Literature DB >> 26475485

Accuracy of percentage of signal intensity recovery and relative cerebral blood volume derived from dynamic susceptibility-weighted, contrast-enhanced MRI in the preoperative diagnosis of cerebral tumours.

Ananya Chakravorty1, Timothy Steel2, Joga Chaganti3.   

Abstract

Conventional magnetic resonance imaging (MRI) is the technique of choice for diagnosis of cerebral tumours, and has become an increasingly powerful tool for their evaluation; however, the diagnosis of common contrast-enhancing lesions can be challenging, as it is sometimes impossible to differentiate them using conventional imaging. Histopathological analysis of biopsy specimens is the gold standard for diagnosis; however, there are significant risks associated with the invasive procedure and definitive diagnosis is not always achieved. Early accurate diagnosis is important, as management differs accordingly. Advanced MRI techniques have increasing utility for aiding diagnosis in a variety of clinical scenarios. Dynamic susceptibility-weighted contrast-enhanced (DSC) MRI is a perfusion imaging technique and a potentially important tool for the characterisation of cerebral tumours. The percentage of signal intensity recovery (PSR) and relative cerebral blood volume (rCBV) derived from DSC MRI provide information about tumour capillary permeability and neoangiogenesis, which can be used to characterise tumour type and grade, and distinguish tumour recurrence from treatment-related effects. Therefore, PSR and rCBV potentially represent a non-invasive means of diagnosis; however, the clinical utility of these parameters has yet to be established. We present a review of the literature to date.
© The Author(s) 2015.

Entities:  

Keywords:  Blood-brain barrier; brain tumour; cerebral blood volume; diagnostics; dynamic susceptibility; glioblastoma; magnetic resonance imaging; neoangiogenesis; perfusion imaging; review; signal intensity recovery; tumour capillary permeability; tumour typing

Mesh:

Year:  2015        PMID: 26475485      PMCID: PMC4757138          DOI: 10.1177/1971400915611916

Source DB:  PubMed          Journal:  Neuroradiol J        ISSN: 1971-4009


  56 in total

Review 1.  What is "quality of evidence" and why is it important to clinicians?

Authors:  Gordon H Guyatt; Andrew D Oxman; Regina Kunz; Gunn E Vist; Yngve Falck-Ytter; Holger J Schünemann
Journal:  BMJ       Date:  2008-05-03

2.  Survey of treatment recommendations for anaplastic oligodendroglioma.

Authors:  Lauren E Abrey; David N Louis; Nina Paleologos; Andrew B Lassman; Jeffrey J Raizer; Warren Mason; Jonathan Finlay; David R MacDonald; Lisa M DeAngelis; J Gregory Cairncross
Journal:  Neuro Oncol       Date:  2007-04-13       Impact factor: 12.300

3.  Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not.

Authors:  J L Boxerman; K M Schmainda; R M Weisskoff
Journal:  AJNR Am J Neuroradiol       Date:  2006-04       Impact factor: 3.825

4.  Comparison of the effectiveness of MRI perfusion and fluorine-18 FDG PET-CT for differentiating radiation injury from viable brain tumor: a preliminary retrospective analysis with pathologic correlation in all patients.

Authors:  Vaios Hatzoglou; Gary A Ulaner; Zhigang Zhang; Kathryn Beal; Andrei I Holodny; Robert J Young
Journal:  Clin Imaging       Date:  2012-10-12       Impact factor: 1.605

5.  Safety and efficacy of frameless and frame-based intracranial biopsy techniques.

Authors:  R Dammers; I K Haitsma; J W Schouten; J M Kros; C J J Avezaat; A J P E Vincent
Journal:  Acta Neurochir (Wien)       Date:  2008-01-03       Impact factor: 2.216

6.  Differentiation of glioblastoma multiforme and single brain metastasis by peak height and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging.

Authors:  S Cha; J M Lupo; M-H Chen; K R Lamborn; M W McDermott; M S Berger; S J Nelson; W P Dillon
Journal:  AJNR Am J Neuroradiol       Date:  2007 Jun-Jul       Impact factor: 3.825

7.  Low-grade gliomas: do changes in rCBV measurements at longitudinal perfusion-weighted MR imaging predict malignant transformation?

Authors:  Nasuda Danchaivijitr; Adam D Waldman; Daniel J Tozer; Christopher E Benton; Gisele Brasil Caseiras; Paul S Tofts; Jeremy H Rees; H Rolf Jäger
Journal:  Radiology       Date:  2008-04       Impact factor: 11.105

8.  Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors.

Authors:  N Rollin; J Guyotat; N Streichenberger; J Honnorat; V-A Tran Minh; F Cotton
Journal:  Neuroradiology       Date:  2006-02-10       Impact factor: 2.804

9.  Prognostic value of perfusion MR imaging of high-grade astrocytomas: long-term follow-up study.

Authors:  T Hirai; R Murakami; H Nakamura; M Kitajima; H Fukuoka; A Sasao; M Akter; Y Hayashida; R Toya; N Oya; K Awai; K Iyama; J-i Kuratsu; Y Yamashita
Journal:  AJNR Am J Neuroradiol       Date:  2008-06-12       Impact factor: 3.825

Review 10.  The 2007 WHO classification of tumours of the central nervous system.

Authors:  David N Louis; Hiroko Ohgaki; Otmar D Wiestler; Webster K Cavenee; Peter C Burger; Anne Jouvet; Bernd W Scheithauer; Paul Kleihues
Journal:  Acta Neuropathol       Date:  2007-07-06       Impact factor: 17.088

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  3 in total

1.  Alterations of the Blood-Brain Barrier and Regional Perfusion in Tumor Development: MRI Insights from a Rat C6 Glioma Model.

Authors:  Monika Huhndorf; Amir Moussavi; Nadine Kramann; Olga Will; Kirsten Hattermann; Christine Stadelmann; Olav Jansen; Susann Boretius
Journal:  PLoS One       Date:  2016-12-22       Impact factor: 3.240

2.  Three-dimensional arterial spin labeling imaging and dynamic susceptibility contrast perfusion-weighted imaging value in diagnosing glioma grade prior to surgery.

Authors:  Hong Ma; Zizheng Wang; Kai Xu; Zefeng Shao; Chun Yang; Peng Xu; Xiaohua Liu; Chunfeng Hu; Xin Lu; Yutao Rong
Journal:  Exp Ther Med       Date:  2017-04-20       Impact factor: 2.447

3.  Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning.

Authors:  Yang Yang; Lin-Feng Yan; Xin Zhang; Yu Han; Hai-Yan Nan; Yu-Chuan Hu; Bo Hu; Song-Lin Yan; Jin Zhang; Dong-Liang Cheng; Xiang-Wei Ge; Guang-Bin Cui; Di Zhao; Wen Wang
Journal:  Front Neurosci       Date:  2018-11-15       Impact factor: 4.677

  3 in total

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