Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background:

The purpose of this work was to identify which Glioblastoma (GBM) problems can be handled by Magnetic Resonance Imaging (MRI) and Machine Learning (ML) techniques. Results, limitations, and trends through a review of the scientific literature in the last 5 years were performed. Google Scholar, PubMed, Elsevier databases, and forward and backward citations were used for searching articles applying ML techniques in GBM. The 50 most relevant papers fulfilling the selection criteria were deeply analyzed. The PRISMA statement was followed to structure our report.

Methods:

A partial taxonomy of the GBM problems tackled with ML methods was formulated with 15 subcategories grouped into four categories: extraction of characteristics from tumoral regions, differentiation, characterization, and problems based on genetics.

Results:

The dominant techniques in solving these problems are: Radiomics for feature extraction, Least Absolute Shrinkage and Selection Operator for feature selection, Support Vector Machines and Random Forest for classification, and Convolutional Neural Networks for characterization. A noticeable trend is that the application of Deep Learning on GBM problems is growing exponentially. The main limitations of ML methods are their interpretability and generalization.

Conclusion:

The diagnosis, treatment, and characterization of GBM have advanced with the aid of ML methods and MRI data, and this improvement is expected to continue. ML methods are effective in solving GBM-related problems with different precisions, Overall Survival being the hardest problem to solve with accuracies ranging from 57%-71%, and GBM differentiation the one with the highest accuracy ranging from 80%-97%.

© 2024 The Author(s). Published by Bentham Science Publisher.
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/10.2174/0115734056265212231122102029
2024-01-19
2025-01-19

  • From This Site

    /content/journals/cmir/10.2174/0115734056265212231122102029
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cmir/20/1/e15734056265212.html?itemId=/content/journals/cmir/10.2174/0115734056265212231122102029&mimeType=html&fmt=ahah

