Skip to main content
Advertisement

Main menu

  • Home
  • Content
    • Current Issue
    • Accepted Manuscripts
    • Article Preview
    • Past Issue Archive
    • Video Articles
    • AJNR Case Collection
    • Case of the Week Archive
    • Case of the Month Archive
    • Classic Case Archive
  • Special Collections
    • AJNR Awards
    • Low-Field MRI
    • Alzheimer Disease
    • ASNR Foundation Special Collection
    • Photon-Counting CT
    • View All
  • Multimedia
    • AJNR Podcasts
    • AJNR SCANtastic
    • Trainee Corner
    • MRI Safety Corner
    • Imaging Protocols
  • For Authors
    • Submit a Manuscript
    • Submit a Video Article
    • Submit an eLetter to the Editor/Response
    • Manuscript Submission Guidelines
    • Statistical Tips
    • Fast Publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Imaging Protocol Submission
    • Author Policies
  • About Us
    • About AJNR
    • Editorial Board
    • Editorial Board Alumni
  • More
    • Become a Reviewer/Academy of Reviewers
    • Subscribers
    • Permissions
    • Alerts
    • Feedback
    • Advertisers
    • ASNR Home

User menu

  • Alerts
  • Log in

Search

  • Advanced search
American Journal of Neuroradiology
American Journal of Neuroradiology

American Journal of Neuroradiology

ASHNR American Society of Functional Neuroradiology ASHNR American Society of Pediatric Neuroradiology ASSR
  • Alerts
  • Log in

Advanced Search

  • Home
  • Content
    • Current Issue
    • Accepted Manuscripts
    • Article Preview
    • Past Issue Archive
    • Video Articles
    • AJNR Case Collection
    • Case of the Week Archive
    • Case of the Month Archive
    • Classic Case Archive
  • Special Collections
    • AJNR Awards
    • Low-Field MRI
    • Alzheimer Disease
    • ASNR Foundation Special Collection
    • Photon-Counting CT
    • View All
  • Multimedia
    • AJNR Podcasts
    • AJNR SCANtastic
    • Trainee Corner
    • MRI Safety Corner
    • Imaging Protocols
  • For Authors
    • Submit a Manuscript
    • Submit a Video Article
    • Submit an eLetter to the Editor/Response
    • Manuscript Submission Guidelines
    • Statistical Tips
    • Fast Publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Imaging Protocol Submission
    • Author Policies
  • About Us
    • About AJNR
    • Editorial Board
    • Editorial Board Alumni
  • More
    • Become a Reviewer/Academy of Reviewers
    • Subscribers
    • Permissions
    • Alerts
    • Feedback
    • Advertisers
    • ASNR Home
  • Follow AJNR on Twitter
  • Visit AJNR on Facebook
  • Follow AJNR on Instagram
  • Join AJNR on LinkedIn
  • RSS Feeds

AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

Research ArticleAdult Brain
Open Access

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

A.P. Bhandari, R. Liong, J. Koppen, S.V. Murthy and A. Lasocki
American Journal of Neuroradiology January 2021, 42 (1) 94-101; DOI: https://doi.org/10.3174/ajnr.A6875
A.P. Bhandari
aFrom the Department of Anatomy (A.P.B.)
cTownsville University Hospital (A.P.B., J.K.), Douglas, Queensland, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for A.P. Bhandari
R. Liong
dDepartment of Medical Imaging Research Office (R.L.), Royal Brisbane and Women’s Hospital, Herston, Queensland, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for R. Liong
J. Koppen
cTownsville University Hospital (A.P.B., J.K.), Douglas, Queensland, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for J. Koppen
S.V. Murthy
bCollege of Medicine and Dentistry (S.V.M.), James Cook University, Townsville, Queensland, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for S.V. Murthy
A. Lasocki
eDepartment of Cancer Imaging (A.L.), Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
fSir Peter MacCallum Department of Oncology (A.L.), The University of Melbourne, Melbourne, Victoria, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for A. Lasocki
  • Article
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • Responses
  • References
  • PDF
Loading

