Skip to content
2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

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.

© 2024 The Author(s). Published by Bentham Open. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/cmir/10.2174/1573405620666230426150015
2024-01-01
2024-11-23
Loading full text...

Full text loading...

/deliver/fulltext/cmir/20/1/CMIM-20-e260423216214.html?itemId=/content/journals/cmir/10.2174/1573405620666230426150015&mimeType=html&fmt=ahah

References

  1. BrayF. FerlayJ. SoerjomataramI. SiegelR.L. TorreL.A. JemalA. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.201868639442410.3322/caac.2149230207593
    [Google Scholar]
  2. MillerK.D. OstromQ.T. KruchkoC. PatilN. TihanT. CioffiG. FuchsH.E. WaiteK.A. JemalA. SiegelR.L. Barnholtz-SloanJ.S. Brain and other central nervous system tumor statistics, 2021.CA Cancer J. Clin.202171538140610.3322/caac.2169334427324
    [Google Scholar]
  3. MaschioM. Brain tumor-related epilepsy.Curr. Neuropharmacol.201210212413310.2174/15701591280060447023204982
    [Google Scholar]
  4. RosatiA. TomassiniA. PolloB. AmbrosiC. SchwarzA. PadovaniA. BonettiB. Epilepsy in cerebral glioma: timing of appearance and histological correlations.J. Neurooncol.200993339540010.1007/s11060‑009‑9796‑519183850
    [Google Scholar]
  5. ChandrashekharT.N. MahadevanA. VaniS. YashaT.C. SampathS. ChandramouliB.A. DeviB.I. ArvindaH.R. ShankarS.K. Pathological spectrum of neuronal/glioneuronal tumors from a tertiary referral neurological Institute.Neuropathology201232111210.1111/j.1440‑1789.2011.01206.x21410777
    [Google Scholar]
  6. ForstD.A. NahedB.V. LoefflerJ.S. BatchelorT.T. Low-Grade Gliomas.Oncologist201419440341310.1634/theoncologist.2013‑034524664484
    [Google Scholar]
  7. WenP.Y. KesariS. Malignant gliomas in adults.N. Engl. J. Med.2008359549250710.1056/NEJMra070812618669428
    [Google Scholar]
  8. FanX. LiY. ShanX. YouG. WuZ. LiZ. QiaoH. JiangT. Seizures at presentation are correlated with better survival outcomes in adult diffuse glioma: A systematic review and meta-analysis.Seizure201859162310.1016/j.seizure.2018.04.01829727741
    [Google Scholar]
  9. KerkhofM. VechtC.J. Seizure characteristics and prognostic factors of gliomas.Epilepsia201354Suppl. 9121710.1111/epi.1243724328866
    [Google Scholar]
  10. MastallM. WolpertF. GramatzkiD. ImbachL. BeckerD. SchmickA. HertlerC. RothP. WellerM. WirschingH.G. Survival of brain tumour patients with epilepsy.Brain2021144113322332710.1093/brain/awab18833974079
    [Google Scholar]
  11. WangY. QianT. YouG. PengX. ChenC. YouY. YaoK. WuC. MaJ. ShaZ. WangS. JiangT. Localizing seizure-susceptible brain regions associated with low-grade gliomas using voxel-based lesion-symptom mapping.Neuro-oncol.201517228228810.1093/neuonc/nou13025031032
    [Google Scholar]
  12. CayuelaN. SimóM. MajósC. Rifà-RosX. Gállego Pérez-LarrayaJ. RipollésP. VidalN. MiróJ. GilF. Gil-GilM. PlansG. GrausF. BrunaJ. Seizure‐susceptible brain regions in glioblastoma: Identification of patients at risk.Eur. J. Neurol.201825238739410.1111/ene.1351829115706
    [Google Scholar]
  13. SoltaniM. BonakdarA. ShakourifarN. BabaeiR. RaahemifarK. Efficacy of location-based features for survival prediction of patients with glioblastoma depending on resection status.Front. Oncol.20211166112310.3389/fonc.2021.66112334295809
