Literature DB >> 25867683

Dynamic Contrast-Enhanced Perfusion MRI and Diffusion-Weighted Imaging in Grading of Gliomas.

Julio Arevalo-Perez1, Kyung K Peck2, Robert J Young1, Andrei I Holodny1, Sasan Karimi1, John K Lyo1.   

Abstract

PURPOSE: Accurate glioma grading is crucial for treatment planning and predicting prognosis. We performed a quantitative volumetric analysis to assess the diagnostic accuracy of histogram analysis of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) T1-weighted perfusion imaging in the preoperative evaluation of gliomas.
METHODS: Sixty-three consecutive patients with pathologically confirmed gliomas who underwent baseline DWI and DCE-MRI were enrolled. The patients were classified by histopathology according to tumor grade: 20 low-grade gliomas (grade II) and 43 high-grade gliomas (grades III and IV). Volumes-of-interest were calculated and transferred to DCE perfusion and apparent diffusion coefficient (ADC) maps. Histogram analysis was performed to determine mean and maximum values for Vp and Ktrans , and mean and minimum values for ADC. Comparisons between high-grade and low-grade gliomas, and between grades II, III, and IV, were performed. A Mann-Whitney U test at a significance level of corrected P ≤ .01 was used to assess differences.
RESULTS: All perfusion parameters could differentiate between high-grade and low-grade gliomas (P < .001) and between grades II and IV, grades II and III, and grades III and IV. Significant differences in minimum ADC were also found (P < .01). Mean ADC only differed significantly between high and low grades and grades II and IV (P < .01). There were no differences between grades II and III (P = .1) and grades III and IV (P = .71).
CONCLUSION: When derived from whole-tumor histogram analysis, DCE-MRI perfusion parameters performed better than ADC in noninvasively discriminating low- from high-grade gliomas.
Copyright © 2015 by the American Society of Neuroimaging.

Entities:  

Keywords:  DCE-MRI; DW-MRI; glioma grading; histogram analysis

Mesh:

Substances:

Year:  2015        PMID: 25867683      PMCID: PMC5525149          DOI: 10.1111/jon.12239

Source DB:  PubMed          Journal:  J Neuroimaging        ISSN: 1051-2284            Impact factor:   2.486


  22 in total

1.  Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging--prediction of patient clinical response.

Authors:  Meng Law; Sarah Oh; James S Babb; Edwin Wang; Matilde Inglese; David Zagzag; Edmond A Knopp; Glyn Johnson
Journal:  Radiology       Date:  2006-01-05       Impact factor: 11.105

2.  Data-driven grading of brain gliomas: a multiparametric MR imaging study.

Authors:  Massimo Caulo; Valentina Panara; Domenico Tortora; Peter A Mattei; Chiara Briganti; Emanuele Pravatà; Simone Salice; Antonio R Cotroneo; Armando Tartaro
Journal:  Radiology       Date:  2014-03-22       Impact factor: 11.105

3.  Glioma: Application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading.

Authors:  S C Jung; J A Yeom; J-H Kim; I Ryoo; S C Kim; H Shin; A L Lee; T J Yun; C-K Park; C-H Sohn; S-H Park; S H Choi
Journal:  AJNR Am J Neuroradiol       Date:  2014-01-02       Impact factor: 3.825

Review 4.  Genetics of adult glioma.

Authors:  McKinsey L Goodenberger; Robert B Jenkins
Journal:  Cancer Genet       Date:  2012-12-11

5.  Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation.

Authors:  Jeongwon Lee; Seung Hong Choi; Ji-Hoon Kim; Chul-Ho Sohn; Sooyeul Lee; Jaeseung Jeong
Journal:  NMR Biomed       Date:  2014-07-07       Impact factor: 4.044

6.  Role of perfusion-weighted imaging at 3T in the histopathological differentiation between astrocytic and oligodendroglial tumors.

Authors:  Taiichi Saito; Fumiyuki Yamasaki; Yoshinori Kajiwara; Nobukazu Abe; Yuji Akiyama; Takako Kakuda; Yukio Takeshima; Kazuhiko Sugiyama; Yoshikazu Okada; Kaoru Kurisu
Journal:  Eur J Radiol       Date:  2011-05-04       Impact factor: 3.528

Review 7.  Perfusion MRI: the five most frequently asked technical questions.

