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Mutlimodality MR imaging for differentiation between brain tumor lesions

F., Durmo LU orcid ; A., Rydelius LU ; S., Engelholm ; S., Kinhult LU ; K., Askaner LU ; J., Lätt LU and Maly Sundgren, Pia LU orcid (2016) 39th European Society of Neuroradiology Diagnostic and Interventional Annual Meeting 20016 In Neuroradiology 58(Suppl 1). p.53-54
Abstract
Purpose: Applying diffusion and perfusion metrics for evaluation of low-(LGG), high grade glioma (HGG) and metastases (MET) for differential diagnosis. Materials and Method: 43 patients (18HGG, 10 LGG, and 15MET) were included. MR data for tumour volume, perilesional edema, rCBF-, rCBV-, FLAIR-, FA-, ADC-maps were quantified by regions of interest (ROI). Measures of different parameters, and ratios, using contralateral white matter as denominator, were performed. A binary logistic regression model was constructed for multi-parametric analysis and ROCanalysis. Results: Significant difference was found for nADCt, rCBF, rCBV between LGG and HGG, nADCe between HGG and MET, and Ev, Ev-Tv ratio, nADCt, nADCe, rCBF, rCBV between LGG and MET.... (More)
Purpose: Applying diffusion and perfusion metrics for evaluation of low-(LGG), high grade glioma (HGG) and metastases (MET) for differential diagnosis. Materials and Method: 43 patients (18HGG, 10 LGG, and 15MET) were included. MR data for tumour volume, perilesional edema, rCBF-, rCBV-, FLAIR-, FA-, ADC-maps were quantified by regions of interest (ROI). Measures of different parameters, and ratios, using contralateral white matter as denominator, were performed. A binary logistic regression model was constructed for multi-parametric analysis and ROCanalysis. Results: Significant difference was found for nADCt, rCBF, rCBV between LGG and HGG, nADCe between HGG and MET, and Ev, Ev-Tv ratio, nADCt, nADCe, rCBF, rCBV between LGG and MET. ROCanalysis for HGG compared to LGG showed 80 % sensitivity and 81.2 % specificity for nADCt, 100 % sensitivity and 100 % specificity for rCBF and 80 % sensitivity and 90 % specificity for rCBV. ROC-curves betweenMETand LGG showed sensitivity and specificity for Ev 73.3 % and 90 %, Ev-Tv ratio 80 % and 100 %, nADCt 90 % and 86.7 %, nADCe 80 % and 90 %, rCBF 93.3 % and 100 %, and rCBV 60 % and 100 %. Combining Ev, Ev-Tv ratio, nADCt, nADCe and rCBV between METand LGG gave 93.3%sensitivity and 100%specificity. Combining nADCt and rCBV between HGG and LGG 86.7 % sensitivity and 100 % specificity. Conclusion: Multi-parametric imaging protocols is an advantage for preoperative distinction of LGG, HGG and MET. (Less)
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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
brain damage, cancer susceptibility, clinical article, diagnostic test accuracy study, diencephalon, differentiation, edema, female, glioma, human, logistic regression analysis, male, metastasis, nuclear magnetic resonance imaging, quantitative study, receiver operating characteristic, sensitivity and specificity, statistical model, tumor volume, white matter
in
Neuroradiology
volume
58
issue
Suppl 1
pages
53 - 54
publisher
Springer
conference name
39th European Society of Neuroradiology Diagnostic and Interventional Annual Meeting 20016
conference location
Belgrade, Serbia
conference dates
2016-09-15 - 2016-09-18
ISSN
0028-3940
DOI
10.1007/s00234-016-1734-6
language
English
LU publication?
yes
additional info
M1 - (Durmo F.) Institution of Clinical Sciences, Lund University, Kristianstad, Sweden M1 - (Rydelius A.) Department of Neurology, Skane University Hospital, Lund University, Lund, Sweden M1 - (Engelholm S.; Kinhult S.) Department of Oncology, Skane University Hospital, Lund University, Lund, Sweden M1 - (Askaner K.) Department of Medical Imaging and Physiology, Skane University Hospital, Lund University, Malmö, Sweden M1 - (Lätt J.) Department of Medical Radiation Physics, Skane University Hospital, Lund University, Lund, Sweden M1 - (Sundgren P.C.) Department of Radiology, Skane University Hospital, Lund University, Lund, Sweden
id
973577b2-96fe-48eb-861d-5ad9e5a5e18b
date added to LUP
2019-02-14 10:26:47
date last changed
2020-11-12 02:31:48
@misc{973577b2-96fe-48eb-861d-5ad9e5a5e18b,
  abstract     = {{Purpose: Applying diffusion and perfusion metrics for evaluation of low-(LGG), high grade glioma (HGG) and metastases (MET) for differential diagnosis. Materials and Method: 43 patients (18HGG, 10 LGG, and 15MET) were included. MR data for tumour volume, perilesional edema, rCBF-, rCBV-, FLAIR-, FA-, ADC-maps were quantified by regions of interest (ROI). Measures of different parameters, and ratios, using contralateral white matter as denominator, were performed. A binary logistic regression model was constructed for multi-parametric analysis and ROCanalysis. Results: Significant difference was found for nADCt, rCBF, rCBV between LGG and HGG, nADCe between HGG and MET, and Ev, Ev-Tv ratio, nADCt, nADCe, rCBF, rCBV between LGG and MET. ROCanalysis for HGG compared to LGG showed 80 % sensitivity and 81.2 % specificity for nADCt, 100 % sensitivity and 100 % specificity for rCBF and 80 % sensitivity and 90 % specificity for rCBV. ROC-curves betweenMETand LGG showed sensitivity and specificity for Ev 73.3 % and 90 %, Ev-Tv ratio 80 % and 100 %, nADCt 90 % and 86.7 %, nADCe 80 % and 90 %, rCBF 93.3 % and 100 %, and rCBV 60 % and 100 %. Combining Ev, Ev-Tv ratio, nADCt, nADCe and rCBV between METand LGG gave 93.3%sensitivity and 100%specificity. Combining nADCt and rCBV between HGG and LGG 86.7 % sensitivity and 100 % specificity. Conclusion: Multi-parametric imaging protocols is an advantage for preoperative distinction of LGG, HGG and MET.}},
  author       = {{F., Durmo and A., Rydelius and S., Engelholm and S., Kinhult and K., Askaner and J., Lätt and Maly Sundgren, Pia}},
  issn         = {{0028-3940}},
  keywords     = {{brain damage; cancer susceptibility; clinical article; diagnostic test accuracy study; diencephalon; differentiation; edema; female; glioma; human; logistic regression analysis; male; metastasis; nuclear magnetic resonance imaging; quantitative study; receiver operating characteristic; sensitivity and specificity; statistical model; tumor volume; white matter}},
  language     = {{eng}},
  note         = {{Conference Abstract}},
  number       = {{Suppl 1}},
  pages        = {{53--54}},
  publisher    = {{Springer}},
  series       = {{Neuroradiology}},
  title        = {{Mutlimodality MR imaging for differentiation between brain tumor lesions}},
  url          = {{http://dx.doi.org/10.1007/s00234-016-1734-6}},
  doi          = {{10.1007/s00234-016-1734-6}},
  volume       = {{58}},
  year         = {{2016}},
}