    [Google Scholar]
  14. ChoH. LeeS. KimJ. ParkH. Classification of the glioma grading using radiomics analysis.PeerJ20186e5982e598210.7717/peerj.598230498643
    [Google Scholar]
  15. RaghavendraU. AcharyaU.R. AdeliH. Artificial intelligence techniques for automated diagnosis of neurological disorders.Eur. Neurol.2019821-3416410.1159/00050429231743905
    [Google Scholar]
  16. LeeS.B. ChoY.J. JeonK. ParkS. LeeS. ChoiY.H. CheonJ.E. KimW.S. Diagnosis of hippocampal sclerosis in children: Comparison of automated brain MRI volumetry and readers of varying experience.AJR Am. J. Roentgenol.2021217122323410.2214/AJR.20.2399032903057
    [Google Scholar]
  17. HuL.S. Hawkins-DaarudA. WangL. LiJ. SwansonK.R. Imaging of intratumoral heterogeneity in high-grade glioma.Cancer Lett.20204779710610.1016/j.canlet.2020.02.02532112907
    [Google Scholar]
  18. PopeW.B. BrandalG. Conventional and advanced magnetic resonance imaging in patients with high-grade glioma.Q. J. Nucl. Med. Mol. Imaging201862323925310.23736/S1824‑4785.18.03086‑829696946
    [Google Scholar]
  19. HoM.L. RojasR. EisenbergR.L. Cerebral Edema.AJR Am. J. Roentgenol.20121993W258W27310.2214/AJR.11.808122915416
    [Google Scholar]
  20. MaldaunM.V.C. SukiD. LangF.F. PrabhuS. ShiW. FullerG.N. WildrickD.M. SawayaR. Cystic glioblastoma multiforme: Survival outcomes in 22 cases.J. Neurosurg.20041001616710.3171/jns.2004.100.1.006114743913
    [Google Scholar]
  21. LatiniF. AxelsonH. FahlströmM. JemstedtM. Alberius MunkhammarÅ. ZetterlingM. RyttleforsM. Role of preoperative assessment in predicting tumor-induced plasticity in patients with diffuse gliomas.J. Clin. Med.2021105110810.3390/jcm1005110833799925
    [Google Scholar]
  22. ChoiJ.Y. ChangJ.W. ParkY.G. KimT.S. LeeB.I. ChungS.S. 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.2004821354210.1159/00007665915007218
    [Google Scholar]
  23. ToledoM. Sarria-EstradaS. QuintanaM. MaldonadoX. Martinez-RicarteF. RodonJ. AugerC. AizpuruaM. Salas-PuigJ. SantamarinaE. Martinez-SaezE. Epileptic features and survival in glioblastomas presenting with seizures.Epilepsy Res.20171301610.1016/j.eplepsyres.2016.12.01328073027
    [Google Scholar]
  24. BerendsenS. VarkilaM. KroonenJ. SeuteT. SnijdersT.J. KauwF. SplietW.G.M. WillemsM. PouletC. BroekmanM.L. BoursV. RobeP.A. Prognostic relevance of epilepsy at presentation in glioblastoma patients.Neuro-oncol.201618570070610.1093/neuonc/nov23826420896
    [Google Scholar]
  25. IsolanG.R. MarthV. FrizonL. DiniL. DiniS. YamakiV.N. FigueiredoE.G. Surgical treatment of drug-resistant epilepsy caused by gliomas in eloquent areas: Experience report.Arq. Neuropsiquiatr.2019771179780510.1590/0004‑282x2019016031826136
    [Google Scholar]
  26. WintersR. WintersA. AmedeeR.G. Statistics: A brief overview.Ochsner J.201010321321621603381
    [Google Scholar]
  27. du PrelJ.B. RöhrigB. HommelG. BlettnerM. Choosing statistical tests: Part 12 of a series on evaluation of scientific publications.Dtsch. Arztebl. Int.20101071934334820532129
    [Google Scholar]
  28. VargasonT. HowsmonD. McGuinnessD. HahnJ. On the use of multivariate methods for analysis of data from biological networks.Processes (Basel)2017543610.3390/pr503003630406024