References

  1. BedrikovetskiS. Dudi-VenkataN.N. MaicasG. KroonH.M. SeowW. CarneiroG. MooreJ.W. SammourT. Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: A systematic review and meta-analysis.Artif. Intell. Med.202111310202210.1016/j.artmed.2021.10202233685585
     [Google Scholar]
  2. YuC. RenG. DongY. Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units.BMC Med. Inform. Decis. Mak.202020S312410.1186/s12911‑020‑1120‑532646412
     [Google Scholar]
  3. ShiF. WangJ. ShiJ. WuZ. WangQ. TangZ. HeK. ShiY. ShenD. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19.IEEE Rev. Biomed. Eng.20211441510.1109/RBME.2020.298797532305937
     [Google Scholar]
  4. ZhangL. YuG. XiaD. WangJ. Protein–protein interactions prediction based on ensemble deep neural networks.Neurocomputing2019324101910.1016/j.neucom.2018.02.097
     [Google Scholar]
  5. NelsonA. HerronD. ReesG. NachevP. Predicting scheduled hospital attendance with artificial intelligence.NPJ Digit. Med.2019212610.1038/s41746‑019‑0103‑331304373
     [Google Scholar]
  6. ReigB. HeacockL. GerasK.J. MoyL. Machine learning in breast MRI.J. Magn. Reson. Imaging2020524998101810.1002/jmri.2685231276247
     [Google Scholar]
  7. NeromyliotisE. KalamatianosT. PaschalisA. KomaitisS. FountasK.N. KapsalakiE.Z. StranjalisG. TsougosI. Machine learning in meningioma MRI: Past to present. A narrative review.J. Magn. Reson. Imaging2022551486010.1002/jmri.2737833006425
     [Google Scholar]
  8. EricksonB.J. KorfiatisP. AkkusZ. KlineT.L. Machine learning for medical imaging.Radiographics201737250551510.1148/rg.201716013028212054
     [Google Scholar]
  9. GuiC. ChanV. Machine learning in medicine.Univ. West. Ont. Med. J.2017862767810.5206/uwomj.v86i2.2060
     [Google Scholar]
  10. IbrahimA. GambleP. JaroensriR. AbdelsameaM.M. MermelC.H. ChenP.H.C. RakhaE.A. Artificial intelligence in digital breast pathology: Techniques and applications.Breast20204926727310.1016/j.breast.2019.12.00731935669
     [Google Scholar]
  11. Lo GulloR. Eskreis-WinklerS. MorrisE.A. PinkerK. Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.Breast20204911512210.1016/j.breast.2019.11.00931786416
     [Google Scholar]
  12. GuD. SuK. ZhaoH. A case-based ensemble learning system for explainable breast cancer recurrence prediction.Artif. Intell. Med.202010710185810.1016/j.artmed.2020.10185832828461
     [Google Scholar]
  13. WeisJ.A. MigaM.I. YankeelovT.E. Three-dimensional image-based mechanical modeling for predicting the response of breast cancer to neoadjuvant therapy.Comput. Methods Appl. Mech. Eng.201731449451210.1016/j.cma.2016.08.02428042181
     [Google Scholar]
  14. LuoY. McShanD. RayD. MatuszakM. JollyS. LawrenceT. KongF-M. Ten HakenR. El NaqaI. Development of a fully cross-validated bayesian network approach for local control prediction in lung cancer.IEEE Trans. Radiat. Plasma Med. Sci.20193223224110.1109/TRPMS.2018.283260930854500
     [Google Scholar]
  15. TsengH.H. LuoY. CuiS. ChienJ.T. Ten HakenR.K. NaqaI.E. Deep reinforcement learning for automated radiation adaptation in lung cancer.Med. Phys.201744126690670510.1002/mp.1262529034482
     [Google Scholar]
  16. OertherB. BurenM.V. KleinC.M. KirsteS. NicolayN.H. SpraveT. Predicting biochemical failure in irradiated patients with prostate cancer by tumour volume measured by multiparametric MRI.In Vivo.20203463473348110.21873/invivo.12187
     [Google Scholar]
  17. ValdesG. SimoneC.B.II ChenJ. LinA. YomS.S. PattisonA.J. CarpenterC.M. SolbergT.D. Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.Radiother. Oncol.2017125339239710.1016/j.radonc.2017.10.01429162279
