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. 2021 Dec;31(12):9098-9109.
doi: 10.1007/s00330-021-08044-z. Epub 2021 May 18.

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

Koung Mi Kang  1   2 , Seung Hong Choi  3   4   5 , Park Chul-Kee  6 , Tae Min Kim  7 , Sung-Hye Park  8 , Joo Ho Lee  9 , Soon-Tae Lee  10 , Inpyeong Hwang  1   2 , Roh-Eul Yoo  1   2 , Tae Jin Yun  1   2 , Ji-Hoon Kim  1   2 , Chul-Ho Sohn  1   2
Affiliations

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

Koung Mi Kang et al. Eur Radiol. 2021 Dec.

Abstract

Objective: This study aimed to evaluate whether arterial input functions (AIFs) obtained from dynamic susceptibility contrast (DSC)-MRI (AIFDSC) improve the reliability and diagnostic accuracy of dynamic contrast-enhanced (DCE)-derived pharmacokinetic (PK) parameters for differentiating glioblastoma from primary CNS lymphoma (PCNSL) compared with AIFs derived from DCE-MRI (AIFDCE).

Methods: This retrospective study included 172 patients with glioblastoma (n = 147) and PCNSL (n = 25). All patients had undergone preoperative DSC- and DCE-MRI. The volume transfer constant (Ktrans), volume of the vascular plasma space (vp), and volume of the extravascular extracellular space (ve) were acquired using AIFDSC and AIFDCE. The relative cerebral blood volume (rCBV) was obtained from DSC-MRI. Intraclass correlation coefficients (ICC) and ROC curves were used to assess the reliability and diagnostic accuracy of individual parameters.

Results: The mean Ktrans, vp, and ve values revealed better ICCs with AIFDSC than with AIFDCE (Ktrans, 0.911 vs 0.355; vp, 0.766 vs 0.503; ve, 0.758 vs 0.657, respectively). For differentiating all glioblastomas from PCNSL, the mean rCBV (AUC = 0.856) was more accurate than the AIFDSC-driven mean Ktrans, which had the largest AUC (0.711) among the DCE-derived parameters (p = 0.02). However, for glioblastomas with low rCBV (≤ 75th percentile of PCNSL; n = 30), the AIFDSC-driven mean Ktrans and vp were more accurate than rCBV (AUC: Ktrans, 0.807 vs rCBV, 0.515, p = 0.004; vp, 0.715 vs rCBV, p = 0.045).

Conclusion: DCE-derived PK parameters using the AIFDSC showed improved reliability and diagnostic accuracy for differentiating glioblastoma with low rCBV from PCNSL.

Key points: • 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.

Keywords: Diagnostic imaging; Glioblastoma; Lymphoma; Magnetic resonance imaging; Perfusion imaging.

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