    [Google Scholar]
  29. ChangE.F. PottsM.B. KelesG.E. LambornK.R. ChangS.M. BarbaroN.M. BergerM.S. Seizure characteristics and control following resection in 332 patients with low-grade gliomas.J. Neurosurg.2008108222723510.3171/JNS/2008/108/2/022718240916
    [Google Scholar]
  30. ChangE.F. ChristieC. SullivanJ.E. GarciaP.A. TihanT. GuptaN. BergerM.S. BarbaroN.M. Seizure control outcomes after resection of dysembryoplastic neuroepithelial tumor in 50 patients.J. Neurosurg. Pediatr.20105112313010.3171/2009.8.PEDS0936820043747
    [Google Scholar]
  31. PalludJ. AudureauE. BlonskiM. SanaiN. BauchetL. FontaineD. MandonnetE. DezamisE. PsimarasD. GuyotatJ. PeruzziP. PageP. GalB. PárragaE. BaronM.H. VlaicuM. GuillevinR. DevauxB. DuffauH. TaillandierL. CapelleL. HuberfeldG. Epileptic seizures in diffuse low-grade gliomas in adults.Brain2014137244946210.1093/brain/awt34524374407
    [Google Scholar]
  32. YangP. LiangT. ZhangC. CaiJ. ZhangW. ChenB. QiuX. YaoK. LiG. WangH. JiangC. YouG. JiangT. Clinicopathological factors predictive of postoperative seizures in patients with gliomas.Seizure201635939910.1016/j.seizure.2015.12.01326808114
    [Google Scholar]
  33. HuangH. YangG. ZhangW. XuX. YangW. JiangW. LaiX. A deep multi-task learning framework for brain tumor segmentation.Front. Oncol.20211169024410.3389/fonc.2021.69024434150660
    [Google Scholar]
  34. BechK.T. SeyediJ.F. SchulzM. PoulsenF.R. PedersenC.B. The risk of developing seizures before and after primary brain surgery of low- and high-grade gliomas.Clin. Neurol. Neurosurg.201816918519110.1016/j.clineuro.2018.04.02429709882
    [Google Scholar]
  35. KoA. KimS.H. KimS.H. ParkE.K. ShimK.W. KangH.C. KimD.S. KimH.D. LeeJ.S. Epilepsy surgery for children with low-grade epilepsy-associated tumors: Factors associated with seizure recurrence and cognitive function.Pediatr. Neurol.201991505610.1016/j.pediatrneurol.2018.10.00830477743
    [Google Scholar]
  36. AkeretK. SerraC. RafiO. StaartjesV.E. FierstraJ. BellutD. MaldanerN. ImbachL.L. WolpertF. PoryazovaR. RegliL. KrayenbühlN. Anatomical features of primary brain tumors affect seizure risk and semiology.Neuroimage Clin.20192210168810.1016/j.nicl.2019.10168830710869
    [Google Scholar]
  37. YuZ. ZhangN. HameedN.U.F. QiuT. ZhuangD. LuJ. WuJ. The analysis of risk factors and survival outcome for chinese patients with epilepsy with high-grade glioma.World Neurosurg.2019125e947e95710.1016/j.wneu.2019.01.21330763739
    [Google Scholar]
  38. IusT. PaulettoG. TomasinoB. MaieronM. BudaiR. IsolaM. CesselliD. LettieriC. SkrapM. Predictors of postoperative seizure outcome in low grade glioma: From volumetric analysis to molecular stratification.Cancers (Basel)202012239710.3390/cancers1202039732046310
    [Google Scholar]
  39. JiangH. LiuB. DengG. YuanF. TanY. YangK. GaoL. WangJ. ChenQ. Short-term outcomes and predictors of post-surgical seizures in patients with supratentorial low-grade gliomas.J. Clin. Neurosci.20207216316810.1016/j.jocn.2019.12.03431937499
    [Google Scholar]
  40. ZengL. MeiQ. LiH. KeC. YuJ. ChenJ. A survival analysis of surgically treated incidental low-grade glioma patients.Sci. Rep.2021111852210.1038/s41598‑021‑88023‑y33875775