References

  1. 1.↵
    1. Stark AM,
    2. van de Bergh J,
    3. Hedderich J, et al
    . Glioblastoma: clinical characteristics, prognostic factors and survival in 492 patients. Clin Neurol Neurosurg 2012;114:840–45 doi:10.1016/j.clineuro.2012.01.026 pmid:22377333
    CrossRefPubMed
  2. 2.↵
    1. Louis DN,
    2. Perry A,
    3. Reifenberger G, et al
    . The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016;131:803–20 doi:10.1007/s00401-016-1545-1 pmid:27157931
    CrossRefPubMed
  3. 3.↵
    1. Akay A,
    2. Rüksen M,
    3. Islekel S
    . Magnetic resonance imaging-guided stereotactic biopsy: a review of 83 cases with outcomes. Asian J Neurosurg 2019;14:90–95 doi:10.4103/ajns.AJNS_81_17 pmid:30937016
    CrossRefPubMed
  4. 4.↵
    1. Lasocki A,
    2. Tsui A,
    3. Tacey MA, et al
    . MRI grading versus histology: predicting survival of World Health Organization grade II-IV astrocytomas. AJNR Am J Neuroradiol 2015;36:77–83 doi:10.3174/ajnr.A4077 pmid:25104288
    Abstract/FREE Full Text
  5. 5.↵
    1. Horbinski C
    . What do we know about IDH 1/2 mutations so far, and how do we use it? Acta Neuropathol 2013;125:621–36 doi:10.1007/s00401-013-1106-9 pmid:23512379
    CrossRefPubMedWeb of Science
  6. 6.↵
    1. Preusser M,
    2. Wöhrer A,
    3. Stary S, et al
    . Value and limitations of immunohistochemistry and gene sequencing for detection of the IDH 1-R132H mutation in diffuse glioma biopsy specimens. J Neuropathol Exp Neurol 2011;70:715–23 doi:10.1097/NEN.0b013e31822713f0 pmid:21760534
    CrossRefPubMed
  7. 7.↵
    1. Broen MPG,
    2. Smits M,
    3. Wijnenga MM, et al
    . The T2-FLAIR mismatch sign as an imaging marker for non-enhancing IDH-mutant, 1p/19q-intact lower-grade glioma: a validation study. Neuro Oncol 2018;20:1393–99 doi:10.1093/neuonc/noy048 pmid:29590424
    CrossRefPubMed
  8. 8.↵
    1. Lasocki A,
    2. Gaillard F,
    3. Gorelik A, et al
    . MRI features can predict 1p/19q status in intracranial gliomas. AJNR Am J Neuroradiol 2018;39:687–92 doi:10.3174/ajnr.A5572 pmid:29519793
    Abstract/FREE Full Text
  9. 9.↵
    1. Patel SH,
    2. Poisson LM,
    3. Brat DJ, et al
    . T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA Project. Clin Cancer Res 2017;23:6078–85 doi:10.1158/1078-0432.CCR-17-0560 pmid:28751449
    Abstract/FREE Full Text
  10. 10.↵
    1. Johnson DR,
    2. Diehn FE,
    3. Giannini C, et al
    . Genetically defined oligodendroglioma is characterized by indistinct tumor borders at MRI. AJNR Am J Neuroradiol 2017;38:678–84 doi:10.3174/ajnr.A5070 pmid:28126746
    Abstract/FREE Full Text
  11. 11.↵
    1. Gillies RJ,
    2. Kinahan PE,
    3. Hricak H
    . Radiomics: images are more than pictures: they are data. Radiology 2016;278:563–77 doi:10.1148/radiol.2015151169 pmid:26579733
    CrossRefPubMed
  12. 12.↵
    1. Lambin P,
    2. Leijenaar RT,
    3. Deist TM, et al
    . Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017;14:749–62 doi:10.1038/nrclinonc.2017.141 pmid:28975929
    CrossRefPubMed
  13. 13.↵
    1. van den Bent MJ
    . Interobserver variation of the histopathological diagnosis in clinical trials on glioma: a clinician's perspective. Acta Neuropathol 2010;120:297–304 doi:10.1007/s00401-010-0725-7 pmid:20644945
    CrossRefPubMedWeb of Science
  14. 14.↵
    1. McInnes MD,
    2. Moher D,
    3. Thombs BD, et al
    ; PRISMA-DTA Group. Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: the PRISMA-DTA Statement. JAMA 2018;319:388–96 doi:10.1001/jama.2017.19163 pmid:29362800
