title: Differentiation of primary central nervous system lymphomas from high-grade gliomas by rCBV and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging.
abstract: OBJECTIVE
  Primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) may have similar enhancement patterns on magnetic resonance imaging (MRI), making the differential diagnosis difficult or even impractical. Relative cerebral blood volume (rCBV) and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MR imaging may help distinguish PCNSL from HGG. The purpose of this study was to evaluate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of these two imaging parameters used alone or in combination for differentiating PCNSL from HGG.
  
  
  METHODS
  A total of 12 patients with PCNSL and 26 patients with HGG were examined using a 3T scanner. rCBV and percentage of signal intensity recovery were obtained and receiver operating characteristic (ROC) analysis was performed to determine optimum thresholds for tumor differentiation. Sensitivity, specificity, PPV, NPV, and accuracy for identifying the tumor types were also calculated.
  
  
  RESULTS
  The optimum threshold of 2.56 for rCBV provided sensitivity, specificity, PPV, NPV, and accuracy of 96.2, 90, 92.6, 94.7, and 93.5%, respectively, for determining PCNSL. A threshold value of 0.89 for percentage of signal intensity recovery optimized differentiation of PCNSL and HGG with a sensitivity, specificity, PPV, NPV, and accuracy of 100, 88.5, 87, 100, and 93.5%, respectively. Combining rCBV with the percentage of signal intensity recovery further improved the differentiation of PCNSL and HGG with a specificity of 98.5% and an accuracy of 95.7%.
  
  
  CONCLUSIONS
  The combination of rCBV measurement with percentage of signal intensity recovery can help in more accurate differentiation of PCNSL from HGG.
authors.affiliation: Department of Radiology, First Affiliated Hospital, Fujian Medical University, 20 Cha-Zhong Road, 350005, Fuzhou, Fujian, P.R. China.
authors.forename: Z
authors.initials: Z
authors.lastname: Xing
authors.forename: R X
authors.initials: RX
authors.lastname: You
authors.forename: J
authors.initials: J
authors.lastname: Li
authors.forename: Y
authors.initials: Y
authors.lastname: Liu
authors.forename: D R
authors.initials: DR
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D001811:Blood Volume Determination / Q000379:methods; D001932:Brain Neoplasms / Q000175:diagnosis*; D003287:Contrast Media; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D019786:Gadolinium DTPA; D005910:Glioma / Q000175:diagnosis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008223:Lymphoma / Q000175:diagnosis*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D060787:Neoplasm Grading; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA