Literature DB >> 30026384

Clinical Value of Vascular Permeability Estimates Using Dynamic Susceptibility Contrast MRI: Improved Diagnostic Performance in Distinguishing Hypervascular Primary CNS Lymphoma from Glioblastoma.

B Lee1, J E Park2, A Bjørnerud3, J H Kim4, J Y Lee5, H S Kim6.   

Abstract

BACKGROUND AND
PURPOSE: A small subset of primary central nervous system lymphomas exhibits high cerebral blood volume, which is indistinguishable from that in glioblastoma on dynamic susceptibility contrast MR imaging. Our study aimed to test whether estimates of combined perfusion and vascular permeability metrics derived from DSC-MR imaging can improve the diagnostic performance in differentiating hypervascular primary central nervous system lymphoma from glioblastoma.
MATERIALS AND METHODS: A total of 119 patients (with 30 primary central nervous system lymphomas and 89 glioblastomas) exhibited hypervascular foci using the reference method of leakage-corrected CBV (reference-normalized CBV). An alternative postprocessing method used the tissue residue function to calculate vascular permeability (extraction fraction), leakage-corrected CBV, cerebral blood flow, and mean transit time. Parameters were compared using Mann-Whitney U tests, and the diagnostic performance to distinguish primary central nervous system lymphoma from glioblastoma was calculated using the area under the curve from the receiver operating characteristic curve and was cross-validated with bootstrapping.
RESULTS: Hypervascular primary central nervous system lymphoma showed similar leakage-corrected normalized CBV and leakage-corrected CBV compared with glioblastoma (P > .05); however, primary central nervous system lymphoma exhibited a significantly higher extraction fraction (P < .001) and CBF (P = .01) and shorter MTT (P < .001) than glioblastoma. The extraction fraction showed the highest diagnostic performance (the area under the receiver operating characteristic curve [AUC], 0.78; 95% confidence interval, 0.69-0.85) for distinguishing hypervascular primary central nervous system lymphoma from glioblastoma, with a significantly higher performance than both CBV (AUC, 0.53-0.59, largest P = .02) and CBF (AUC, 0.72) and MTT (AUC, 0.71).
CONCLUSIONS: Estimation of vascular permeability with DSC-MR imaging further characterizes hypervascular primary central nervous system lymphoma and improves diagnostic performance in glioblastoma differentiation.
© 2018 by American Journal of Neuroradiology.

Entities:  

Mesh:

Year:  2018        PMID: 30026384     DOI: 10.3174/ajnr.A5732

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


  4 in total

Review 1.  Glioblastoma in adults: a Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions.

Authors:  Patrick Y Wen; Michael Weller; Eudocia Quant Lee; Brian M Alexander; Jill S Barnholtz-Sloan; Floris P Barthel; Tracy T Batchelor; Ranjit S Bindra; Susan M Chang; E Antonio Chiocca; Timothy F Cloughesy; John F DeGroot; Evanthia Galanis; Mark R Gilbert; Monika E Hegi; Craig Horbinski; Raymond Y Huang; Andrew B Lassman; Emilie Le Rhun; Michael Lim; Minesh P Mehta; Ingo K Mellinghoff; Giuseppe Minniti; David Nathanson; Michael Platten; Matthias Preusser; Patrick Roth; Marc Sanson; David Schiff; Susan C Short; Martin J B Taphoorn; Joerg-Christian Tonn; Jonathan Tsang; Roel G W Verhaak; Andreas von Deimling; Wolfgang Wick; Gelareh Zadeh; David A Reardon; Kenneth D Aldape; Martin J van den Bent
Journal:  Neuro Oncol       Date:  2020-08-17       Impact factor: 12.300

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

Authors:  Ji Eun Park; Ho Sung Kim; Junkyu Lee; E-Nae Cheong; Ilah Shin; Sung Soo Ahn; Woo Hyun Shim
Journal:  Sci Rep       Date:  2020-12-08       Impact factor: 4.379

3.  Differentiation Between Primary Central Nervous System Lymphoma and Atypical Glioblastoma Based on MRI Morphological Feature and Signal Intensity Ratio: A Retrospective Multicenter Study.

Authors:  Yu Han; Zi-Jun Wang; Wen-Hua Li; Yang Yang; Jian Zhang; Xi-Biao Yang; Lin Zuo; Gang Xiao; Sheng-Zhong Wang; Lin-Feng Yan; Guang-Bin Cui
Journal:  Front Oncol       Date:  2022-01-31       Impact factor: 6.244

4.  Cerebral and tumoral blood flow in adult gliomas: a systematic review of results from magnetic resonance imaging.

Authors:  Mueez Waqar; Daniel Lewis; Erjon Agushi; Matthew Gittins; Alan Jackson; David Coope
Journal:  Br J Radiol       Date:  2021-06-09       Impact factor: 3.629

  4 in total

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