    CrossRefPubMed
  15. 15.↵
    1. Matsui Y,
    2. Maruyama T,
    3. Nitta M, et al
    . Prediction of lower-grade glioma molecular subtypes using deep learning. J Neurooncol 2020;146:321–27 doi:10.1007/s11060-019-03376-9 pmid:31865510
    CrossRefPubMed
  16. 16.↵
    1. Zhou XH,
    2. McClish DK,
    3. Obuchowski NA, et al
    . Statistical Methods in Diagnostic Medicine. John Wiley & Sons; 2011
  17. 17.↵
    1. Whiting PF,
    2. Rutjes AW,
    3. Westwood ME, et al
    ; QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011;155:529–36 doi:10.7326/0003-4819-155-8-201110180-00009 pmid:22007046
    CrossRefPubMedWeb of Science
  18. 18.↵
    1. Collins GS,
    2. Reitsma JB,
    3. Altman DG, et al
    . Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). Ann Intern Med 2015;162:735–36 doi:10.7326/L15-5093-2 pmid:25984857
    CrossRefPubMed
  19. 19.↵
    1. McHugh ML
    . Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 2012;22:276–82 doi:10.11613/BM.2012.031 pmid:23092060
    CrossRefPubMedWeb of Science
  20. 20.↵
    1. Ursprung S,
    2. Beer L,
    3. Bruining A, et al
    . Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis. Eur Radiol 2020;30:3558–66 doi:10.1007/s00330-020-06666-3 pmid:32060715
    CrossRefPubMed
  21. 21.↵
    1. Park JE,
    2. Kim HS,
    3. Kim D, et al
    . A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features. BMC Cancer 2020;20:29 doi:10.1186/s12885-019-6504-5 pmid:31924170
    CrossRefPubMed
  22. 22.↵
    1. Fukuma R,
    2. Yanagisawa T,
    3. Kinoshita M, et al
    . Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network. Sci Rep 2019;9:20311 doi:10.1038/s41598-019-56767-3 pmid:31889117
    CrossRefPubMed
  23. 23.↵
    1. Gihr GA,
    2. Horvath-Rizea D,
    3. Hekeler E, et al
    . Histogram analysis of diffusion weighted imaging in low-grade gliomas: in vivo characterization of tumor architecture and corresponding neuropathology. Front Oncol 2020;10:206 doi:10.3389/fonc.2020.00206 pmid:32158691
    CrossRefPubMed
  24. 24.↵
    1. Jakola AS,
    2. Zhang YH,
    3. Skjulsvik AJ, et al
    . Quantitative texture analysis in the prediction of IDH status in low-grade gliomas. Clin Neurol Neurosurg 2018;164:114–20 doi:10.1016/j.clineuro.2017.12.007 pmid:29220731
    CrossRefPubMed
  25. 25.↵
    1. Kim M,
    2. Jung SY,
    3. 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. Eur Radiol 2020;30:2142–51 doi:10.1007/s00330-019-06548-3 pmid:31828414
    CrossRefPubMed
  26. 26.↵
    1. Kocak B,
    2. Durmaz ES,
    3. Ates E, et al
    . Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status. Eur Radiol 2020;30:877–86 doi:10.1007/s00330-019-06492-2 pmid:31691122
    CrossRefPubMed
  27. 27.↵
    1. Li Z,
    2. Wang Y,
    3. Yu J, et al
    . Deep Learning-Based Radiomics (DLR) and its usage in noninvasive IDH 1 prediction for low grade glioma. Sci Rep 2017;7:5467 doi:10.1038/s41598-017-05848-2 pmid:28710497
    CrossRefPubMed
  28. 28.↵
    1. Lu CF,
    2. Hsu FT,
    3. Hsieh KLC, et al
    . Machine learning–based radiomics for molecular subtyping of gliomas. Clin Cancer Res 2018;24:4429–36 doi:10.1158/1078-0432.CCR-17-3445 pmid:29789422
    Abstract/FREE Full Text
  29. 29.↵
    1. Park CJ,
    2. Choi YS,
    3. Park YW, et al
    . Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status. Neuroradiology 2020;62:319–26 doi:10.1007/s00234-019-02312-y pmid:31820065