     [Google Scholar]
  18. van DijkL.V. Van den BoschL. AljabarP. PeressuttiD. BothS. J H M SteenbakkersR. LangendijkJ.A. GoodingM.J. BrouwerC.L. Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.Radiother. Oncol.202014211512310.1016/j.radonc.2019.09.02231653573
     [Google Scholar]
  19. AhnS.H. YeoA.U. KimK.H. KimC. GohY. ChoS. LeeS.B. LimY.K. KimH. ShinD. KimT. KimT.H. YounS.H. OhE.S. JeongJ.H. Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.Radiat. Oncol.201914121310.1186/s13014‑019‑1392‑z31775825
     [Google Scholar]
  20. FechterT. AdebahrS. BaltasD. Ben AyedI. DesrosiersC. DolzJ. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.Med. Phys.201744126341635210.1002/mp.1259328940372
     [Google Scholar]
  21. JungJ.W. LeeC. MosherE.G. MilleM.M. YeomY.S. JonesE.C. ChoiM. LeeC. Automatic segmentation of cardiac structures for breast cancer radiotherapy.Phys. Imaging Radiat. Oncol.201912444810.1016/j.phro.2019.11.00733458294
     [Google Scholar]
  22. ChaoH.H. ValdesG. LunaJ.M. HeskelM. BermanA.T. SolbergT.D. SimoneC.B.II Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.J. Appl. Clin. Med. Phys.201819553954610.1002/acm2.1241529992732
     [Google Scholar]
  23. HasnainZ. MasonJ. GillK. MirandaG. GillI.S. KuhnP. NewtonP.K. Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients.PLoS One2019142e021097610.1371/journal.pone.021097630785915
     [Google Scholar]
  24. LombardiM. AssemM. Glioblastoma genomics: A very complicated story.GlioblastomaBrisbane (AU)Codon Publications201710.15586/codon.glioblastoma.2017.ch1
     [Google Scholar]
  25. ValdebenitoJ. MedinaF. Machine learning approaches to study glioblastoma: A review of the last decade of applications.Cancer Rep.201926e122610.1002/cnr2.122632729254
     [Google Scholar]
  26. NguyenA.V. BlearsE.E. RossE. LallR.R. Ortega-BarnettJ. Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: A systematic review and meta-analysis.Neurosurg. Focus2018455E510.3171/2018.8.FOCUS1832530453459
     [Google Scholar]
  27. NeilZ.D. PierzchajloN. BoyettC. LittleO. KuoC.C. BrownN.J. GendreauJ. Assessing metabolic markers in glioblastoma using machine learning: A systematic review.Metabolites202313216110.3390/metabo1302016136837779
     [Google Scholar]
  28. BoothT.C. GrzedaM. ChelliahA. RomanA. Al BusaidiA. DragosC. ShuaibH. LuisA. MirchandaniA. AlparslanB. MansoorN. LavradorJ. VerganiF. AshkanK. ModatM. OurselinS. Imaging biomarkers of glioblastoma treatment response: A systematic review and meta-analysis of recent machine learning studies.Front. Oncol.20221279966210.3389/fonc.2022.79966235174084
     [Google Scholar]
  29. TewarieI.A. SendersJ.T. KremerS. DeviS. GormleyW.B. ArnaoutO. SmithT.R. BroekmanM.L.D. Survival prediction of glioblastoma patients—are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.Neurosurg. Rev.20214442047205710.1007/s10143‑020‑01430‑z33156423
     [Google Scholar]
  30. UribeC.F. MathotaarachchiS. GaudetV. SmithK.C. Rosa-NetoP. BénardF. BlackS.E. ZukotynskiK. Machine learning in nuclear medicine: Part 1—Introduction.J. Nucl. Med.201960445145810.2967/jnumed.118.22349530733322
     [Google Scholar]
  31. ZukotynskiK. GaudetV. UribeC.F. MathotaarachchiS. SmithK.C. Rosa-NetoP. BénardF. BlackS.E. Machine learning in nuclear medicine: Part 2—neural networks and clinical aspects.J. Nucl. Med.2021621222910.2967/jnumed.119.23183732978286
     [Google Scholar]
  32. YoungR.J. KnoppE.A. Brain MRI: Tumor evaluation.J. Magn. Reson. Imaging200624470972410.1002/jmri.2070416958058
     [Google Scholar]
  33. NehringS. TadiP. TennyS. Cerebral Edema.StatPearls Publishing2022
