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

Kazuhiro Murayama, Yuya Nishiyama, Yuichi Hirose, Masato Abe, Shigeharu Ohyu, Ayako Ninomiya, Takashi Fukuba, Kazuhiro Katada, Hiroshi Toyama

Research output: Contribution to journalArticle

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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.

Original languageEnglish
Pages (from-to)42-49
Number of pages8
JournalMagnetic Resonance in Medical Sciences
Volume17
Issue number1
DOIs
Publication statusPublished - 01-01-2018

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Glioma
Non-Hodgkin's Lymphoma
Central Nervous System
Cerebral Blood Volume
ROC Curve
Extracellular Space
Blood Vessels
Permeability
Lymphoma
Retrospective Studies
Perfusion

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

Cite this

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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.",
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Differentiating between central nervous system lymphoma and high-grade glioma using dynamic susceptibility contrast and dynamic contrast-enhanced MR imaging with histogram analysis. / Murayama, Kazuhiro; Nishiyama, Yuya; Hirose, Yuichi; Abe, Masato; Ohyu, Shigeharu; Ninomiya, Ayako; Fukuba, Takashi; Katada, Kazuhiro; Toyama, Hiroshi.

In: Magnetic Resonance in Medical Sciences, Vol. 17, No. 1, 01.01.2018, p. 42-49.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Differentiating between central nervous system lymphoma and high-grade glioma using dynamic susceptibility contrast and dynamic contrast-enhanced MR imaging with histogram analysis

AU - Murayama, Kazuhiro

AU - Nishiyama, Yuya

AU - Hirose, Yuichi

AU - Abe, Masato

AU - Ohyu, Shigeharu

AU - Ninomiya, Ayako

AU - Fukuba, Takashi

AU - Katada, Kazuhiro

AU - Toyama, Hiroshi

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

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