    CrossRefPubMed
  30. 30.↵
    1. Ren Y,
    2. Zhang X,
    3. Rui W, et al
    . Noninvasive prediction of IDH 1 mutation and ATRX expression loss in low-grade gliomas using multiparametric MR radiomic features. J Magn Reson Imaging 2019;49:808–17 doi:10.1002/jmri.26240 pmid:30194745
    CrossRefPubMed
  31. 31.↵
    1. Shofty B,
    2. Artzi M,
    3. Ben Bashat D, et al
    . MRI radiomics analysis of molecular alterations in low-grade gliomas. Int J Comput Assist Radiol Surg 2018;13:563–71 doi:10.1007/s11548-017-1691-5 pmid:29270916
    CrossRefPubMed
  32. 32.↵
    1. Yu J,
    2. Shi Z,
    3. Lian Y, et al
    . Noninvasive IDH 1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 2017;27:3509–22 doi:10.1007/s00330-016-4653-3 pmid:28004160
    CrossRefPubMed
  33. 33.↵
    1. Zhang X,
    2. Tian Q,
    3. Wang L, et al
    . Radiomics strategy for molecular subtype stratification of lower-grade glioma: detecting IDH and TP53 mutations based on multimodal MRI. J Magn Reson Imaging 2018;48:916–26 doi:10.1002/jmri.25960 pmid:29394005
    CrossRefPubMed
  34. 34.↵
    1. Zhou H,
    2. Vallieres M,
    3. Bai H, et al
    . MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 2017;19:862–70 doi:10.1093/neuonc/now256 pmid:28339588
    CrossRefPubMed
  35. 35.↵
    1. Han Y,
    2. Wang W,
    3. Yang Y, et al
    . Amide proton transfer imaging in predicting isocitrate dehydrogenase 1 mutation status of grade II/III gliomas based on support vector machine. Front Neurosci 2020;14:144 doi:10.3389/fnins.2020.00144 pmid:32153362
    CrossRefPubMed
  36. 36.↵
    1. Bakas S,
    2. Akbari H,
    3. Sotiras A, et al
    . Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 2017;4:170117 doi:10.1038/sdata.2017.117 pmid:28872634
    CrossRefPubMed
  37. 37.
    1. Bakas S,
    2. Reyes M,
    3. Jakab A, et al
    . Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. 2018 https://arxiv.org/abs/1811.02629. Accessed April 18, 2020
  38. 38.↵
    1. Menze BH,
    2. Jakab A,
    3. Bauer S, et al
    . The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging 2015;34:1993–2024 doi:10.1109/TMI.2014.2377694 pmid:25494501
    CrossRefPubMed
  39. 39.↵
    1. Myronenko A
    . 3D MRI brain tumor segmentation using autoencoder regularization. International MICCAI Brainlesion Workshop: Springer; 2018:311–20
  40. 40.↵
    1. Zhang Z,
    2. Xiao J,
    3. Wu S, et al
    . Deep convolutional radiomic features on diffusion tensor images for classification of glioma grades. J Digit Imaging.2020;33:826–37 doi:10.1007/s10278-020-00322-4 pmid:32040669
    CrossRefPubMed
  41. 41.↵
    1. Higgins JP,
    2. Thomas J,
    3. Chandler J, et al
    . Cochrane Handbook for Systematic Reviews of Interventions. John Wiley & Sons; 2019
  42. 42.↵
    1. van der Voort SR,
    2. Incekara F,
    3. Wijnenga MM, et al
    . Predicting the 1p/19q codeletion status of presumed low-grade glioma with an externally validated machine learning algorithm. Clin Cancer Res 2019;25:7455–62 doi:10.1158/1078-0432.CCR-19-1127 pmid:31548344
    Abstract/FREE Full Text
  43. 43.↵
    1. Delfanti RL,
    2. Piccioni DE,
    3. Handwerker J, et al
    . Imaging correlates for the 2016 update on WHO classification of grade II/III gliomas: implications for IDH, 1p/19q and ATRX status. J Neurooncol 2017;135:601–09 doi:10.1007/s11060-017-2613-7 pmid:28871469
    CrossRefPubMed
  44. 44.↵
    1. Zhou Q,
    2. Cao YH,
    3. Chen ZH