     [Google Scholar]
  34. GilliesR.J. KinahanP.E. HricakH. Radiomics: Images are more than pictures, they are data.Radiology2016278256357710.1148/radiol.201515116926579733
     [Google Scholar]
  35. van TimmerenJ.E. CesterD. Tanadini-LangS. AlkadhiH. BaesslerB. Radiomics in medical imaging—“how-to” guide and critical reflection.Insights Imaging20201119110.1186/s13244‑020‑00887‑232785796
     [Google Scholar]
  36. TianJ. DongD. LiuZ. WeiJ. Radiomics and Its Clinical Application.ChinaAcademic Press2021
     [Google Scholar]
  37. MoradmandH. AghamiriS.M.R. GhaderiR. Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma.J. Appl. Clin. Med. Phys.202021117919010.1002/acm2.1279531880401
     [Google Scholar]
  38. CepedaS. Pérez-NuñezA. García-GarcíaS. García-PérezD. ArreseI. Jiménez-RoldánL. García-GalindoM. GonzálezP. Velasco-CasaresM. ZamoraT. SarabiaR. Predicting short-term survival after gross total or near total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI.Cancers20211320504710.3390/cancers1320504734680199
     [Google Scholar]
  39. LeungK. Naive bayesian classifier.Polytechnic University Department of Computer Science/Finance and Risk Engineering2007123156
     [Google Scholar]
  40. SiddiqueN. PahedingS. ElkinC.P. DevabhaktuniV. U-net and its variants for medical image segmentation: A review of theory and applications.IEEE Access202199820318205710.1109/ACCESS.2021.3086020
     [Google Scholar]
  41. MitchellR. FrankE. Accelerating the XGBoost algorithm using GPU computing.PeerJ Comput. Sci.20173e12710.7717/peerj‑cs.127
     [Google Scholar]
  42. ShboulZ.A. AlamM. VidyaratneL. PeiL. ElbakaryM.I. IftekharuddinK.M. Feature-guided deep radiomics for glioblastoma patient survival prediction.Front. Neurosci.20191396696610.3389/fnins.2019.0096631619949
     [Google Scholar]
  43. NobleW.S. What is a support vector machine?Nat. Biotechnol.200624121565156710.1038/nbt1206‑156517160063
     [Google Scholar]
  44. YeZ. PriceR.L. LiuX. LinJ. YangQ. SunP. WuA.T. WangL. HanR.H. SongC. YangR. GaryS.E. MaoD.D. WallendorfM. CampianJ.L. LiJ.S. DahiyaS. KimA.H. SongS.K. Diffusion histology imaging combining diffusion basis spectrum imaging (DBSI) and machine learning improves detection and classification of glioblastoma pathology.Clin. Cancer Res.202026205388539910.1158/1078‑0432.CCR‑20‑073632694155
     [Google Scholar]
  45. LemhadriI. RuanF. AbrahamL. TibshiraniR. LassoNet: A neural network with feature sparsity.J. Mach. Learn. Res.20212212910.48550/arXiv.1907.1220
     [Google Scholar]
  46. YangY. HanY. HuX. WangW. CuiG. GuoL. ZhangX. An improvement of survival stratification in glioblastoma patients via combining subregional radiomics signatures.Front. Neurosci.20211568345210.3389/fnins.2021.68345234054424
     [Google Scholar]
  47. ChaZ. Yunqian M. Ensemble Machine Learning: Methods and ApplicationsSpringer201215717610.1007/978‑1‑4419‑9326‑7_5
     [Google Scholar]
  48. ChiuF.Y. LeN.Q.K. ChenC.Y. A multiparametric MRI-based radiomics analysis to efficiently classify tumor subregions of glioblastoma: A pilot study in machine learning.J. Clin. Med.2021109203010.3390/jcm1009203034068528
     [Google Scholar]
  49. ChiuF.Y. YenY. Efficient radiomics-based classification of multi-parametric MR images to identify volumetric habitats and signatures in glioblastoma: A machine learning approach.Cancers2022146147510.3390/cancers1406147535326626
     [Google Scholar]
