Investigative Magnetic Resonance Imaging

  • 제21권1호
  • /
  • Pages.9-19
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  • 2017
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  • 2384-1095(pISSN)
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  • 2384-1109(eISSN)
대한자기공명의과학회 (Korean Society of Magnetic Resonance in Medicine)
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Differentiation between Glioblastoma and Primary Central Nervous System Lymphoma Using Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging: Comparison Study of the Manual versus Semiautomatic Segmentation Method

  • Kim, Ye Eun (College of Medicine, Seoul National University) ;
  • Choi, Seung Hong (Department of Radiology, Seoul National University College of Medicine) ;
  • Lee, Soon Tae (Department of Neurology, Seoul National University College of Medicine) ;
  • Kim, Tae Min (Department of Internal Medicine, Cancer Research Institute, Seoul National University College of Medicine) ;
  • Park, Chul-Kee (Department of Neurosurgery, Biomedical Research Institute, Seoul National University College of Medicine) ;
  • Park, Sung-Hye (Department of Pathology, Seoul National University College of Medicine) ;
  • Kim, Il Han (Department of Radiation Oncology, Cancer Research Institute, Seoul National University College of Medicine)
  • 투고 : 2016.11.24
  • 심사 : 2017.01.13
  • 발행 : 2017.03.30
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초록

Background: Normalized cerebral blood volume (nCBV) can be measured using manual or semiautomatic segmentation method. However, the difference in diagnostic performance on brain tumor differentiation between differently measured nCBV has not been evaluated. Purpose: To compare the diagnostic performance of manually obtained nCBV to that of semiautomatically obtained nCBV on glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) differentiation. Materials and Methods: Histopathologically confirmed forty GBM and eleven PCNSL patients underwent 3T MR imaging with dynamic susceptibility contrast-enhanced perfusion MR imaging before any treatment or biopsy. Based on the contrast-enhanced T1-weighted imaging, the mean nCBV (mCBV) was measured using the manual method (manual mCBV), random regions of interest (ROIs) placement by the observer, or the semiautomatic segmentation method (semiautomatic mCBV). The volume of enhancing portion of the tumor was also measured during semiautomatic segmentation process. T-test, ROC curve analysis, Fisher's exact test and multivariate regression analysis were performed to compare the value and evaluate the diagnostic performance of each parameter. Results: GBM showed a higher enhancing volume (P = 0.0307), a higher manual mCBV (P = 0.018) and a higher semiautomatic mCBV (P = 0.0111) than that of the PCNSL. Semiautomatic mCBV had the highest value (0.815) for the area under the curve (AUC), however, the AUCs of the three parameters were not significantly different from each other. The semiautomatic mCBV was the best independent predictor for the GBM and PCNSL differential diagnosis according to the stepwise multiple regression analysis. Conclusion: We found that the semiautomatic mCBV could be a better predictor than the manual mCBV for the GBM and PCNSL differentiation. We believe that the semiautomatic segmentation method can contribute to the advancement of perfusion based brain tumor evaluation.

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참고문헌

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피인용 문헌

  1. Dynamic Susceptibility Contrast (DSC) Perfusion MR in the Prediction of Long-Term Survival of Glioblastomas (GBM): Correlation with MGMT Promoter Methylation and 1p/19q Deletions vol.22, pp.3, 2018, https://doi.org/10.13104/imri.2018.22.3.158
  2. Correlation and Characteristics of Intravoxel Incoherent Motion and Arterial Spin Labeling Techniques Versus Multiple Parameters Obtained on Dynamic Susceptibility Contrast Perfusion MRI for Brain Tum vol.66, pp.3, 2019, https://doi.org/10.2152/jmi.66.308
  3. Differential diagnosis of central lymphoma and high-grade glioma: dynamic contrast-enhanced histogram vol.61, pp.9, 2017, https://doi.org/10.1177/0284185119896519