    . Lack of evidence and criteria to evaluate artificial intelligence and radiomics tools to be implemented in clinical settings. Eur J Nucl Med Mol Imaging 2019;46:2812–13 doi:10.1007/s00259-019-04493-3 pmid:31696246
    CrossRefPubMed
  45. 45.↵
    1. Lew C
    . The promise of clinical AI: an adaptive future. Applied Radiol 2018;47:3–6 https://www.appliedradiology.com/articles/the-promise-of-clinical-ai-an-adaptive-future#:∼:text=The%20Promise%20of%20Clinical%20AI%3A%20An%20Adaptive%20Future.,predictive%20analytics%20for%20population%20health%20management%2C%20and%20beyond. Accessed April 18, 2020
  46. 46.
    1. Sana M
    . Machine learning and artificial intelligence in radiology. J Am Coll Radiol 2018;15:1139–42 doi:10.1016/j.jacr.2017.11.015 pmid:29275917
    CrossRefPubMed
  47. 47.
    1. Shader RI
    . Some thoughts about artificial intelligence. Clin Ther 2019;41:1401–03 doi:10.1016/j.clinthera.2019.06.016 pmid:31345558
    CrossRefPubMed
  48. 48.↵
    1. Topol EJ
    . High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44–56 doi:10.1038/s41591-018-0300-7 pmid:30617339
    CrossRefPubMed
  49. 49.↵
    European Society of Radiology. Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights imaging 2019;10:105 doi:10.1186/s13244-019-0798-3 pmid:31673823
    CrossRefPubMed
  50. 50.
    1. van Hoek J,
    2. Huber A,
    3. Leichtle A, et al
    . A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol 2019;121:108742 doi:10.1016/j.ejrad.2019.108742 pmid:31734640
    CrossRefPubMed
  51. 51.↵
    1. Waymel Q,
    2. Badr S,
    3. Demondion X, et al
    . Impact of the rise of artificial intelligence in radiology: what do radiologists think? Diagn Interv Imaging 2019;100:327–36 doi:10.1016/j.diii.2019.03.015 pmid:31072803
    CrossRefPubMed
  52. 52.↵
    1. Palmisciano P,
    2. Jamjoom AA,
    3. Taylor D, et al
    . Attitudes of patients and their relatives toward artificial intelligence in neurosurgery. World Neurosurg 2020;138:e627–33 doi:10.1016/j.wneu.2020.03.029 pmid:32179185
    CrossRefPubMed
  53. 53.↵
    1. Likert R
    . A technique for the measurement of attitudes. Archives of Psychology 1932;22:55
  54. 54.↵
    1. Ongena YP,
    2. Haan M,
    3. Yakar D, et al
    . Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire. Eur Radiol 2020;30:1033–40 doi:10.1007/s00330-019-06486-0 pmid:31705254
    CrossRefPubMed
  55. 55.↵
    1. Janssen CP,
    2. Donker SF,
    3. Brumby DP, et al
    . History and future of human-automation interaction. International Journal of Human-Computer Studies 2019;131:99–107 doi:10.1016/j.ijhcs.2019.05.006
    CrossRef
  56. 56.↵
    IBM Services. Beyond the hype: a guide to understanding and successfully implementing artificial intelligence within your business. https://www.ibm.com/downloads/cas/8ZDXNKQ4. 2018. Accessed April 18, 2020
  57. 57.↵
    1. Amershi S,
    2. Weld D,
    3. Vorvoreanu M, et al
    . Guidelines for human-AI interaction. In: Proceedings of the Cultural Homestay International Conference on Human Factors in Computing System, Glasgow, UK; May 4–9, 2019
  58. 58.↵
    1. Friedman LM.
    2. Furberg CD,
    3. DeMets Dl, et al
    . Fundamentals of Clinical Trials. Springer-Verlag; 2010
  59. 59.↵
    1. He J,
    2. Baxter SL,
    3. Xu J, et al
    . The practical implementation of artificial intelligence technologies in medicine. Nat Med 2019;25:30–36 doi:10.1038/s41591-018-0307-0 pmid:30617336
    CrossRefPubMed
PreviousNext
Back to top