Authors:  Marco Essig; Mark S Shiroishi; Thanh Binh Nguyen; Marc Saake; James M Provenzale; David Enterline; Nicoletta Anzalone; Arnd Dörfler; Alex Rovira; Max Wintermark; Meng Law
Journal:  AJR Am J Roentgenol       Date:  2013-01       Impact factor: 3.959

8.  DCE and DSC MR perfusion imaging in the differentiation of recurrent tumour from treatment-related changes in patients with glioma.

Authors:  K E Shin; K J Ahn; H S Choi; S L Jung; B S Kim; S S Jeon; Y G Hong
Journal:  Clin Radiol       Date:  2014-03-01       Impact factor: 2.350

9.  Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas.

Authors:  M Law; R Young; J Babb; E Pollack; G Johnson
Journal:  AJNR Am J Neuroradiol       Date:  2007-04       Impact factor: 3.825

Review 10.  Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols.

Authors:  P S Tofts; G Brix; D L Buckley; J L Evelhoch; E Henderson; M V Knopp; H B Larsson; T Y Lee; N A Mayr; G J Parker; R E Port; J Taylor; R M Weisskoff
Journal:  J Magn Reson Imaging       Date:  1999-09       Impact factor: 4.813

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  27 in total

Review 1.  Glioblastoma multiforme: emerging treatments and stratification markers beyond new drugs.

Authors:  C von Neubeck; A Seidlitz; H H Kitzler; B Beuthien-Baumann; M Krause
Journal:  Br J Radiol       Date:  2015-07-10       Impact factor: 3.039

Review 2.  Clinical Imaging for Diagnostic Challenges in the Management of Gliomas: A Review.

Authors:  Alipi V Bonm; Reed Ritterbusch; Patrick Throckmorton; Jerome J Graber
Journal:  J Neuroimaging       Date:  2020-01-10       Impact factor: 2.486

Review 3.  'Low grade glioma': an update for radiologists.

Authors:  Jennifer Larsen; Steve B Wharton; Fiona McKevitt; Charles Romanowski; Caroline Bridgewater; Hesham Zaki; Nigel Hoggard
Journal:  Br J Radiol       Date:  2016-12-07       Impact factor: 3.039

4.  Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery.

Authors:  Xi-Xun Qi; Da-Fa Shi; Si-Xie Ren; Su-Ya Zhang; Long Li; Qing-Chang Li; Li-Ming Guan
Journal:  Eur Radiol       Date:  2017-11-16       Impact factor: 5.315

5.  Glioma Grading and Determination of IDH Mutation Status and ATRX loss by DCE and ASL Perfusion.

Authors:  Cornelia Brendle; Johann-Martin Hempel; Jens Schittenhelm; Marco Skardelly; Ghazaleh Tabatabai; Benjamin Bender; Ulrike Ernemann; Uwe Klose
Journal:  Clin Neuroradiol       Date:  2017-05-09       Impact factor: 3.649

6.  Diagnostic performance of apparent diffusion coefficient parameters for glioma grading.

Authors:  Qun Wang; JiaShu Zhang; Xinghua Xu; XiaoLei Chen; BaiNan Xu
Journal:  J Neurooncol       Date:  2018-03-24       Impact factor: 4.130

7.  MRI based texture analysis to classify low grade gliomas into astrocytoma and 1p/19q codeleted oligodendroglioma.

Authors:  Shun Zhang; Gloria Chia-Yi Chiang; Rajiv S Magge; Howard Alan Fine; Rohan Ramakrishna; Eileen Wang Chang; Tejas Pulisetty; Yi Wang; Wenzhen Zhu; Ilhami Kovanlikaya
Journal:  Magn Reson Imaging       Date:  2018-11-19       Impact factor: 2.546

8.  Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas.

Authors:  Shun Zhang; Gloria Chia-Yi Chiang; Rajiv S Magge; Howard Alan Fine; Rohan Ramakrishna; Eileen Wang Chang; Tejas Pulisetty; Yi Wang; Wenzhen Zhu; Ilhami Kovanlikaya
Journal:  Eur Radiol       Date:  2019-01-07       Impact factor: 5.315

9.  Contrast-enhanced dynamic and diffusion-weighted magnetic resonance imaging at 3.0 T to assess early-stage nasopharyngeal carcinoma.

Authors:  Liangping Ni; Ying Liu
Journal:  Oncol Lett       Date:  2018-02-05       Impact factor: 2.967

10.  Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma.

Authors:  X Lin; M Lee; O Buck; K M Woo; Z Zhang; V Hatzoglou; A Omuro; J Arevalo-Perez; A A Thomas; J Huse; K Peck; A I Holodny; R J Young
Journal:  AJNR Am J Neuroradiol       Date:  2016-12-08       Impact factor: 3.825

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