    [Google Scholar]
  41. HuangC. ChiX. HuX. ChenN. ZhouQ. ZhouD. LiJ. Predictors and mechanisms of epilepsy occurrence in cerebral gliomas: What to look for in clinicopathology.Exp. Mol. Pathol.2017102111512210.1016/j.yexmp.2017.01.00528087392
    [Google Scholar]
  42. BlumenthalD.T. AisensteinO. Ben-HorinI. Ben BashatD. ArtziM. CornB.W. KannerA.A. RamZ. BoksteinF. Calcification in high grade gliomas treated with bevacizumab.J. Neurooncol.2015123228328810.1007/s11060‑015‑1796‑z25939440
    [Google Scholar]
  43. ChauW. McIntoshA.R. The Talairach coordinate of a point in the MNI space: How to interpret it.Neuroimage200525240841610.1016/j.neuroimage.2004.12.00715784419
    [Google Scholar]
  44. Conde-BlancoE. Pascual-DiazS. CarreñoM. Muñoz-MorenoE. ParienteJ.C. BogetT. ManzanaresI. DonaireA. CentenoM. GrausF. BargallóN. Volumetric and shape analysis of the hippocampus in temporal lobe epilepsy with GAD65 antibodies compared with non-immune epilepsy.Sci. Rep.20211111019910.1038/s41598‑021‑89010‑z33986308
    [Google Scholar]
  45. MandalP.K. MahajanR. DinovI.D. Structural brain atlases: Design, rationale, and applications in normal and pathological cohorts.J. Alzheimers Dis.201230S169S188
    [Google Scholar]
  46. KalakotiP. EdwardsA. FerrierC. SharmaK. HuynhT. LedbetterC. Gonzalez-ToledoE. NandaA. SunH. Biomarkers of seizure activity in patients with intracranial metastases and gliomas: A wide range study of correlated regions of interest.Front. Neurol.20201144410.3389/fneur.2020.0044432547475
    [Google Scholar]
  47. GoldbergerJ. RoweisS.T. HintonG.E. SalakhutdinovR. Neighbourhood Components Analysis.NIPS2004
    [Google Scholar]
  48. BatesE. WilsonS.M. SayginA.P. DickF. SerenoM.I. KnightR.T. DronkersN.F. Voxel-based lesion–symptom mapping.Nat. Neurosci.20036544845010.1038/nn105012704393
    [Google Scholar]
  49. LeeJ.W. WenP.Y. HurwitzS. BlackP. KesariS. DrappatzJ. GolbyA.J. WellsW.M.III WarfieldS.K. KikinisR. BromfieldE.B. Morphological characteristics of brain tumors causing seizures.Arch. Neurol.201067333634210.1001/archneurol.2010.220212231
    [Google Scholar]
  50. MansouriA.M. GermannJ. BoutetA. EliasG.J.B. MithaniK. ChowC.T. KarmurB. IbrahimG.M. McAndrewsM.P. LozanoA.M. ZadehG. ValianteT.A. Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis.Sci. Rep.20201011098910.1038/s41598‑020‑67626‑x32620922
    [Google Scholar]
  51. BastosA.M. SchoffelenJ.M. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls.Front. Syst. Neurosci.2016917510.3389/fnsys.2015.0017526778976
    [Google Scholar]
  52. AertsH.J.W.L. VelazquezE.R. LeijenaarR.T.H. ParmarC. GrossmannP. CarvalhoS. BussinkJ. MonshouwerR. Haibe-KainsB. RietveldD. HoebersF. RietbergenM.M. LeemansC.R. DekkerA. QuackenbushJ. GilliesR.J. LambinP. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.Nat. Commun.201451400610.1038/ncomms500624892406
    [Google Scholar]
  53. LambinP. LeijenaarR.T.H. DeistT.M. PeerlingsJ. de JongE.E.C. van TimmerenJ. SanduleanuS. LarueR.T.H.M. EvenA.J.G. JochemsA. van WijkY. WoodruffH. van SoestJ. LustbergT. RoelofsE. van ElmptW. DekkerA. MottaghyF.M. WildbergerJ.E. WalshS. Radiomics: the bridge between medical imaging and personalized medicine.Nat. Rev. Clin. Oncol.2017141274976210.1038/nrclinonc.2017.14128975929