  50. BakasS. SakoC. AkbariH. BilelloM. SotirasA. ShuklaG. RudieJ.D. SantamaríaN.F. KazerooniA.F. PatiS. RathoreS. MamourianE. HaS.M. ParkerW. DoshiJ. BaidU. BergmanM. BinderZ.A. VermaR. LustigR.A. DesaiA.S. BagleyS.J. MourelatosZ. MorrissetteJ. WattC.D. BremS. WolfR.L. MelhemE.R. NasrallahM.L.P. MohanS. O’RourkeD.M. DavatzikosC. The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: Advanced MRI, clinical, genomics, & radiomics.Sci. Data20229145310.1038/s41597‑022‑01560‑7
     [Google Scholar]
  51. ShinI. KimH. AhnS.S. SohnB. BaeS. ParkJ.E. KimH.S. LeeS.K. Development and validation of a deep learning–based model to distinguish glioblastoma from solitary brain metastasis using conventional MR images.AJNR Am. J. Neuroradiol.202142583884410.3174/ajnr.A700333737268
     [Google Scholar]
  52. LaValleyM.P. Logistic Regression.Circulation2008117182395239910.1161/CIRCULATIONAHA.106.68265818458181
     [Google Scholar]
  53. ChenC. OuX. WangJ. GuoW. MaX. Radiomics-based machine learning in differentiation between glioblastoma and metastatic brain tumors.Front. Oncol.2019980610.3389/fonc.2019.0080631508366
     [Google Scholar]
  54. QianZ. LiY. WangY. LiL. LiR. WangK. LiS. TangK. ZhangC. FanX. ChenB. LiW. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.Cancer Lett.201945112813510.1016/j.canlet.2019.02.05430878526
     [Google Scholar]
  55. de CausansA. CarréA. RouxA. Tauziède-EspariatA. AmmariS. DezamisE. DhermainF. ReuzéS. DeutschE. OppenheimC. VarletP. PalludJ. EdjlaliM. RobertC. Development of a machine learning classifier based on radiomic features extracted from post-contrast 3D T1-Weighted MR images to distinguish glioblastoma from solitary brain metastasis.Front. Oncol.20211163826210.3389/fonc.2021.63826234327133
     [Google Scholar]
  56. FanY. ChenC. ZhaoF. TianZ. WangJ. MaX. XuJ. Radiomics-based machine learning technology enables better differentiation between glioblastoma and anaplastic oligodendroglioma.Front. Oncol.201991164116410.3389/fonc.2019.0116431750250
     [Google Scholar]
  57. KeG. MengQ. FinleyT. WangT. ChenW. Lightgbm: A highly efficient gradient boosting decision tree.Adv. Neural Inf. Process. Syst.201730
     [Google Scholar]
  58. XanthopoulosP. PardalosP. TrafalisT. Linear discriminant analysis.Robust Data MiningSpringer2013273310.1007/978‑1‑4419‑9878‑1_4
     [Google Scholar]
  59. ChenC. ZhengA. OuX. WangJ. MaX. Comparison of radiomics-based machine-learning classifiers in diagnosis of glioblastoma from primary central nervous system lymphoma.Front. Oncol.2020101151115110.3389/fonc.2020.0115133042784
     [Google Scholar]
  60. XiaW. HuB. LiH. ShiW. TangY. YuY. GengC. WuQ. YangL. YuZ. GengD. LiY. Deep learning for automatic differential diagnosis of primary central nervous system lymphoma and glioblastoma: Multi-parametric magnetic resonance imaging based convolutional neural network model.J. Magn. Reson. Imaging202154388088710.1002/jmri.2759233694250
     [Google Scholar]
  61. QianZ. ZhangL. HuJ. ChenS. ChenH. ShenH. ZhengF. ZangY. ChenX. Machine learning-based analysis of magnetic resonance radiomics for the classification of gliosarcoma and glioblastoma.Front. Oncol.20211169978910.3389/fonc.2021.69978934490097
     [Google Scholar]
  62. ShrotS. SalhovM. DvorskiN. KonenE. AverbuchA. HoffmannC. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.Neuroradiology201961775776510.1007/s00234‑019‑02195‑z30949746
     [Google Scholar]
  63. StadlbauerA. MarholdF. OberndorferS. HeinzG. BuchfelderM. KinfeT.M. Meyer-BäseA. Radiophysiomics: Brain tumors classification by machine learning and physiological mri data.Cancers20221410236310.3390/cancers1410236335625967
     [Google Scholar]
  64. RamchounH. IdrissiM.A.J. GhanouY. EttaouilM. Multilayer perceptron: Architecture optimization and training.Reunir2017711610.1145/3090354.3090427
