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Brain Tumor Characterization Using Multibiometric Evaluation of MRI

Durmo, Faris LU orcid ; Lätt, Jimmy LU ; Rydelius, Anna LU ; Engelholm, Silke ; Kinhult, Sara LU ; Askaner, Krister LU ; Englund, Elisabet LU orcid ; Bengzon, Johan LU ; Nilsson, Markus LU and Björkman-Burtscher, Isabella M LU , et al. (2018) In Tomography : a journal for imaging research 4(1). p.14-25
Abstract

The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for... (More)

The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) between LGG and HGG, and when these metrics were combined, HGG could be distinguished from LGG with a sensitivity and specificity of 100%. The only metric to distinguish HGG from MET was the normalized-ADC-E with a sensitivity of 68.8% and a specificity of 80%. LGG can be distinguished from MET by combining edema volume (Vol-E), Vol-E/tumor volume (Vol-T), nADC-T, nCBF-T, nCBV-T, and nADC-E with a sensitivity of 93.3% and a specificity of 100%. The present study confirms the usability of a multibiometric approach including volume, perfusion, and diffusion metrics in differentially diagnosing brain tumors in preoperative patients and adds to the growing body of evidence in the clinical field in need of validation and standardization.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Tomography : a journal for imaging research
volume
4
issue
1
pages
12 pages
publisher
Grapho Publications LLC
external identifiers
  • scopus:85077207303
  • pmid:29675474
ISSN
2379-1381
DOI
10.18383/j.tom.2017.00020
project
Optimisation and Validation of Dynamic Susceptibility Contrast MRI
language
English
LU publication?
yes
id
0b04a04b-c71d-4b57-b26d-08c7214369f1
date added to LUP
2018-09-07 11:24:08
date last changed
2024-04-01 10:01:01
@article{0b04a04b-c71d-4b57-b26d-08c7214369f1,
  abstract     = {{<p>The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) between LGG and HGG, and when these metrics were combined, HGG could be distinguished from LGG with a sensitivity and specificity of 100%. The only metric to distinguish HGG from MET was the normalized-ADC-E with a sensitivity of 68.8% and a specificity of 80%. LGG can be distinguished from MET by combining edema volume (Vol-E), Vol-E/tumor volume (Vol-T), nADC-T, nCBF-T, nCBV-T, and nADC-E with a sensitivity of 93.3% and a specificity of 100%. The present study confirms the usability of a multibiometric approach including volume, perfusion, and diffusion metrics in differentially diagnosing brain tumors in preoperative patients and adds to the growing body of evidence in the clinical field in need of validation and standardization.</p>}},
  author       = {{Durmo, Faris and Lätt, Jimmy and Rydelius, Anna and Engelholm, Silke and Kinhult, Sara and Askaner, Krister and Englund, Elisabet and Bengzon, Johan and Nilsson, Markus and Björkman-Burtscher, Isabella M and Chenevert, Thomas and Knutsson, Linda and Sundgren, Pia C}},
  issn         = {{2379-1381}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{14--25}},
  publisher    = {{Grapho Publications LLC}},
  series       = {{Tomography : a journal for imaging research}},
  title        = {{Brain Tumor Characterization Using Multibiometric Evaluation of MRI}},
  url          = {{http://dx.doi.org/10.18383/j.tom.2017.00020}},
  doi          = {{10.18383/j.tom.2017.00020}},
  volume       = {{4}},
  year         = {{2018}},
}