Skip to main content
Advertisement

Main menu

  • Home
  • Content
    • Current Issue
    • Accepted Manuscripts
    • Article Preview
    • Past Issue Archive
    • AJNR Case Collection
    • Case of the Week Archive
    • Classic Case Archive
    • Case of the Month Archive
  • Special Collections
    • Spinal CSF Leak Articles (Jan 2020-June 2024)
    • 2024 AJNR Journal Awards
    • Most Impactful AJNR Articles
  • Multimedia
    • AJNR Podcast
    • AJNR Scantastics
    • Video Articles
  • For Authors
    • Submit a Manuscript
    • Author Policies
    • Fast publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Manuscript Submission Guidelines
    • Imaging Protocol Submission
    • Submit a Case for the Case Collection
  • About Us
    • About AJNR
    • Editorial Board
  • More
    • Become a Reviewer/Academy of Reviewers
    • Subscribers
    • Permissions
    • Alerts
    • Feedback
    • Advertisers
    • ASNR Home
  • Other Publications
    • ajnr

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
    • AJNR Case Collection
    • Case of the Week Archive
    • Classic Case Archive
    • Case of the Month Archive
  • Special Collections
    • Spinal CSF Leak Articles (Jan 2020-June 2024)
    • 2024 AJNR Journal Awards
    • Most Impactful AJNR Articles
  • Multimedia
    • AJNR Podcast
    • AJNR Scantastics
    • Video Articles
  • For Authors
    • Submit a Manuscript
    • Author Policies
    • Fast publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Manuscript Submission Guidelines
    • Imaging Protocol Submission
    • Submit a Case for the Case Collection
  • About Us
    • About AJNR
    • Editorial Board
  • 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

Welcome to the new AJNR, Updated Hall of Fame, and more. Read the full announcements.


AJNR is seeking candidates for the position of Associate Section Editor, AJNR Case Collection. Read the full announcement.

 

Getting new auth cookie, if you see this message a lot, tell someone!
Research ArticleBrain
Open Access

Prediction of Pseudoprogression in Patients with Glioblastomas Using the Initial and Final Area Under the Curves Ratio Derived from Dynamic Contrast-Enhanced T1-Weighted Perfusion MR Imaging

C.H. Suh, H.S. Kim, Y.J. Choi, N. Kim and S.J. Kim
American Journal of Neuroradiology December 2013, 34 (12) 2278-2286; DOI: https://doi.org/10.3174/ajnr.A3634
C.H. Suh
aFrom the Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
H.S. Kim
aFrom the Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Y.J. Choi
aFrom the Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
N. Kim
aFrom the Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
S.J. Kim
aFrom the Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • Responses
  • References
  • PDF
Loading