In this issue

American Journal of Neuroradiology: 42 (1)
American Journal of Neuroradiology
Vol. 42, Issue 1
1 Jan 2021
  • Table of Contents
  • Index by author
  • Complete Issue (PDF)
Advertisement
Print
Download PDF
Email Article

Thank you for your interest in spreading the word on American Journal of Neuroradiology.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review
(Your Name) has sent you a message from American Journal of Neuroradiology
(Your Name) thought you would like to see the American Journal of Neuroradiology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Cite this article
A.P. Bhandari, R. Liong, J. Koppen, S.V. Murthy, A. Lasocki
Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review
American Journal of Neuroradiology Jan 2021, 42 (1) 94-101; DOI: 10.3174/ajnr.A6875

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
0 Responses
Respond to this article
Share
Bookmark this article
Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review
A.P. Bhandari, R. Liong, J. Koppen, S.V. Murthy, A. Lasocki
American Journal of Neuroradiology Jan 2021, 42 (1) 94-101; DOI: 10.3174/ajnr.A6875
del.icio.us logo Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Purchase

Jump to section

  • Article
    • Abstract
    • ABBREVIATIONS:
    • MATERIALS AND METHODS
    • RESULTS
    • DISCUSSION
    • CONCLUSIONS
    • Footnotes
    • References
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • Responses
  • References
  • PDF

Related Articles

  • PubMed
  • Google Scholar

Cited By...

  • Automated Determination of the H3 K27-Altered Status in Spinal Cord Diffuse Midline Glioma by Radiomics Based on T2-Weighted MR Images
  • Radiogenomics Provides Insights into Gliomas Demonstrating Single-Arm 1p or 19q Deletion
  • Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
  • Crossref (58)
  • Google Scholar

