Differentiating between central nervous system lymphoma and high-grade glioma using dynamic susceptibility contrast and dynamic contrast-enhanced MR imaging with histogram analysis

Abstract

Purpose: We evaluated the diagnostic performance of histogram analysis of data from a combination of dynamic susceptibility contrast (DSC)-MRI and dynamic contrast-enhanced (DCE)-MRI for quantitative differentiation between central nervous system lymphoma (CNSL) and high-grade glioma (HGG), with the aim of identifying useful perfusion parameters as objective radiological markers for differentiating between them. Methods: Eight lesions with CNSLs and 15 with HGGs who underwent MRI examination, including DCE and DSC-MRI, were enrolled in our retrospective study. DSC-MRI provides a corrected cerebral blood volume (cCBV), and DCE-MRI provides a volume transfer coefficient (Ktrans) for transfer from plasma to the extravascular extracellular space. Ktransand cCBV were measured from a round region-of-interest in the slice of maximum size on the contrast-enhanced lesion. The differences in t values between CNSL and HGG for determining the most appropriate percentile of Ktransand cCBV were investigated. The differences in Ktrans, cCBV, and Ktrans/cCBV between CNSL and HGG were investigated using histogram analysis. Receiver oper­ating characteristic (ROC) analysis of Ktrans, cCBV, and Ktrans/cCBV ratio was performed. Results: The 30th percentile (C30) in Ktransand 80th percentile (C80) in cCBV were the most appropriate percentiles for distinguishing between CNSL and HGG from the differences in t values. CNSL showed sig­nificantly lower C80 cCBV, significantly higher C30 Ktrans, and significantly higher C30 Ktrans/C80 cCBV than those of HGG. In ROC analysis, C30 Ktrans/C80 cCBV had the best discriminative value for differentiating between CNSL and HGG as compared to C30 Ktransor C80 cCBV. Conclusion: The combination of Ktransby DCE-MRI and cCBV by DSC-MRI was found to reveal the charac­teristics of vascularity and permeability of a lesion more precisely than either Ktransor cCBV alone. Histogram analysis of these vascular microenvironments enabled quantitative differentiation between CNSL and HGG.