REFERENCES

  1. 1.↵
    1. Gasparetto EL,
    2. Pawlak MA,
    3. Patel SH,
    4. et al
    . Posttreatment recurrence of malignant brain neoplasm: accuracy of relative cerebral blood volume fraction in discriminating low from high malignant histologic volume fraction. Radiology 2009;250:887–96
    CrossRefPubMedWeb of Science
  2. 2.↵
    1. Chamberlain MC
    . Pseudoprogression in glioblastoma. J Clin Oncol 2008;26:4359; author reply 4360
    FREE Full Text
  3. 3.↵
    1. Fatterpekar GM,
    2. Galheigo D,
    3. Narayana A,
    4. et al
    . Treatment-related change versus tumor recurrence in high-grade gliomas: a diagnostic conundrum–use of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI. AJR Am J Roentgenol 2012;198:19–26
    CrossRefPubMed
  4. 4.↵
    1. Hu X,
    2. Wong KK,
    3. Young GS,
    4. et al
    . Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 2011;33:296–305
    CrossRefPubMed
  5. 5.↵
    1. Nihashi T,
    2. Dahabreh IJ,
    3. Terasawa T
    . Diagnostic accuracy of PET for recurrent glioma diagnosis: a meta-analysis. AJNR Am J Neuroradiol 2013;34:944–50
    Abstract/FREE Full Text
  6. 6.↵
    1. Roberts C,
    2. Issa B,
    3. Stone A,
    4. et al
    . Comparative study into the robustness of compartmental modeling and model-free analysis in DCE-MRI studies. J Magn Reson Imaging 2006;23:554–63
    CrossRefPubMedWeb of Science
  7. 7.↵
    1. Cheng HL
    . Improved correlation to quantitative DCE-MRI pharmacokinetic parameters using a modified initial area under the uptake curve (mIAUC) approach. J Magn Reson Imaging 2009;30:864–72
    CrossRefPubMed
  8. 8.↵
    1. Liu G,
    2. Rugo HS,
    3. Wilding G,
    4. et al
    . Dynamic contrast-enhanced magnetic resonance imaging as a pharmacodynamic measure of response after acute dosing of AG-013736, an oral angiogenesis inhibitor, in patients with advanced solid tumors: results from a phase I study. J Clin Oncol 2005;23:5464–73
    Abstract/FREE Full Text
  9. 9.↵
    1. Pope WB,
    2. Kim HJ,
    3. Huo J,
    4. et al
    . Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009;252:182–89
    CrossRefPubMedWeb of Science
  10. 10.↵
    1. Wen PY,
    2. Macdonald DR,
    3. Reardon DA,
    4. et al
    . Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010;28:1963–72
    Abstract/FREE Full Text
  11. 11.↵
    1. Evelhoch JL,
    2. LoRusso PM,
    3. He Z,
    4. et al
    . Magnetic resonance imaging measurements of the response of murine and human tumors to the vascular-targeting agent ZD6126. Clin Cancer Res 2004;10:3650–57
    Abstract/FREE Full Text
  12. 12.↵
    1. Tozer DJ,
    2. Jager HR,
    3. Danchaivijitr N,
    4. et al
    . Apparent diffusion coefficient histograms may predict low-grade glioma subtype. NMR Biomed 2007;20:49–57
    CrossRefPubMedWeb of Science
  13. 13.↵
    1. DeLong ER,
    2. DeLong DM,
    3. Clarke-Pearson DL
    . Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–45
    CrossRefPubMedWeb of Science
  14. 14.↵
    1. Evelhoch JL
    . Key factors in the acquisition of contrast kinetic data for oncology. J Magn Reson Imaging 1999;10:254–59
    CrossRefPubMedWeb of Science
  15. 15.↵
    1. Narang J,
    2. Jain R,
    3. Arbab AS,
    4. et al
    . Differentiating treatment-induced necrosis from recurrent/progressive brain tumor using nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion. Neuro Oncol 2011;13:1037–46
    Abstract/FREE Full Text
  16. 16.↵
    1. Kiselev VG
    . On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI. Magn Reson Med 2001;46:1113–22
    CrossRefPubMedWeb of Science
  17. 17.↵
    1. Larsen VA,
    2. Simonsen HJ,
    3. Law I,
    4. et al
    . Evaluation of dynamic contrast-enhanced T1-weighted perfusion MRI in the differentiation of tumor recurrence from radiation necrosis. Neuroradiology 2013;55:361–69
    CrossRefPubMed
  18. 18.↵
    1. Walker-Samuel S,
    2. Leach MO,
    3. Collins DJ
    . Evaluation of response to treatment using DCE-MRI: the relationship between initial area under the gadolinium curve (IAUGC) and quantitative pharmacokinetic analysis. Phys Med Biol 2006;51:3593–602
    CrossRefPubMedWeb of Science
  19. 19.↵
    1. Bisdas S,
    2. Naegele T,
    3. Ritz R,
    4. et al
    . Distinguishing recurrent high-grade gliomas from radiation injury: a pilot study using dynamic contrast-enhanced MR imaging. Acad Radiol 2011;18:575–83
    CrossRefPubMed
  20. 20.↵
    1. Levy LM
    . What is right about MRI permeability studies. AJNR Am J Neuroradiol 2005;26:3–4
    FREE Full Text
  21. 21.↵
    1. Hu LS,
    2. Baxter LC,
    3. Smith KA,
    4. et al
    . Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements. AJNR Am J Neuroradiol 2009;30:552–58
    Abstract/FREE Full Text
PreviousNext
Back to top

In this issue

American Journal of Neuroradiology: 34 (12)
American Journal of Neuroradiology
Vol. 34, Issue 12
1 Dec 2013
  • 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.
Prediction of Pseudoprogression in Patients with Glioblastomas Using the Initial and Final Area Under the Curves Ratio Derived from Dynamic Contrast-Enhanced T1-Weighted Perfusion MR Imaging
(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
C.H. Suh, H.S. Kim, Y.J. Choi, N. Kim, S.J. Kim
Prediction of Pseudoprogression in Patients with Glioblastomas Using the Initial and Final Area Under the Curves Ratio Derived from Dynamic Contrast-Enhanced T1-Weighted Perfusion MR Imaging
American Journal of Neuroradiology Dec 2013, 34 (12) 2278-2286; DOI: 10.3174/ajnr.A3634

