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

Abstract

The 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System has brought a transformative shift in the categorization of adult gliomas. Departing from traditional histological subtypes, the new classification system is guided by molecular genotypes, particularly the Isocitrate Dehydrogenase (IDH) mutation. This alteration reflects a pivotal change in understanding tumor behavior, emphasizing the importance of molecular profiles over morphological characteristics. Gliomas are now categorized into IDH-mutant and IDH wildtype, with significant prognostic implications. For IDH-mutant gliomas, the concurrent presence of Alpha-Thalassemia/mental retardation syndrome X-linked (ATRX) gene expression and co-deletion of 1p19q genes further refine classification. In the absence of 1p19q co-deletion, further categorization depends on the phenotypic expression of CDKN2A/B. Notably, IDH wildtype gliomas exhibit a poorer prognosis, particularly when associated with TERT promoter mutations, EGFR amplification, and +7/-10 co-deletion. Although not part of the new guidelines, the methylation status of the MGMT gene is crucial for guiding alkylating agent treatment. The integration of structural and functional Magnetic Resonance Imaging (MRI) techniques may play a vital role in evaluating these genetic phenotypes, offering insights into tumor microenvironment changes. This multimodal approach may enhance diagnostic precision, aid in treatment planning, and facilitate effective prognosis evaluation of glioma patients.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056288909240219061430
2024-02-26
2025-07-04
The full text of this item is not currently available.

References

  1. WenP.Y. PackerR.J. The 2021 WHO classification of tumors of the central nervous system: Clinical implications.Neuro-oncol.20212381215121710.1093/neuonc/noab12034185090
    [Google Scholar]
  2. TorpS.H. SolheimO. SkjulsvikA.J. The WHO 2021 Classification of Central Nervous System tumours: A practical update on what neurosurgeons need to know—a minireview.Acta Neurochir.202216492453246410.1007/s00701‑022‑05301‑y35879477
    [Google Scholar]
  3. JiangS. WenZ. AhnS.S. CaiK. PaechD. EberhartC.G. ZhouJ. Applications of chemical exchange saturation transfer magnetic resonance imaging in identifying genetic markers in gliomas.NMR Biomed.2023366e473110.1002/nbm.473135297117
    [Google Scholar]
  4. CalabreseE. RudieJ.D. RauscheckerA.M. Villanueva-MeyerJ.E. ClarkeJ.L. SolomonD.A. ChaS. Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma.Neurooncol. Adv.202241vdac06010.1093/noajnl/vdac06035611269
    [Google Scholar]
  5. PrenerM. OpheimG. ShamsZ. SøndergaardC.B. LindbergU. LarssonH.B.W. ZiebellM. LarsenV.A. VestergaardM.B. PaulsonO.B. Single-voxel MR spectroscopy of gliomas with s-LASER at 7T.Diagnostics20231310180510.3390/diagnostics1310180537238288
    [Google Scholar]
  6. YanoH. IkegameY. MiwaK. NakayamaN. MaruyamaT. IkutaS. YokoyamaK. MuragakiY. IwamaT. ShinodaJ. Radiological prediction of isocitrate dehydrogenase (IDH) mutational status and pathological verification for lower-grade astrocytomas.Cureus2022147e2715710.7759/cureus.2715736017268
    [Google Scholar]
  7. McCormackA. Temozolomide in aggressive pituitary tumours and pituitary carcinomas.Best Pract. Res. Clin. Endocrinol. Metab.202236610171310.1016/j.beem.2022.10171336274026
    [Google Scholar]
  8. VarmaM. LuM. GardnerR. DunnmonJ. KhandwalaN. RajpurkarP. LongJ. BeaulieuC. ShpanskayaK. Fei-FeiL. LungrenM.P. PatelB.N. Automated abnormality detection in lower extremity radiographs using deep learning.Nat. Mach. Intell.2019112578583[J].10.1038/s42256‑019‑0126‑0
    [Google Scholar]
  9. YeL. RochanM. LiuZ. Cross-modal self-attention network for referring image segmentation.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition20191050210511
    [Google Scholar]