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

  • Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
    Xingping Zhang, Yanchun Zhang, Guijuan Zhang, Xingting Qiu, Wenjun Tan, Xiaoxia Yin, Liefa Liao
    Frontiers in Oncology 2022 12
  • Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative
    Gaia Spadarella, Arnaldo Stanzione, Tugba Akinci D’Antonoli, Anna Andreychenko, Salvatore Claudio Fanni, Lorenzo Ugga, Elmar Kotter, Renato Cuocolo
    European Radiology 2022 33 3
  • MRI biomarkers in neuro-oncology
    Marion Smits
    Nature Reviews Neurology 2021 17 8
  • MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift
    Ahmed Habib, Nicolina Jovanovich, Meagan Hoppe, Murat Ak, Priyadarshini Mamindla, Rivka R. Colen, Pascal O. Zinn
    Journal of Clinical Medicine 2021 10 7
  • Multicenter DSC–MRI-Based Radiomics Predict IDH Mutation in Gliomas
    Georgios C. Manikis, Georgios S. Ioannidis, Loizos Siakallis, Katerina Nikiforaki, Michael Iv, Diana Vozlic, Katarina Surlan-Popovic, Max Wintermark, Sotirios Bisdas, Kostas Marias
    Cancers 2021 13 16
  • Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis
    Evi J. van Kempen, Max Post, Manoj Mannil, Benno Kusters, Mark ter Laan, Frederick J. A. Meijer, Dylan J. H. A. Henssen
    Cancers 2021 13 11
  • Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas
    Sedat Giray Kandemirli, Burak Kocak, Shotaro Naganawa, Kerem Ozturk, Stephen S.F. Yip, Saurav Chopra, Luciano Rivetti, Amro Saad Aldine, Karra Jones, Zuzan Cayci, Toshio Moritani, Takashi Shawn Sato
    World Neurosurgery 2021 151
  • Multi-Modal Learning for Predicting the Genotype of Glioma
    Yiran Wei, Xi Chen, Lei Zhu, Lipei Zhang, Carola-Bibiane Schönlieb, Stephen Price, Chao Li
    IEEE Transactions on Medical Imaging 2023 42 11
  • Conventional MRI features can predict the molecular subtype of adult grade 2–3 intracranial diffuse gliomas
    Arian Lasocki, Michael E. Buckland, Katharine J. Drummond, Heng Wei, Jing Xie, Michael Christie, Andrew Neal, Frank Gaillard
    Neuroradiology 2022 64 12
  • Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients
    Roberto Casale, Elizaveta Lavrova, Sebastian Sanduleanu, Henry C. Woodruff, Philippe Lambin
    European Journal of Radiology 2021 139

More in this TOC Section

Adult Brain

  • Diagnostic Neuroradiology of Monoclonal Antibodies
  • NCCT vs. MRI for Brain Atrophy in Acute Stroke
  • Clinical Outcomes After Chiari I Decompression
Show more Adult Brain

Functional

  • Kurtosis and Epileptogenic Tubers: A Pilot Study
  • Glutaric Aciduria Type 1: DK vs. Conventional MRI
  • Predicting Outcomes in Tuberous Sclerosis Epilepsy
Show more Functional

Similar Articles

Advertisement

Indexed Content

  • Current Issue
  • Accepted Manuscripts
  • Article Preview
  • Past Issues
  • Editorials
  • Editor's Choice
  • Fellows' Journal Club
  • Letters to the Editor
  • Video Articles

Cases

  • Case Collection
  • Archive - Case of the Week
  • Archive - Case of the Month
  • Archive - Classic Case

More from AJNR

  • Trainee Corner
  • Imaging Protocols
  • MRI Safety Corner
  • Book Reviews

Multimedia

  • AJNR Podcasts
  • AJNR Scantastics

Resources

  • Turnaround Time
  • Submit a Manuscript
  • Submit a Video Article
  • Submit an eLetter to the Editor/Response
  • Manuscript Submission Guidelines
  • Statistical Tips
  • Fast Publishing of Accepted Manuscripts
  • Graphical Abstract Preparation
  • Imaging Protocol Submission
  • Evidence-Based Medicine Level Guide
  • Publishing Checklists
  • Author Policies
  • Become a Reviewer/Academy of Reviewers
  • News and Updates

About Us

  • About AJNR
  • Editorial Board
  • Editorial Board Alumni
  • Alerts
  • Permissions
  • Not an AJNR Subscriber? Join Now
  • Advertise with Us
  • Librarian Resources
  • Feedback
  • Terms and Conditions
  • AJNR Editorial Board Alumni

American Society of Neuroradiology

  • Not an ASNR Member? Join Now

© 2025 by the American Society of Neuroradiology All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Print ISSN: 0195-6108 Online ISSN: 1936-959X

Powered by HighWire