Literature DB >> 29805537

Contribution of susceptibility- and diffusion-weighted magnetic resonance imaging for grading gliomas.

Jianxing Xu1, Hai Xu2, Wei Zhang2, Jiangang Zheng1.   

Abstract

The aim of the present study was to assess the value of susceptibility-weighted imaging (SWI) and diffusion-weighted imaging (DWI) in the grading of gliomas and to evaluate the correlation between these quantitative parameters derived from SWI and DWI. A total of 49 patients with glioma were assessed by DWI and SWI. The evaluation included the ratio of apparent diffuse coefficient values between the solid portion of tumors and contralateral normal white matter (rADC) and the degree of intratumoral susceptibility signal intensity (ITSS) within tumors. Receiver operating characteristic curve (ROC) analyses were performed and the area under the ROC curve was calculated to compare the diagnostic performance, determine optimum thresholds for tumor grading, and calculate the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for identifying high-grade gliomas. The correlation between DWI- and SWI-derived parameters was also evaluated. The rADC and the degrees of ITSS within tumors were significantly higher in high-grade gliomas than those in low-grade gliomas. ROC curve analysis indicated that the rADC was a better index for grading gliomas than the ITSS degree. Statistical analysis demonstrated a threshold value of 1.497 for rADC to provide a sensitivity, specificity, PPV and NPV of 86.2, 85.0, 89.3 and 81.0%, respectively, for determining high-grade gliomas. A degree of ITSS of 1.5 was defined as the threshold to identify high-grade gliomas and sensitivity, specificity, PPV and NPV of 82.8, 75.0, 82.8 and 75.0% were obtained, respectively. Furthermore, a moderate inverse correlation between rADC and the ITSS degree was revealed. Combination of SWI with DWI may provide valuable information for glioma grading.

Entities:  

Keywords:  diffusion weighted imaging; glioma grading; magnetic resonance imaging; susceptibility weighted imaging

Year:  2018        PMID: 29805537      PMCID: PMC5952072          DOI: 10.3892/etm.2018.6017

Source DB:  PubMed          Journal:  Exp Ther Med        ISSN: 1792-0981            Impact factor:   2.447


  28 in total

1.  Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas.

Authors:  T Sugahara; Y Korogi; M Kochi; I Ikushima; Y Shigematu; T Hirai; T Okuda; L Liang; Y Ge; Y Komohara; Y Ushio; M Takahashi
Journal:  J Magn Reson Imaging       Date:  1999-01       Impact factor: 4.813

Review 2.  Clinical applications of neuroimaging with susceptibility-weighted imaging.

Authors:  Vivek Sehgal; Zachary Delproposto; E Mark Haacke; Karen A Tong; Nathaniel Wycliffe; Daniel K Kido; Yingbiao Xu; Jaladhar Neelavalli; Djamel Haddar; Jürgen R Reichenbach
Journal:  J Magn Reson Imaging       Date:  2005-10       Impact factor: 4.813

3.  Susceptibility weighted imaging: data acquisition, image reconstruction and clinical applications.

Authors:  Alexander Rauscher; Jan Sedlacik; Andreas Deistung; Hans-Joachim Mentzel; Jürgen R Reichenbach
Journal:  Z Med Phys       Date:  2006       Impact factor: 4.820

4.  High-resolution contrast-enhanced, susceptibility-weighted MR imaging at 3T in patients with brain tumors: correlation with positron-emission tomography and histopathologic findings.

Authors:  K Pinker; I M Noebauer-Huhmann; I Stavrou; R Hoeftberger; P Szomolanyi; G Karanikas; M Weber; A Stadlbauer; E Knosp; K Friedrich; S Trattnig
Journal:  AJNR Am J Neuroradiol       Date:  2007-08       Impact factor: 3.825

5.  Direct measurement of the signal intensity of diffusion-weighted magnetic resonance imaging for preoperative grading and treatment guidance for brain gliomas.

Authors:  Chih-Chun Wu; Wan-Yuo Guo; Min-Hsiung Chen; Donald M T Ho; Alex S C Hung; Hsiao-Wen Chung
Journal:  J Chin Med Assoc       Date:  2012-11-02       Impact factor: 2.743

6.  Grading of cerebral glioma with multiparametric MR imaging and 18F-FDG-PET: concordance and accuracy.

Authors:  Jeong Hee Yoon; Ji-hoon Kim; Won Jun Kang; Chul-Ho Sohn; Seung Hong Choi; Tae Jin Yun; Yong Eun; Yong Sub Song; Kee-Hyun Chang
Journal:  Eur Radiol       Date:  2014-02       Impact factor: 5.315

Review 7.  The WHO classification of tumors of the nervous system.

Authors:  Paul Kleihues; David N Louis; Bernd W Scheithauer; Lucy B Rorke; Guido Reifenberger; Peter C Burger; Webster K Cavenee
Journal:  J Neuropathol Exp Neurol       Date:  2002-03       Impact factor: 3.685

8.  Three-dimensional susceptibility-weighted imaging at 7 T using fractal-based quantitative analysis to grade gliomas.

Authors:  Antonio Di Ieva; Sabine Göd; Günther Grabner; Fabio Grizzi; Camillo Sherif; Christian Matula; Manfred Tschabitscher; Siegfrid Trattnig
Journal:  Neuroradiology       Date:  2012-08-18       Impact factor: 2.804

9.  Apparent diffusion coefficient and cerebral blood volume in brain gliomas: relation to tumor cell density and tumor microvessel density based on stereotactic biopsies.

Authors:  N Sadeghi; N D'Haene; C Decaestecker; M Levivier; T Metens; C Maris; D Wikler; D Baleriaux; I Salmon; S Goldman
Journal:  AJNR Am J Neuroradiol       Date:  2007-12-13       Impact factor: 3.825

10.  MR classification of brain gliomas: value of magnetization transfer and conventional imaging.

Authors:  T Kurki; N Lundbom; H Kalimo; S Valtonen
Journal:  Magn Reson Imaging       Date:  1995       Impact factor: 2.546

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  1 in total

1.  Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: Systematic review and meta-analysis.

Authors:  Qiang-Ping Wang; De-Qiang Lei; Ye Yuan; Nan-Xiang Xiong
Journal:  Medicine (Baltimore)       Date:  2020-02       Impact factor: 1.817

  1 in total

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