Literature DB >> 16801657

Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors.

M A Weber1, S Zoubaa, M Schlieter, E Jüttler, H B Huttner, K Geletneky, C Ittrich, M P Lichy, A Kroll, J Debus, F L Giesel, M Hartmann, M Essig.   

Abstract

OBJECTIVE: To assess the value of spectroscopic and perfusion MRI for glioma grading and for distinguishing glioblastomas from metastases and from CNS lymphomas.
METHODS: The authors examined 79 consecutive patients with first detection of a brain neoplasm on nonenhanced CT scans and no therapy prior to evaluation. Spectroscopic MRI; arterial spin-labeling MRI for measuring cerebral blood flow (CBF); first-pass dynamic, susceptibility-weighted, contrast-enhanced MRI for measuring cerebral blood volume; and T1-weighted dynamic contrast-enhanced MRI were performed. Receiver operating characteristic analysis was performed, and optimum thresholds for tumor classification and glioma grading were determined.
RESULTS: Perfusion MRI had a higher diagnostic performance than spectroscopic MRI. Because of a significantly higher tumor blood flow in glioblastomas compared with CNS lymphomas, a threshold value of 1.2 for CBF provided sensitivity of 97%, specificity of 80%, positive predictive value (PPV) of 94%, and negative predictive value (NPV) of 89%. Because CBF was significantly higher in peritumoral nonenhancing T2-hyperintense regions of glioblastomas compared with metastases, a threshold value of 0.5 for CBF provided sensitivity, specificity, PPV, and NPV of 100%, 71%, 94%, and 100%. Glioblastomas had the highest tumor blood flow values among all other glioma grades. For discrimination of glioblastomas from grade 3 gliomas, sensitivity was 97%, specificity was 50%, PPV was 84%, and NPV was 86% (CBF threshold value of 1.4), and for discrimination of glioblastomas from grade 2 gliomas, sensitivity was 94%, specificity was 78%, PPV was 94%, and NPV was 78% (CBF threshold value of 1.6).
CONCLUSION: Perfusion MRI is predictive in distinguishing glioblastomas from metastases, CNS lymphomas and other gliomas vs MRI and magnetic resonance spectroscopy.

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Year:  2006        PMID: 16801657     DOI: 10.1212/01.wnl.0000219767.49705.9c

Source DB:  PubMed          Journal:  Neurology        ISSN: 0028-3878            Impact factor:   9.910


  83 in total

1.  Percentage signal recovery derived from MR dynamic susceptibility contrast imaging is useful to differentiate common enhancing malignant lesions of the brain.

Authors:  R Mangla; B Kolar; T Zhu; J Zhong; J Almast; S Ekholm
Journal:  AJNR Am J Neuroradiol       Date:  2011-04-21       Impact factor: 3.825

2.  Arterial spin-labeling magnetic resonance imaging: the timing of regional maximal perfusion-related signal intensity revealed by a multiphase technique.

Authors:  Tomoyuki Noguchi; Takashi Yoshiura; Akio Hiwatashi; Osamu Togao; Koji Yamashita; Eiki Nagao; Hiroshi Honda
Journal:  Jpn J Radiol       Date:  2011-12-16       Impact factor: 2.374

3.  Differentiation of primary central nervous system lymphomas from high-grade gliomas by rCBV and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging.

Authors:  Z Xing; R X You; J Li; Y Liu; D R Cao
Journal:  Clin Neuroradiol       Date:  2013-08-31       Impact factor: 3.649

Review 4.  Intra-operative 3-T MRI for paediatric brain tumours: challenges and perspectives.

Authors:  L J Abernethy; S Avula; G M Hughes; E J Wright; C L Mallucci
Journal:  Pediatr Radiol       Date:  2012-02

5.  MR Imaging-Based Analysis of Glioblastoma Multiforme: Estimation of IDH1 Mutation Status.

Authors:  K Yamashita; A Hiwatashi; O Togao; K Kikuchi; R Hatae; K Yoshimoto; M Mizoguchi; S O Suzuki; T Yoshiura; H Honda
Journal:  AJNR Am J Neuroradiol       Date:  2015-09-24       Impact factor: 3.825

6.  Measurement of cerebral perfusion with arterial spin labeling: Part 2. Applications.

Authors:  Gregory G Brown; Camellia Clark; Thomas T Liu
Journal:  J Int Neuropsychol Soc       Date:  2007-05       Impact factor: 2.892

Review 7.  Clinical neuroimaging using arterial spin-labeled perfusion magnetic resonance imaging.

Authors:  Ronald L Wolf; John A Detre
Journal:  Neurotherapeutics       Date:  2007-07       Impact factor: 7.620

8.  Performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images.

Authors:  K Yamashita; T Yoshiura; H Arimura; F Mihara; T Noguchi; A Hiwatashi; O Togao; Y Yamashita; T Shono; S Kumazawa; Y Higashida; H Honda
Journal:  AJNR Am J Neuroradiol       Date:  2008-04-03       Impact factor: 3.825

Review 9.  Brain metastases: neuroimaging.

Authors:  Whitney B Pope
Journal:  Handb Clin Neurol       Date:  2018

Review 10.  MR Imaging Biomarkers in Oncology Clinical Trials.

Authors:  Richard G Abramson; Lori R Arlinghaus; Adrienne N Dula; C Chad Quarles; Ashley M Stokes; Jared A Weis; Jennifer G Whisenant; Eduard Y Chekmenev; Igor Zhukov; Jason M Williams; Thomas E Yankeelov
Journal:  Magn Reson Imaging Clin N Am       Date:  2016-02       Impact factor: 2.266

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