  10. AzadR. KhosraviN. DehghanmanshadiM. Medical image segmentation on mri images with missing modalities: A reviewarXiv:2203.062172022
    [Google Scholar]
  11. Gonzalez CastroL.N. Arrillaga-RomanyI.C. BatchelorT.T. Challenges and opportunities for clinical trials in patients with glioma.JAMA Neurol.202380322722810.1001/jamaneurol.2022.492436648934
    [Google Scholar]
  12. WerleniusK. KinhultS. SolheimT.S. MagelssenH. LöfgrenD. MudaisiM. HylinS. BartekJ.Jr StrandéusM. LindskogM. RashidH.B. CarstamL. GulatiS. SolheimO. BartekJ. SalvesenØ. JakolaA.S. Effect of disulfiram and copper plus chemotherapy vs chemotherapy alone on survival in patients with recurrent glioblastoma.JAMA Netw. Open202363e234149e23414910.1001/jamanetworkopen.2023.414937000452
    [Google Scholar]
  13. KinslowC.J. MercurioA. KumarP. Association of MGMT promotor methylation with survival in low-grade and anaplastic gliomas after alkylating chemotherapy.JAMA Oncol.202397919927
    [Google Scholar]
  14. PointerK.B. GatsonN.T.N. MGMT methylation status in grades 2 and 3 gliomas is important, but is it prognostic?JAMA Oncol.20239792892910.1001/jamaoncol.2023.075937200043
    [Google Scholar]
  15. KimM.M. AryalM.P. SunY. ParmarH.A. LiP. SchipperM. WahlD.R. LawrenceT.S. CaoY. Response assessment during chemoradiation using a hypercellular/hyperperfused imaging phenotype predicts survival in patients with newly diagnosed glioblastoma.Neuro-oncol.20212391537154610.1093/neuonc/noab03833599755
    [Google Scholar]
  16. HalefogluA.M. CamurcuogluE. TanikC. KizilkayaO. YilmazA. Predictive role of magnetic resonance imaging in the distinction of isocitrate dehydrogenase (IDH) mutant grade 4 astrocytomas versus glioblastomas.Acta Radiol.20236462074208610.1177/0284185123116528237038636
    [Google Scholar]
  17. PeiD. GuanF. HongX. LiuZ. WangW. QiuY. DuanW. WangM. SunC. WangW. WangX. GuoY. WangZ. LiuZ. XingA. GuoZ. LuoL. LiuX. ChengJ. ZhangB. ZhangZ. YanJ. Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas.Eur. Radiol.20233353455346610.1007/s00330‑023‑09459‑636853347
    [Google Scholar]
  18. CindilE. SendurH.N. CeritM.N. ErdoganN. CelebiF. DagN. CeltikciE. InanA. OnerY. TaliT. Prediction of IDH mutation status in high-grade gliomas using DWI and high T1-weight DSC-MRI.Acad. Radiol.202229S3S52S6210.1016/j.acra.2021.02.00233685792
    [Google Scholar]
  19. YangX. XingZ. SheD. LinY. ZhangH. SuY. CaoD. Grading of IDH-mutant astrocytoma using diffusion, susceptibility and perfusion-weighted imaging.BMC Med. Imaging202222110510.1186/s12880‑022‑00832‑335644621
    [Google Scholar]
  20. WuS. ZhangX. RuiW. ShengY. YuY. ZhangY. YaoZ. QiuT. RenY. A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas.Eur. Radiol.20223253187319810.1007/s00330‑021‑08444‑135133485
    [Google Scholar]
  21. Vander HeidenM G CantleyL C ThompsonC B Understanding the warburg effect: The metabolic requirements of cell proliferation.science2009324593010291033
    [Google Scholar]
  22. IwahashiH NagashimaH TanakaK 2-Hydroxyglutarate magnetic resonance spectroscopy in adult brainstem glioma.J. Neurosurg.2023118
    [Google Scholar]
  23. de GodoyL.L. LimK.C. RajanA. VermaG. HanaokaM. O’RourkeD.M. LeeJ.Y.K. DesaiA. ChawlaS. MohanS. Non-invasive assessment of isocitrate dehydrogenase-mutant gliomas using optimized proton magnetic resonance spectroscopy on a routine clinical 3-tesla MRI.Cancers20231518445310.3390/cancers1518445337760422
    [Google Scholar]
  24. BumesE. FellnerC. FellnerF.A. FleischanderlK. HäcklM. LenzS. LinkerR. MirusT. OefnerP.J. PaarC. ProescholdtM.A. RiemenschneiderM.J. RosengarthK. WeisS. WendlC. WimmerS. HauP. GronwaldW. HuttererM. Validation study for non-invasive prediction of IDH mutation status in patients with glioma using in vivo 1H-magnetic resonance spectroscopy and machine learning.Cancers20221411276210.3390/cancers1411276235681741
    [Google Scholar]
