title: Diagnostic value of peritumoral minimum apparent diffusion coefficient for differentiation of glioblastoma multiforme from solitary metastatic lesions.
abstract: OBJECTIVE
  In glioblastoma multiforme, the peritumoral region may be infiltrated with malignant cells in addition to vasogenic edema, whereas in a metastatic deposit, the peritumoral areas comprise predominantly vasogenic edema. The purpose of this study was to determine whether the minimum apparent diffusion coefficient (ADC) can be used to differentiate glioblastoma from solitary metastasis on the basis of cellularity levels in the enhancing tumor and in the peritumoral region.
  
  
  METHODS
  Seventy-three patients underwent conventional MRI and diffusion-weighted imaging (DWI) before undergoing treatment. The minimum ADC was measured in the enhancing tumor, peritumoral region, and contralateral normal white matter. To determine whether there was a statistical difference between metastasis and glioblastoma, we analyzed patient age and sex, minimum ADC value, and ADC ratio of the two groups. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff value of the minimum ADC that had the best combination of sensitivity and specificity for distinguishing between glioblastoma and metastasis.
  
  
  RESULTS
  The mean minimum ADC values and mean ADC ratios in the peritumoral regions of glioblastomas were significantly higher than those in metastases. However, the mean minimum ADC values and mean ADC ratios in enhancing tumors showed no statistically significant difference between the two groups. According to ROC curve analysis, a cutoff value of 1.302 × 10(-3) mm(2)/s for the minimum peritumoral ADC value generated the best combination of sensitivity (82.9%) and specificity (78.9%) for distinguishing between glioblastoma and metastasis.
  
  
  CONCLUSIONS
  Although the characteristics of solitary metastasis and glioblastoma multiforme may be similar on conventional MRI, DWI can offer diagnostic information to distinguish between the tumors.
authors.affiliation: Department of Radiology, Kwandong University, College of Medicine, Myongji Hospital, Koyang City, Gyunggi-Do, South Korea.
authors.forename: Eun Ja
authors.initials: EJ
authors.lastname: Lee
authors.forename: Karel
authors.initials: K
authors.lastname: terBrugge
authors.forename: David
authors.initials: D
authors.lastname: Mikulis
authors.forename: Dae Seob
authors.initials: DS
authors.lastname: Choi
authors.forename: Jong Myon
authors.initials: JM
authors.lastname: Bae
authors.forename: Seon Kyu
authors.initials: SK
authors.lastname: Lee
authors.forename: Soon Young
authors.initials: SY
authors.lastname: Moon
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D016009:Chi-Square Distribution; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D005909:Glioblastoma / Q000175:diagnosis* / Q000556:secondary*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009362:Neoplasm Metastasis / Q000175:diagnosis; D012372:ROC Curve; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric