared with non-immune epilepsy. Sci Rep 2021; 11(1): 10199.
[http://dx.doi.org/10.1038/s41598-021-89010-z] [PMID: 33986308]
[45]
Mandal PK, Mahajan R, Dinov ID. Structural brain atlases: Design, rationale, and applications in normal and pathological cohorts. J Alzheimers Dis 2012; 30: S169-88.
[46]
Kalakoti P, Edwards A, Ferrier C, et al. Biomarkers of seizure activity in patients with intracranial metastases and gliomas: A wide range study of correlated regions of interest. Front Neurol 2020; 11: 444.
[http://dx.doi.org/10.3389/fneur.2020.00444] [PMID: 32547475]
[47]
Goldberger J, Roweis ST, Hinton GE, Salakhutdinov R, Eds. Neighbourhood Components Analysis. NIPS 2004.
[48]
Bates E, Wilson SM, Saygin AP, et al. Voxel-based lesion–symptom mapping. Nat Neurosci 2003; 6(5): 448-50.
[http://dx.doi.org/10.1038/nn1050] [PMID: 12704393]
[49]
Lee JW, Wen PY, Hurwitz S, et al. Morphological characteristics of brain tumors causing seizures. Arch Neurol 2010; 67(3): 336-42.
[http://dx.doi.org/10.1001/archneurol.2010.2] [PMID: 20212231]
[50]
Mansouri AM, Germann J, Boutet A, et al. Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis. Sci Rep 2020; 10(1): 10989.
[http://dx.doi.org/10.1038/s41598-020-67626-x] [PMID: 32620922]
[51]
Bastos AM, Schoffelen JM. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci 2016; 9: 175.
[http://dx.doi.org/10.3389/fnsys.2015.00175] [PMID: 26778976]
[52]
Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5(1): 4006.
[http://dx.doi.org/10.1038/ncomms5006] [PMID: 24892406]
[53]
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14(12): 749-62.
[http://dx.doi.org/10.1038/nrclinonc.2017.141] [PMID: 28975929]
[54]
van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017; 77(21): e104-7.
[http://dx.doi.org/10.1158/0008-5472.CAN-17-0339] [PMID: 29092951]
[55]
Feng X, Tustison NJ, Patel SH, Meyer CH. Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features. Front Comput Neurosci 2020; 14: 25.
[http://dx.doi.org/10.3389/fncom.2020.00025] [PMID: 32322196]
[56]
Liu Z, Wang Y, Liu X, et al. Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas. Neuroimage Clin 2018; 19: 271-8.
[http://dx.doi.org/10.1016/j.nicl.2018.04.024] [PMID: 30035021]
[57]
Sun K, Liu Z, Li Y, et al. Radiomics analysis of postoperative epilepsy seizures in low-grade gliomas using preoperative MR images. Front Oncol 2020; 10: 1096.
[http://dx.doi.org/10.3389/fonc.2020.01096] [PMID: 32733804]
[58]
Wang Y, Wei W, Liu Z, et al. Predicting the type of tumor-related epilepsy in patients with low-grade gliomas: A radiomics study. Front Oncol 2020; 10: 235.
[http://dx.doi.org/10.3389/fonc.2020.00235] [PMID: 32231995]
[59]
Gao A, Yang H, Wang Y, et al. Radiomics for the prediction of epilepsy in patients with frontal glioma. Front Oncol 2021; 11: 725926.
[http://dx.doi.org/10.3389/fonc.2021.725926] [PMID: 34881174]
[60]
Veres G, Vas NF, Lyngby Lassen M, et al. Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups. PLoS One 2021; 16(6): e0253419.
[http://dx.doi.org/10.1371/journal.pone.0253419] [PMID: 34143830]
[61]
Sun X, Shi L, Luo Y, et al. Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions. Biomed Eng Online 2015; 14(1): 73.
[http://dx.doi.org/10.1186/s12938-015-0064-y] [PMID: 26215471]
[62]
Jie B, Hongxi Y, Ankang G, et al. Radiomics nomogram improves the prediction of epilepsy in patients with gliomas. Front Oncol 2022; 12: 856359.
[http://dx.doi.org/10.3389/fonc.2022.856359] [PMID: 35433444]
[63]
Narisetty NN. Bayesian model selection for high-dimensional data.Handbook of Statistics 43. Elsevier 2020; pp. 207-48.

[64]
Pan SP, Zheng XL, Zhang N, et al. A novel nomogram for predicting the risk of epilepsy occurrence after operative in gliomas patients without preoperative epilepsy history. Epilepsy Res 2021; 174: 106641.
[http://dx.doi.org/10.1016/j.eplepsyres.2021.106641] [PMID: 33878595]
[65]
Bette S, Barz M, Nham H, et al. Image analysis reveals microstructural and volumetric differences in glioblastoma patients with and without preoperative seizures. Cancers (Basel) 2020; 12(4): 994.
[http://dx.doi.org/10.3390/cancers12040994] [PMID: 32316566]
[66]
Skardelly M, Brendle E, Noell S, et al. Predictors of preoperative and early postoperative seizures in patients with intra-axial primary and metastatic brain tumors: A retrospective observational single center study. Ann Neurol 2015; 78(6): 917-28.
[http://dx.doi.org/10.1002/ana.24522] [PMID: 26385488]
[67]
Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol 2018; 17(3): 279-88.
[http://dx.doi.org/10.1016/S1474-4422(18)30038-3] [PMID: 29452687]
[68]
Zhong Z, Wang Z, Wang Y, You G, Jiang T. IDH1/2 mutation is associated with seizure as an initial symptom in low-grade glioma: A report of 311 Chinese adult glioma patients. Epilepsy Res 2015; 109: 100-5.
[http://dx.doi.org/10.1016/j.eplepsyres.2014.09.012] [PMID: 25524848]
[69]
Blumcke I, Aronica E, Urbach H, Alexopoulos A, Gonzalez-Martinez JA. A neuropathology-based approach to epilepsy surgery in brain tumors and proposal for a new terminology use for long-term epilepsy-associated brain tumors. Acta Neuropathol 2014; 128(1): 39-54.
[http://dx.doi.org/10.1007/s00401-014-1288-9] [PMID: 24858213]
[70]
Sperber C, Wiesen D, Karnath H-O. An empirical evaluation of multivariate lesion behaviour mapping using support vector regression. bioRxiv 2018.
[http://dx.doi.org/10.1101/446153]
[71]
Xue C, Yuan J, Zhou Y, Wong OL, Cheung KY, Yu SK. Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study. Visual Computing for Industry, Biomedicine, and Art 2022; 5(1): 10.
[http://dx.doi.org/10.1186/s42492-022-00106-3] [PMID: 35359245]
[72]
Goceri E. Fully automated and adaptive intensity normalization using statistical features for brain MR images. 2018.
[http://dx.doi.org/10.18466/cbayarfbe.384729]
[73]
Duron L, Balvay D, Vande Perre S, et al. Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS One 2019; 14(3): e0213459.
[http://dx.doi.org/10.1371/journal.pone.0213459] [PMID: 30845221]
[74]
Rorden C, Karnath HO, Bonilha L. Improving lesion-symptom mapping. J Cogn Neurosci 2007; 19(7): 1081-8.
[http://dx.doi.org/10.1162/jocn.2007.19.7.1081] [PMID: 17583985]
[75]
Zhang Y, Kimberg DY, Coslett HB, Schwartz MF, Wang Z. Multivariate lesion-symptom mapping using support vector regression. Hum Brain Mapp 2014; 35(12): 5861-76.
[http://dx.doi.org/10.1002/hbm.22590] [PMID: 25044213]
[76]
Gong H, Yu L, Leng S, et al. A deep learning‐ and partial least square regression‐based model observer for a low‐contrast lesion detection task in CT. Med Phys 2019; 46(5): 2052-63.
[http://dx.doi.org/10.1002/mp.13500] [PMID: 30889282]
[77]
Bonkhoff AK, Lim JS, Bae HJ, et al. Generative lesion pattern decomposition of cognitive impairment after stroke. Brain Commun 2021; 3(2): fcab110.
[http://dx.doi.org/10.1093/braincomms/fcab110] [PMID: 34189457]
[78]
Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol 2016; 61(13): R150-66.
[http://dx.doi.org/10.1088/0031-9155/61/13/R150] [PMID: 27269645]
[79]
Fang S, Zhou C, Fan X, Jiang T, Wang Y. Epilepsy-related brain network alterations in patients with temporal lobe glioma in the left hemisphere. Front Neurol 2020; 11: 684.
[http://dx.doi.org/10.3389/fneur.2020.00684] [PMID: 32765403]
[80]
Prasanna P, Karnawat A, Ismail M, Madabhushi A, Tiwari P. Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. J Med Imaging (Bellingham) 2019; 6(2): 1.
[http://dx.doi.org/10.1117/1.JMI.6.2.024005] [PMID: 31093517]
[81]
Shim KY, Chung SW, Jeong JH, et al. Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI. Sci Rep 2021; 11(1): 9974.
[http://dx.doi.org/10.1038/s41598-021-89218-z] [PMID: 33976264]

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