  25. Di StefanoA.L. NichelliL. BerzeroG. ValabregueR. TouatM. CapelleL. PontoizeauC. BielleF. LerondJ. GiryM. VillaC. BaussartB. DehaisC. GalanaudD. BaldiniC. SavatovskyJ. DhermainF. DeelchandD.K. OttolenghiC. LehéricyS. MarjańskaM. BranzoliF. SansonM. In vivo 2-hydroxyglutarate monitoring with edited MR spectroscopy for the follow-up of IDH-mutant diffuse gliomas: The IDASPE prospective study.Neurology20231001e94e10610.1212/WNL.000000000020113736180241
    [Google Scholar]
  26. Sacli-BilmezB. DanyeliA.E. YakicierM.C. ArasF.K. PamirM.N. ÖzdumanK. DinçerA. Ozturk-IsikE. Magnetic resonance spectroscopic correlates of progression free and overall survival in “glioblastoma, IDH-wildtype, WHO grade-4”.Front. Neurosci.202317114929210.3389/fnins.2023.114929237457011
    [Google Scholar]
  27. CairncrossG. JenkinsR. Gliomas with 1p/19q Codeletion:a.k.a. Oligodendroglioma.Cancer J.200814635235710.1097/PPO.0b013e31818d817819060598
    [Google Scholar]
  28. YangX. LinY. XingZ. SheD. SuY. CaoD. Predicting 1p/19q codeletion status using diffusion-, susceptibility-, perfusion-weighted, and conventional MRI in IDH-mutant lower-grade gliomas.Acta Radiol.202162121657166510.1177/028418512097362433222488
    [Google Scholar]
  29. SuX. YangX. SunH. Evaluation of key molecular markers in adult diffuse gliomas based on a novel combination of diffusion and perfusion MRI and MR spectroscopy.J Magn Reson Imaging202359262863837246748
    [Google Scholar]
  30. SongS. WangL. YangH. ShanY. ChengY. XuL. DongC. ZhaoG. LuJ. Static 18F-FET PET and DSC-PWI based on hybrid PET/MR for the prediction of gliomas defined by IDH and 1p/19q status.Eur. Radiol.20213164087409610.1007/s00330‑020‑07470‑933211141
    [Google Scholar]
  31. SuC. XuS. LinD. HeH. ChenZ. DamenF.C. KeC. LvX. CaiK. Multi-parametric Z-spectral MRI may have a good performance for glioma stratification in clinical patients.Eur. Radiol.202232110111110.1007/s00330‑021‑08175‑334272981
    [Google Scholar]
  32. HeJ. RenJ. NiuG. LiuA. WuQ. XieS. MaX. LiB. WangP. ShenJ. WuJ. GaoY. Multiparametric MR radiomics in brain glioma: Models comparation to predict biomarker status.BMC Med. Imaging202222113710.1186/s12880‑022‑00865‑835931979
    [Google Scholar]
  33. LuM QuY MaA Prediction of 1p/19q codeletion status in diffuse lower-grade glioma using multimodal MRI radiomics.South. Med. J.202343610231028
    [Google Scholar]
  34. LasockiA. BucklandM.E. MolinaroT. XieJ. WhittleJ.R. WeiH. GaillardF. Correlating MRI features with additional genetic markers and patient survival in histological grade 2-3 IDH-mutant astrocytomas.Neuroradiology20236581215122310.1007/s00234‑023‑03175‑037316586
    [Google Scholar]
  35. ParkY.W. ParkK.S. ParkJ.E. AhnS.S. ParkI. KimH.S. ChangJ.H. LeeS.K. KimS.H. Qualitative and quantitative magnetic resonance imaging phenotypes may predict CDKN2A/B homozygous deletion status in isocitrate dehydrogenase-mutant astrocytomas: a multicenter study.Korean J. Radiol.202324213314410.3348/kjr.2022.073236725354
    [Google Scholar]
  36. ZhangL. WangR. GaoJ. TangY. XuX. KanY. CaoX. WenZ. LiuZ. CuiS. LiY. A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma.Eur. Radiol.202334139139910.1007/s00330‑023‑09944‑y37553486
    [Google Scholar]
  37. ZhangH. ZhangH. ZhangY. ZhouB. WuL. LeiY. HuangB. Deep learning radiomics for the assessment of telomerase reverse transcriptase promoter mutation status in patients with glioblastoma using multiparametric MRI.J. Magn. Reson. Imaging20235851441145110.1002/jmri.2867136896953
    [Google Scholar]
  38. ZhangH. ZhouB. ZhangH. ZhangY. LeiY. HuangB. Peritumoral radiomics for identification of telomerase reverse transcriptase promoter mutation in patients with glioblastoma based on preoperative MRI.Can. Assoc. Radiol. J.202380846537123118330910.1177/0846537123118330937552107
    [Google Scholar]
  39. ZhangH. lyuG. HeW. LeiY. LinF. WangM. ZhangH. LiangL. FengY. YangJ. DSC and DCE histogram analyses of glioma biomarkers, including IDH, MGMT, and TERT, on differentiation and survival.Acad. Radiol.20202712e263e27110.1016/j.acra.2019.12.01031983532
