title: Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering.
abstract: OBJECTIVE
  To assess whether glioma volumes from knowledge-based fuzzy c-means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging.
  
  
  METHODS
  Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed.
  
  
  RESULTS
  The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas (P = 0.576-0.970). When identifying a high-risk patient group (expected survival <2 years) and a low-risk patient group (expected survival >2 years), a higher log-rank value from Kaplan-Meier survival analysis was observed when using automatic tumor volumes (14.403; P < 0.001) compared with the manual volumes (10.650-12.761; P = 0.001-0.002).
  
  
  CONCLUSIONS
  Our results suggest that knowledge-based FCM clustering of multiple MR image classes provides a completely automatic, user-independent approach to selecting the target region for presurgical glioma characterization.
authors: Department of Medical Physics, Rikshospitalet University Hospital, Oslo, Norway. kyrre.eeg.emblem@rikshospitalet.no
authors: Kyrre E
authors: KE
authors: Emblem
authors: Baard
authors: B
authors: Nedregaard
authors: John K
authors: JK
authors: Hald
authors: Terje
authors: T
authors: Nome
authors: Paulina
authors: P
authors: Due-Tonnessen
authors: Atle
authors: A
authors: Bjornerud
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D019540:Area Under Curve; D001932:Brain Neoplasms / Q000473:pathology*; D003287:Contrast Media; D017352:Echo-Planar Imaging / Q000379:methods; D005260:Female; D017143:Fuzzy Logic; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D009942:Organometallic Compounds; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D055815:Young Adult
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; C090600:gadobutrol