     [Google Scholar]
  65. SwinburneN.C. ScheffleinJ. SakaiY. OermannE.K. TitanoJ.J. ChenI. TadayonS. AggarwalA. DoshiA. NaelK. Machine learning for semi­automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging.Ann. Transl. Med.201971123210.21037/atm.2018.08.0531317002
     [Google Scholar]
  66. WijethilakeN. IslamM. MeedeniyaD. ChitraranjanC. PereraI. RenH. Radiogenomics of glioblastoma: Identification of radiomics associated with molecular subtypes.Lect. Notes Comput. Sci.20201244922923910.1007/978‑3‑030‑66843‑3_22
     [Google Scholar]
  67. LeK. DoT. DangL. HuynhT.-T. Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI.Comput Biol Med202113210432010.1016/j.compbiomed.2021.104320
     [Google Scholar]
  68. Le FèvreC. LhermitteB. AhleG. ChambrelantI. CebulaH. AntoniD. KellerA. SchottR. ThieryA. ConstansJ.M. NoëlG. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review.Crit. Rev. Oncol. Hematol.202115710318810.1016/j.critrevonc.2020.10318833307200
     [Google Scholar]
  69. AkbariH. RathoreS. BakasS. NasrallahM.P. ShuklaG. MamourianE. RozyckiM. BagleyS.J. RudieJ.D. FlandersA.E. DickerA.P. DesaiA.S. O’RourkeD.M. BremS. LustigR. MohanS. WolfR.L. BilelloM. Martinez-LageM. DavatzikosC. Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma.Cancer2020126112625263610.1002/cncr.3279032129893
     [Google Scholar]
  70. SunY.Z. YanL.F. HanY. NanH.Y. XiaoG. TianQ. PuW.H. LiZ.Y. WeiX.C. WangW. CuiG.B. Differentiation of pseudoprogression from true progressionin glioblastoma patients after standard treatment: A machine learning strategy combinedwith radiomics features from T1-weighted contrast-enhanced imaging.BMC Med. Imaging20212111710.1186/s12880‑020‑00545‑533535988
     [Google Scholar]
  71. KingsfordC. SalzbergS.L. What are decision trees?Nat. Biotechnol.20082691011101310.1038/nbt0908‑101118779814
     [Google Scholar]
  72. YanJ.L. TohC.H. WeiK.C. ChenP.Y. A neural network approach to identify glioblastoma progression phenotype from multimodal MRI.Cancers2021139200610.3390/cancers13092006
     [Google Scholar]
  73. MoassefiM. FaghaniS. ConteG.M. KowalchukR.O. VahdatiS. CromptonD.J. Perez-VegaC. CabrejaR.A.D. VoraS.A. Quiñones-HinojosaA. ParneyI.F. TrifilettiD.M. EricksonB.J. A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients.J. Neurooncol.2022159244745510.1007/s11060‑022‑04080‑x35852738
     [Google Scholar]
  74. GeldofT. Van DammeN. HuysI. Van DyckW. Patient-level effectiveness prediction modeling for glioblastoma using classification trees.Front. Pharmacol.202010166510.3389/fphar.2019.0166532116674
     [Google Scholar]
  75. LiZ. WangY. YuJ. GuoY. ZhangQ. Age groups related glioblastoma study based on radiomics approach.Comput. Assist. Surg.201722sup1182510.1080/24699322.2017.137872228914549
     [Google Scholar]
  76. SabelM. Principles of adjuvant chemotherapy for breast cancer.Essentials of Breat Surgery.Mosby200924726610.1016/B978‑0‑323‑03758‑7.00016‑8
     [Google Scholar]
  77. BaeS. ChoiY.S. AhnS.S. ChangJ.H. KangS.G. KimE.H. KimS.H. LeeS.K. Radiomic MRI phenotyping of glioblastoma: Improving survival prediction.Radiology2018289379780610.1148/radiol.201818020030277442
     [Google Scholar]
  78. YoonH.G. CheonW. JeongS.W. KimH.S. KimK. NamH. HanY. LimD.H. Multi-parametric deep learning model for prediction of overall survival after postoperative concurrent chemoradiotherapy in glioblastoma patients.Cancers2020128228410.3390/cancers1208228432823939
     [Google Scholar]
  79. WankhedeD.S. SelvaraniR. Dynamic architecture based deep learning approach for glioblastoma brain tumor survival prediction.Neurosci. Inf.20222410006210.1016/j.neuri.2022.100062
     [Google Scholar]
  80. OsmanA.F.I. A multi-parametric MRI-based radiomics signature and a practical ML model for stratifying glioblastoma patients based on survival toward precision oncology.Front. Comput. Neurosci.201913585810.3389/fncom.2019.0005831507398
     [Google Scholar]
  81. HanW. QinL. BayC. ChenX. YuK.H. MiskinN. LiA. XuX. YoungG. Deep transfer learning and radiomics feature prediction of survival of patients with high-grade gliomas.AJNR Am. J. Neuroradiol.2020411404810.3174/ajnr.A636531857325
     [Google Scholar]
  82. XinJ. YixuanZ. DixiangS. A multiparametric MRI-based radiomics nomogram for preoperative prediction of survival stratification in glioblastoma patients with standard treatment.Front. Oncol.20221275862210.3389/fonc.2022.758622
     [Google Scholar]
  83. JajroudiM. EnferadiM. HomayounA.A. ReiaziR. MRI-based machine learning for determining quantitative and qualitative characteristics affecting the survival of glioblastoma multiforme.Magn. Reson. Imaging20228522222710.1016/j.mri.2021.10.02334687850
     [Google Scholar]
  84. LaoJ. ChenY. LiZ.C. LiQ. ZhangJ. LiuJ. ZhaiG. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme.Sci. Rep.2017711035310.1038/s41598‑017‑10649‑828871110
     [Google Scholar]
  85. Della PepaG.M. CaccavellaV.M. MennaG. IusT. AuricchioA.M. ChiesaS. GaudinoS. MarcheseE. OliviA. Machine learning–based prediction of 6-month postoperative karnofsky performance status in patients with glioblastoma: Capturing the real-life interaction of multiple clinical and oncologic factors.World Neurosurg.2021149e866e87610.1016/j.wneu.2021.01.08233516864
     [Google Scholar]
  86. LamichhaneB. DanielA.G.S. LeeJ.J. MarcusD.S. ShimonyJ.S. LeuthardtE.C. Machine learning analytics of resting-state functional connectivity predicts survival outcomes of glioblastoma multiforme patients.Front. Neurol.2021126422464224110.3389/fneur.2021.64224133692747
     [Google Scholar]
  87. HuL.S. YoonH. EschbacherJ.M. BaxterL.C. DueckA.C. NespodzanyA. SmithK.A. NakajiP. XuY. WangL. KarisJ.P. Hawkins-DaarudA.J. SingletonK.W. JacksonP.R. AnderiesB.J. BendokB.R. ZimmermanR.S. QuarlesC. Porter-UmphreyA.B. MrugalaM.M. SharmaA. HoxworthJ.M. SatturM.G. SanaiN. KoulemberisP.E. KrishnaC. MitchellJ.R. WuT. TranN.L. SwansonK.R. LiJ. Accurate patient-specific machine learning models of glioblastoma invasion using transfer learning.AJNR Am. J. Neuroradiol.201940341842510.3174/ajnr.A598130819771
     [Google Scholar]
  88. GatesE.D.H. WeinbergJ.S. PrabhuS.S. LinJ.S. HamiltonJ. HazleJ.D. FullerG.N. BaladandayuthapaniV. FuentesD.T. SchellingerhoutD. Estimating local cellular density in glioma using MR imaging data.AJNR Am. J. Neuroradiol.202142110210810.3174/ajnr.A688433243897
     [Google Scholar]
  89. WongK.K. RostomilyR. WongS.T.C. Prognostic gene discovery in glioblastoma patients using deep learning.Cancers20191115310.3390/cancers1101005330626092
     [Google Scholar]
  90. YoungJ.D. CaiC. LuX. Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.BMC Bioinformatics201718S1138110.1186/s12859‑017‑1798‑228984190
     [Google Scholar]
  91. DoD.T. YangM.R. LamL.H.T. LeN.Q.K. WuY.W. Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach.Sci. Rep.20221211341210.1038/s41598‑022‑17707‑w35927323
     [Google Scholar]
  92. LiZ.C. BaiH. SunQ. LiQ. LiuL. ZouY. ChenY. LiangC. ZhengH. Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.Eur. Radiol.20182893640365010.1007/s00330‑017‑5302‑129564594
     [Google Scholar]
