Literature DB >> 30015801

Differentiation of Glioblastoma and Solitary Brain Metastasis by Gradient of Relative Cerebral Blood Volume in the Peritumoral Brain Zone Derived from Dynamic Susceptibility Contrast Perfusion Magnetic Resonance Imaging.

Dejun She1, Zhen Xing, Dairong Cao.   

Abstract

OBJECTIVE: The purpose of our study was to evaluate the efficacy of the relative cerebral blood volume (rCBV) gradient in the peritumoral brain zone (PBZ)-the difference in the rCBV values from the area closest to the enhancing lesion to the area closest to the healthy white matter-in differentiating glioblastoma (GB) from solitary brain metastasis (MET).
METHODS: A 3.0-T magnetic resonance imaging (MRI) machine was used to perform dynamic susceptibility contrast perfusion MRI (DSC-MRI) on 43 patients with a solitary brain tumor (24 GB, 19 MET). The rCBV ratios were acquired by DSC-MRI data in 3 regions of the PBZ (near the enhancing tumor, G1; intermediate distance from the enhancing tumor, G2; far from the enhancing tumor, G3). The maximum rCBV ratios in the PBZ (rCBVp) and the enhancing tumor were also calculated, respectively. The perfusion parameters were evaluated using the nonparametric Mann-Whitney test. The sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve were identified.
RESULTS: The rCBVp ratios and rCBV gradient in the PBZ were significantly higher in GB compared with MET (P < 0.05 for both rCBVp ratios and rCBV gradient). The threshold values of 0.50 or greater for rCBVp ratios provide sensitivity and specificity of 57.69% and 79.17%, respectively, for differentiation of GB from MET. Compared with rCBVp ratios, rCBV gradient had higher sensitivity (94.44%) and specificity (91.67%) using the threshold value of greater than 0.06.
CONCLUSIONS: The parameter of rCBV gradient derived from DSC-MRI in the PBZ seems to be the most efficient parameter to differentiate GB from METs.

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Year:  2019        PMID: 30015801     DOI: 10.1097/RCT.0000000000000771

Source DB:  PubMed          Journal:  J Comput Assist Tomogr        ISSN: 0363-8715            Impact factor:   1.826


  5 in total

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Authors:  Rafael Roesler; Simone Afonso Dini; Gustavo R Isolan
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2.  Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning.

Authors:  Yukun Liu; Tianshi Li; Ziwen Fan; Yiming Li; Zhiyan Sun; Shaowu Li; Yuchao Liang; Chunyao Zhou; Qiang Zhu; Hong Zhang; Xing Liu; Lei Wang; Yinyan Wang
Journal:  Front Neurosci       Date:  2022-05-12       Impact factor: 5.152

3.  Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis.

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Review 4.  Current landscape and future perspectives in preclinical MR and PET imaging of brain metastasis.

Authors:  Synnøve Nymark Aasen; Heidi Espedal; Olivier Keunen; Tom Christian Holm Adamsen; Rolf Bjerkvig; Frits Thorsen
Journal:  Neurooncol Adv       Date:  2021-10-14

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

Authors:  Andrea Romano; Giulia Moltoni; Alessia Guarnera; Luca Pasquini; Alberto Di Napoli; Antonio Napolitano; Maria Camilla Rossi Espagnet; Alessandro Bozzao
Journal:  Radiol Med       Date:  2022-03-22       Impact factor: 6.313

  5 in total

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