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
Prediction of Pseudoprogression in Patients with Glioblastomas Using the Initial and Final Area Under the Curves Ratio Derived from Dynamic Contrast-Enhanced T1-Weighted Perfusion MR Imaging
C.H. Suh, H.S. Kim, Y.J. Choi, N. Kim, S.J. Kim
American Journal of Neuroradiology Dec 2013, 34 (12) 2278-2286; DOI: 10.3174/ajnr.A3634
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
  • Info & Metrics
  • Responses
  • References
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • Structural and practical identifiability of contrast transport models for DCE-MRI
  • Response Assessment in Neuro-Oncology Criteria for Gliomas: Practical Approach Using Conventional and Advanced Techniques
  • Detection of Local Recurrence in Patients with Head and Neck Squamous Cell Carcinoma Using Voxel-Based Color Maps of Initial and Final Area under the Curve Values Derived from DCE-MRI
  • Quantitative Evaluation for Differentiating Malignant and Benign Thyroid Nodules Using Histogram Analysis of Grayscale Sonograms
  • Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI
  • ASFNR Recommendations for Clinical Performance of MR Dynamic Susceptibility Contrast Perfusion Imaging of the Brain
  • Diffusion and Perfusion MRI to Differentiate Treatment-Related Changes Including Pseudoprogression from Recurrent Tumors in High-Grade Gliomas with Histopathologic Evidence
  • Crossref (79)
  • Google Scholar

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

  • MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis
    Praneil Patel, Hediyeh Baradaran, Diana Delgado, Gulce Askin, Paul Christos, Apostolos John Tsiouris, Ajay Gupta
    Neuro-Oncology 2017 19 1
  • Diagnostic accuracy of magnetic resonance imaging techniques for treatment response evaluation in patients with high-grade glioma, a systematic review and meta-analysis
    Bart R. J. van Dijken, Peter Jan van Laar, Gea A. Holtman, Anouk van der Hoorn
    European Radiology 2017 27 10
  • ASFNR Recommendations for Clinical Performance of MR Dynamic Susceptibility Contrast Perfusion Imaging of the Brain
    K. Welker, J. Boxerman, A. Kalnin, T. Kaufmann, M. Shiroishi, M. Wintermark
    American Journal of Neuroradiology 2015 36 6
  • Emerging Applications of Artificial Intelligence in Neuro-Oncology
    Jeffrey D. Rudie, Andreas M. Rauschecker, R. Nick Bryan, Christos Davatzikos, Suyash Mohan
    Radiology 2019 290 3
  • Diffusion and Perfusion MRI to Differentiate Treatment-Related Changes Including Pseudoprogression from Recurrent Tumors in High-Grade Gliomas with Histopathologic Evidence
    A.J. Prager, N. Martinez, K. Beal, A. Omuro, Z. Zhang, R.J. Young
    American Journal of Neuroradiology 2015 36 5
  • Response Assessment in Neuro-Oncology Criteria for Gliomas: Practical Approach Using Conventional and Advanced Techniques
    D.J. Leao, P.G. Craig, L.F. Godoy, C.C. Leite, B. Policeni
    American Journal of Neuroradiology 2020 41 1
  • Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI
    S. Wang, M. Martinez-Lage, Y. Sakai, S. Chawla, S.G. Kim, M. Alonso-Basanta, R.A. Lustig, S. Brem, S. Mohan, R.L. Wolf, A. Desai, H. Poptani
    American Journal of Neuroradiology 2016 37 1
  • Incidence of Tumour Progression and Pseudoprogression in High-Grade Gliomas: a Systematic Review and Meta-Analysis
    Abdul W. Abbasi, Henriette E. Westerlaan, Gea A. Holtman, Kamal M. Aden, Peter Jan van Laar, Anouk van der Hoorn
    Clinical Neuroradiology 2018 28 3
  • Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma
    Bum-Sup Jang, Seung Hyuck Jeon, Il Han Kim, In Ah Kim
    Scientific Reports 2018 8 1
  • Pre- and Posttreatment Glioma: Comparison of Amide Proton Transfer Imaging with MR Spectroscopy for Biomarkers of Tumor Proliferation
    Ji Eun Park, Ho Sung Kim, Kye Jin Park, Sang Joon Kim, Jeong Hoon Kim, Seth A. Smith
    Radiology 2016 278 2

More in this TOC Section

  • Statin Therapy Does Not Affect the Radiographic and Clinical Profile of Patients with TIA and Minor Stroke
  • Optimal MRI Sequence for Identifying Occlusion Location in Acute Stroke: Which Value of Time-Resolved Contrast-Enhanced MRA?
  • SWI or T2*: Which MRI Sequence to Use in the Detection of Cerebral Microbleeds? The Karolinska Imaging Dementia Study
Show more Brain

Similar Articles

Advertisement

Indexed Content

  • Current Issue
  • Accepted Manuscripts
  • Article Preview
  • Past Issues
  • Editorials
  • Editors Choice
  • Fellow Journal Club
  • Letters to the Editor

Cases

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

Special Collections

  • Special Collections

Resources

  • News and Updates
  • Turn around Times
  • Submit a Manuscript
  • Author Policies
  • Manuscript Submission Guidelines
  • Evidence-Based Medicine Level Guide
  • Publishing Checklists
  • Graphical Abstract Preparation
  • Imaging Protocol Submission
  • Submit a Case
  • Become a Reviewer/Academy of Reviewers
  • Get Peer Review Credit from Publons

Multimedia

  • AJNR Podcast
  • AJNR SCANtastic
  • Video Articles

About Us

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

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