Literature DB >> 31371360

Utility of Percentage Signal Recovery and Baseline Signal in DSC-MRI Optimized for Relative CBV Measurement for Differentiating Glioblastoma, Lymphoma, Metastasis, and Meningioma.

M D Lee1, G L Baird2, L C Bell3, C C Quarles3, J L Boxerman4,2.   

Abstract

BACKGROUND AND
PURPOSE: The percentage signal recovery in non-leakage-corrected (no preload, high flip angle, intermediate TE) DSC-MR imaging is known to differ significantly for glioblastoma, metastasis, and primary CNS lymphoma. Because the percentage signal recovery is influenced by preload and pulse sequence parameters, we investigated whether the percentage signal recovery can still differentiate these common contrast-enhancing neoplasms using a DSC-MR imaging protocol designed for relative CBV accuracy (preload, intermediate flip angle, low TE).
MATERIALS AND METHODS: We retrospectively analyzed DSC-MR imaging of treatment-naïve, pathology-proved glioblastomas (n = 14), primary central nervous system lymphomas (n = 7), metastases (n = 20), and meningiomas (n = 13) using a protocol designed for relative CBV accuracy (a one-quarter-dose preload and single-dose bolus of gadobutrol, TR/TE = 1290/40 ms, flip angle = 60° at 1.5T). Mean percentage signal recovery, relative CBV, and normalized baseline signal intensity were compared within contrast-enhancing lesion volumes. Classification accuracy was determined by receiver operating characteristic analysis.
RESULTS: Relative CBV best differentiated meningioma from glioblastoma and from metastasis with areas under the curve of 0.84 and 0.82, respectively. The percentage signal recovery best differentiated primary central nervous system lymphoma from metastasis with an area under the curve of 0.81. Relative CBV and percentage signal recovery were similar in differentiating primary central nervous system lymphoma from glioblastoma and from meningioma. Although neither relative CBV nor percentage signal recovery differentiated glioblastoma from metastasis, mean normalized baseline signal intensity achieved 86% sensitivity and 50% specificity.
CONCLUSIONS: Similar to results for non-preload-based DSC-MR imaging, percentage signal recovery for one-quarter-dose preload-based, intermediate flip angle DSC-MR imaging differentiates most pair-wise comparisons of glioblastoma, metastasis, primary central nervous system lymphoma, and meningioma, except for glioblastoma versus metastasis. Differences in normalized post-preload baseline signal for glioblastoma and metastasis, reflecting a snapshot of dynamic contrast enhancement, may motivate the use of single-dose multiecho protocols permitting simultaneous quantification of DSC-MR imaging and dynamic contrast-enhanced MR imaging parameters.
© 2019 by American Journal of Neuroradiology.

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Year:  2019        PMID: 31371360      PMCID: PMC6728173          DOI: 10.3174/ajnr.A6153

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  34 in total

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2.  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

3.  Dynamic perfusion MRI characteristics of dural metastases and meningiomas: a pilot study characterizing the first-pass wash-in phase beyond relative cerebral blood volume.

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5.  Assessment of a simplified spin and gradient echo (sSAGE) approach for human brain tumor perfusion imaging.

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Review 6.  Imaging tutorial: differential diagnosis of bright lesions on diffusion-weighted MR images.

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7.  Contribution of dynamic contrast MR imaging to the differentiation between dural metastasis and meningioma.

Authors:  S Kremer; S Grand; C Rémy; B Pasquier; A L Benabid; S Bracard; J F Le Bas
Journal:  Neuroradiology       Date:  2004-06-30       Impact factor: 2.804

8.  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

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Authors:  S Kremer; S Grand; F Berger; D Hoffmann; B Pasquier; C Rémy; A L Benabid; J F Le Bas
Journal:  Neuroradiology       Date:  2002-12-07       Impact factor: 2.804

10.  Assessing tumor cytoarchitecture using multiecho DSC-MRI derived measures of the transverse relaxivity at tracer equilibrium (TRATE).

Authors:  Natenael B Semmineh; Junzhong Xu; Jack T Skinner; Jingping Xie; Hua Li; Gregory Ayers; C Chad Quarles
Journal:  Magn Reson Med       Date:  2014-09-16       Impact factor: 4.668

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1.  Presurgical Identification of Primary Central Nervous System Lymphoma with Normalized Time-Intensity Curve: A Pilot Study of a New Method to Analyze DSC-PWI.

Authors:  A Pons-Escoda; A Garcia-Ruiz; P Naval-Baudin; M Cos; N Vidal; G Plans; J Bruna; R Perez-Lopez; C Majos
Journal:  AJNR Am J Neuroradiol       Date:  2020-09-17       Impact factor: 3.825

2.  Role of intra-tumoral vasculature imaging features on susceptibility weighted imaging in differentiating primary central nervous system lymphoma from glioblastoma: a multiparametric comparison with pathological validation.

Authors:  Rupsa Bhattacharjee; Mamta Gupta; Tanu Singh; Shalini Sharma; Gaurav Khanna; Suhail P Parvaze; Rana Patir; Sandeep Vaishya; Sunita Ahlawat; Anup Singh; Rakesh Kumar Gupta
Journal:  Neuroradiology       Date:  2022-04-18       Impact factor: 2.995

3.  Time optimization of gadobutrol-enhanced brain MRI for metastases and primary tumors using a dynamic contrast-enhanced imaging.

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Journal:  BMC Med Imaging       Date:  2022-10-17       Impact factor: 2.795

4.  Accuracy of apparent diffusion coefficient in differentiation of glioblastoma from metastasis.

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5.  Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation.

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6.  Single brain metastasis versus glioblastoma multiforme: a VOI-based multiparametric analysis for differential diagnosis.

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Review 7.  Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading.

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