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Differentiation between glioblastoma and primary CNS lymphoma: application of DCE-MRI parameters based on arterial input function obtained from DSC-MRI.

Koung Mi KangSeung Hong ChoiPark Chul-KeeTae Min KimSung-Hye ParkJoo Ho LeeSoon-Tae LeeInpyeong HwangRoh-Eul YooTae Jin YunJi-Hoon KimChul-Ho Sohn
Published in: European radiology (2021)
• An accurate differential diagnosis of glioblastoma and PCNSL is crucial because of different therapeutic strategies. • In contrast to the rCBV from DSC-MRI, another perfusion imaging technique, the DCE parameters for the differential diagnosis have been limited because of the low reliability of AIFs from DCE-MRI. • When we analyzed DCE-MRI data using AIFs from DSC-MRI (AIFDSC), AIFDSC-driven DCE parameters showed improved reliability and better diagnostic accuracy than rCBV for differentiating glioblastoma with low rCBV from PCNSL.
Keyphrases
  • contrast enhanced
  • magnetic resonance imaging
  • magnetic resonance
  • computed tomography
  • diffusion weighted imaging
  • machine learning
  • deep learning
  • photodynamic therapy