    [Google Scholar]
  40. AhnS.H. AhnS.S. ParkY.W. ParkC.J. LeeS.K. Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study.Neuroradiol. J.2023361495810.1177/1971400922109836935532193
    [Google Scholar]
  41. YamashitaK. HataeR. KikuchiK. KugaD. HataN. YamamotoH. ObaraM. YoshimotoK. IshigamiK. TogaoO. Predicting TERT promoter mutation status using 1H-MR spectroscopy and stretched-exponential model of diffusion-weighted imaging in IDH-wildtype diffuse astrocytic glioma without intense enhancement.Neuroradiology20236581205121310.1007/s00234‑023‑03177‑y
    [Google Scholar]
  42. MinamiN. HongD. SteversN. BargerC.J. RadoulM. HongC. ChenL. KimY. BatsiosG. GillespieA.M. PieperR.O. CostelloJ.F. ViswanathP. RonenS.M. Imaging biomarkers of TERT or GABPB1 silencing in TERT-positive glioblastoma.Neuro-oncol.202224111898191010.1093/neuonc/noac11235460557
    [Google Scholar]
  43. ViswanathP. BatsiosG. MukherjeeJ. GillespieA.M. LarsonP.E.Z. LuchmanH.A. PhillipsJ.J. CostelloJ.F. PieperR.O. RonenS.M. Non-invasive assessment of telomere maintenance mechanisms in brain tumors.Nat. Commun.20211219210.1038/s41467‑020‑20312‑y33397920
    [Google Scholar]
  44. XingZ. HuangW. SuY. YangX. ZhouX. CaoD. Non-invasive prediction of p53 and Ki-67 labelling indices and O-6-methylguanine-DNA methyltransferase promoter methylation status in adult patients with isocitrate dehydrogenase wild-type glioblastomas using diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging combined with conventional MRI.Clin. Radiol.2022778e576e58410.1016/j.crad.2022.03.01535469666
    [Google Scholar]
  45. OzturkK. SoyluE. CayciZ. Correlation between dynamic susceptibility contrast perfusion MRI and genomic alterations in glioblastoma.Neuroradiology202163111801181010.1007/s00234‑021‑02674‑233738509
    [Google Scholar]
  46. ParkY.W. AhnS.S. MoonJ.H. KimE.H. KangS.G. ChangJ.H. KimS.H. LeeS.K. Dynamic contrast-enhanced MRI may be helpful to predict response and prognosis after bevacizumab treatment in patients with recurrent high-grade glioma: Comparison with diffusion tensor and dynamic susceptibility contrast imaging.Neuroradiology202163111811182210.1007/s00234‑021‑02693‑z33755766
    [Google Scholar]
  47. ChoiH.J. ChoiS.H. YouS.H. YooR.E. KangK.M. YunT.J. KimJ. SohnC.H. ParkC.K. ParkS.H. MGMT promoter methylation status in initial and recurrent glioblastoma: correlation study with DWI and DSC PWI features.AJNR Am. J. Neuroradiol.202142585386010.3174/ajnr.A700433632732
    [Google Scholar]
  48. LuJ. LiX. LiH. Perfusion parameters derived from MRI for preoperative prediction of IDH mutation and MGMT promoter methylation status in glioblastomas.Magn. Reson. Imaging20218318919510.1016/j.mri.2021.09.00534506909
    [Google Scholar]
  49. YangX. HuC. XingZ. LinY. SuY. WangX. CaoD. Prediction of Ki-67 labeling index, ATRX mutation, and MGMT promoter methylation status in IDH-mutant astrocytoma by morphological MRI, SWI, DWI, and DSC-PWI.Eur. Radiol.202333107003701410.1007/s00330‑023‑09695‑w37133522
    [Google Scholar]
  50. BaidU MahajanA TalbarS GBM segmentation with 3D U-Net and survival prediction with radiomics.Pre-conference proceedings of the 7th medical image computing and computer-assisted interventions (MICCAI) BraTS Challenge20182835
    [Google Scholar]
  51. GatesE PauloskiJ G SchellingerhoutD Glioma segmentation and a simple accurate model for overall survival prediction.International MICCAI Brainlesion Workshop2018476484
    [Google Scholar]
  52. KaoP.Y. NgoT. ZhangA. Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction.ChamSpringer2018
    [Google Scholar]
  53. FengX TustisonN MeyerC. Brain tumor segmentation using an ensemble of 3D UNets.BraTS20182018110
    [Google Scholar]
/content/journals/cmir/10.2174/0115734056288909240219061430
Loading
/content/journals/cmir/10.2174/0115734056288909240219061430
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