    [Google Scholar]
  54. van GriethuysenJ.J.M. FedorovA. ParmarC. HosnyA. AucoinN. NarayanV. Beets-TanR.G.H. Fillion-RobinJ.C. PieperS. AertsH.J.W.L. Computational radiomics system to decode the radiographic phenotype.Cancer Res.20177721e104e10710.1158/0008‑5472.CAN‑17‑033929092951
    [Google Scholar]
  55. FengX. TustisonN.J. PatelS.H. MeyerC.H. Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features.Front. Comput. Neurosci.2020142510.3389/fncom.2020.0002532322196
    [Google Scholar]
  56. LiuZ. WangY. LiuX. DuY. TangZ. WangK. WeiJ. DongD. ZangY. DaiJ. JiangT. TianJ. Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas.Neuroimage Clin.20181927127810.1016/j.nicl.2018.04.02430035021
    [Google Scholar]
  57. SunK. LiuZ. LiY. WangL. TangZ. WangS. ZhouX. ShaoL. SunC. LiuX. JiangT. WangY. TianJ. Radiomics analysis of postoperative epilepsy seizures in low-grade gliomas using preoperative MR images.Front. Oncol.202010109610.3389/fonc.2020.0109632733804
    [Google Scholar]
  58. WangY. WeiW. LiuZ. LiangY. LiuX. LiY. TangZ. JiangT. TianJ. Predicting the type of tumor-related epilepsy in patients with low-grade gliomas: A radiomics study.Front. Oncol.20201023510.3389/fonc.2020.0023532231995
    [Google Scholar]
  59. GaoA. YangH. WangY. ZhaoG. WangC. WangH. ZhangX. ZhangY. ChengJ. YangG. BaiJ. Radiomics for the prediction of epilepsy in patients with frontal glioma.Front. Oncol.20211172592610.3389/fonc.2021.72592634881174
    [Google Scholar]
  60. VeresG. VasN.F. Lyngby LassenM. BéresováM. K KrizsanA. ForgácsA. BerényiE. BalkayL. Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups.PLoS One2021166e025341910.1371/journal.pone.025341934143830
    [Google Scholar]
  61. SunX. ShiL. LuoY. YangW. LiH. LiangP. LiK. MokV.C.T. ChuW.C.W. WangD. Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions.Biomed. Eng. Online20151417310.1186/s12938‑015‑0064‑y26215471
    [Google Scholar]
  62. JieB. HongxiY. AnkangG. YidaW. GuohuaZ. XiaoyueM. ChenglongW. HaijieW. XiaonanZ. GuangY. YongZ. JingliangC. Radiomics nomogram improves the prediction of epilepsy in patients with gliomas.Front. Oncol.20221285635910.3389/fonc.2022.85635935433444
    [Google Scholar]
  63. NarisettyN.N. Bayesian model selection for high-dimensional data.Handbook of Statistics. 43.Chapter 4 Srinivasa RaoA.S.R. RaoC.R. Elsevier2020207248
    [Google Scholar]
  64. PanS.P. ZhengX.L. ZhangN. LinX.M. LiK.J. XiaX.F. ZouC.L. ZhangW.Y. A novel nomogram for predicting the risk of epilepsy occurrence after operative in gliomas patients without preoperative epilepsy history.Epilepsy Res.202117410664110.1016/j.eplepsyres.2021.10664133878595
    [Google Scholar]
  65. BetteS. BarzM. NhamH. HuberT. BerndtM. SalesA. Schmidt-GrafF. MeyerH. RyangY.M. MeyerB. ZimmerC. KirschkeJ. WiestlerB. GemptJ. Image analysis reveals microstructural and volumetric differences in glioblastoma patients with and without preoperative seizures.Cancers (Basel)202012499410.3390/cancers1204099432316566
    [Google Scholar]
  66. SkardellyM. BrendleE. NoellS. BehlingF. WuttkeT.V. SchittenhelmJ. BisdasS. MeisnerC. RonaS. TatagibaM.S. TabatabaiG. 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.201578691792810.1002/ana.2452226385488
    [Google Scholar]
  67. ElgerC.E. HoppeC. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection.Lancet Neurol.201817327928810.1016/S1474‑4422(18)30038‑329452687
    [Google Scholar]
  68. ZhongZ. WangZ. WangY. YouG. JiangT. 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.201510910010510.1016/j.eplepsyres.2014.09.01225524848
    [Google Scholar]
  69. BlumckeI. AronicaE. UrbachH. AlexopoulosA. Gonzalez-MartinezJ.A. 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.20141281395410.1007/s00401‑014‑1288‑924858213
    [Google Scholar]
  70. SperberC. WiesenD. KarnathH-O. An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.bioRxiv201810.1101/446153
    [Google Scholar]
  71. XueC. YuanJ. ZhouY. WongO.L. CheungK.Y. YuS.K. Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study.Visual Computing for Industry, Biomedicine, and Art2022511010.1186/s42492‑022‑00106‑335359245
    [Google Scholar]
  72. GoceriE. Fully automated and adaptive intensity normalization using statistical features for brain MR images201810.18466/cbayarfbe.384729
    [Google Scholar]
  73. DuronL. BalvayD. Vande PerreS. BouchouichaA. SavatovskyJ. SadikJ.C. Thomassin-NaggaraI. FournierL. LeclerA. Gray-level discretization impacts reproducible MRI radiomics texture features.PLoS One2019143e021345910.1371/journal.pone.021345930845221
    [Google Scholar]
  74. RordenC. KarnathH.O. BonilhaL. Improving lesion-symptom mapping.J. Cogn. Neurosci.20071971081108810.1162/jocn.2007.19.7.108117583985
    [Google Scholar]
  75. ZhangY. KimbergD.Y. CoslettH.B. SchwartzM.F. WangZ. Multivariate lesion-symptom mapping using support vector regression.Hum. Brain Mapp.201435125861587610.1002/hbm.2259025044213
    [Google Scholar]
  76. GongH. YuL. LengS. DilgerS.K. RenL. ZhouW. FletcherJ.G. McColloughC.H. A deep learning‐ and partial least square regression‐based model observer for a low‐contrast lesion detection task in CT.Med. Phys.20194652052206310.1002/mp.1350030889282
    [Google Scholar]
  77. BonkhoffA.K. LimJ.S. BaeH.J. WeaverN.A. KuijfH.J. BiesbroekJ.M. RostN.S. BzdokD. Generative lesion pattern decomposition of cognitive impairment after stroke.Brain Commun.202132fcab11010.1093/braincomms/fcab11034189457
    [Google Scholar]
  78. YipS.S.F. AertsH.J.W.L. Applications and limitations of radiomics.Phys. Med. Biol.20166113R150R16610.1088/0031‑9155/61/13/R15027269645
    [Google Scholar]
  79. FangS. ZhouC. FanX. JiangT. WangY. Epilepsy-related brain network alterations in patients with temporal lobe glioma in the left hemisphere.Front. Neurol.20201168410.3389/fneur.2020.0068432765403
    [Google Scholar]
  80. PrasannaP. KarnawatA. IsmailM. MadabhushiA. TiwariP. Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.J. Med. Imaging (Bellingham)201962110.1117/1.JMI.6.2.02400531093517
    [Google Scholar]
  81. ShimK.Y. ChungS.W. JeongJ.H. HwangI. ParkC.K. KimT.M. ParkS.H. WonJ.K. LeeJ.H. LeeS.T. YooR.E. KangK.M. YunT.J. KimJ.H. SohnC.H. ChoiK.S. ChoiS.H. Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI.Sci. Rep.2021111997410.1038/s41598‑021‑89218‑z33976264
    [Google Scholar]
/content/journals/cmir/10.2174/1573405620666230426150015
Loading
/content/journals/cmir/10.2174/1573405620666230426150015
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test