  93. ChoiS. ChoH.H. KooH. ChoK. NenningK.H. Multi-habitat radiomics unravels distinct phenotypic subtypes of glioblastoma with clinical and genomic significance.Cancers2020127170710.3390/cancers12071707
     [Google Scholar]
  94. XinL. BoC. BinT. Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time.J Cell Mol Med20192364375438510.1111/jcmm.14328
     [Google Scholar]
  95. FataiA.A. GamieldienJ. A 35-gene signature discriminates between rapidly- and slowly-progressing glioblastoma multiforme and predicts survival in known subtypes of the cancer.BMC Cancer201818137710.1186/s12885‑018‑4103‑529614978
     [Google Scholar]
  96. WayG.P. AllawayR.J. BouleyS.J. FadulC.E. SanchezY. GreeneC.S. A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma.BMC Genomics201718112710.1186/s12864‑017‑3519‑728166733
     [Google Scholar]
  97. PasquiniL. NapolitanoA. LucignaniM. TaglienteE. DellepianeF. Rossi-EspagnetM.C. Comparison of machine learning classifiers to predict patient survival and genetics of GBM: Towards a standardized model for clinical implementation. cornell university.arXiv:2102.065262021
     [Google Scholar]
  98. HsuJ.B.K. LeeG.A. ChangT.H. HuangS.W. LeN.Q.K. ChenY.C. KuoD.P. LiY.T. ChenC.Y. Radiomic immunophenotyping of gsea-assessed immunophenotypes of glioblastoma and its implications for prognosis: A feasibility study.Cancers20201210303910.3390/cancers1210303933086550
     [Google Scholar]
  99. LeelatianN. SinnaeveJ. MistryA.M. BaroneS.M. BrockmanA.A. DigginsK.E. GreenplateA.R. WeaverK.D. ThompsonR.C. ChamblessL.B. MobleyB.C. IhrieR.A. IrishJ.M. Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells.eLife20209e5687910.7554/eLife.5687932573435
     [Google Scholar]
  100. VoortS. IncekaraF. WijnengaM. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.Neuro-Oncology000025227928910.1093/neuonc/noac166
     [Google Scholar]
  101. ParkJ.E. KimH.S. ParkS.Y. NamS.J. ChunS.M. JoY. KimJ.H. Prediction of core signaling pathway by using diffusion- and perfusion-based mri radiomics and next-generation sequencing in isocitrate dehydrogenase wild-type glioblastoma.Radiology2020294238839710.1148/radiol.201919091331845844
     [Google Scholar]
  102. LiZ.C. BaiH. SunQ. ZhaoY. LvY. ZhouJ. LiangC. ChenY. LiangD. ZhengH. Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma.Cancer Med.20187125999600910.1002/cam4.186330426720
     [Google Scholar]
  103. WuJ. HeS. ZhouS. Multi-atlas subcortical segmentation: An orchestration of 3D fully convolutional network and generalized mixture function.Mach. Vis. Appl.20233446410.1007/s00138‑023‑01415‑0
     [Google Scholar]
  104. WuJ. YangQ. ZhouS. Latent shape image learning via disentangled representation for cross-sequence image registration and segmentation.Int. J. CARS202218462162810.1007/s11548‑022‑02788‑936346499
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056265212231122102029
Loading
Machine Learning in Magnetic Resonance Images of Glioblastoma: A Review
Curr. Med. Imaging 20, e15734056265212 (2024); https://doi.org/10.2174/0115734056265212231122102029
/content/journals/cmir/10.2174/0115734056265212231122102029
/content/journals/cmir/10.2174/0115734056265212231122102029
Loading

Data & Media loading...

Supplements

PRISMA checklist is available as supplementary material on the publisher’s website along with the published article. Supplementary material is available on the publisher’s website along with the published article.

file format download Download
Keyword(s): Artificial intelligence; Deep learning; Glioblastoma; Machine learning; Magnetic resonance imaging; Overall survival

Most Cited Most Cited RSS feed

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