title: New similarity search based glioma grading.
abstract: BACKGROUND
  MR-based differentiation between low- and high-grade gliomas is predominately based on contrast-enhanced T1-weighted images (CE-T1w). However, functional MR sequences as perfusion- and diffusion-weighted sequences can provide additional information on tumor grade. Here, we tested the potential of a recently developed similarity search based method that integrates information of CE-T1w and perfusion maps for non-invasive MR-based glioma grading.
  
  
  METHODS
  We prospectively included 37 untreated glioma patients (23 grade I/II, 14 grade III gliomas), in whom 3T MRI with FLAIR, pre- and post-contrast T1-weighted, and perfusion sequences was performed. Cerebral blood volume, cerebral blood flow, and mean transit time maps as well as CE-T1w images were used as input for the similarity search. Data sets were preprocessed and converted to four-dimensional Gaussian Mixture Models that considered correlations between the different MR sequences. For each patient, a so-called tumor feature vector (= probability-based classifier) was defined and used for grading. Biopsy was used as gold standard, and similarity based grading was compared to grading solely based on CE-T1w.
  
  
  RESULTS
  Accuracy, sensitivity, and specificity of pure CE-T1w based glioma grading were 64.9%, 78.6%, and 56.5%, respectively. Similarity search based tumor grading allowed differentiation between low-grade (I or II) and high-grade (III) gliomas with an accuracy, sensitivity, and specificity of 83.8%, 78.6%, and 87.0%.
  
  
  CONCLUSIONS
  Our findings indicate that integration of perfusion parameters and CE-T1w information in a semi-automatic similarity search based analysis improves the potential of MR-based glioma grading compared to CE-T1w data alone.
authors.affiliation: Department of Neuroradiology, Ludwig-Maximilians-University of Munich, Marchioninistrasse 15, 81377, Munich, Germany. katrin.haegler@med.uni-muenchen.de
authors.forename: Katrin
authors.initials: K
authors.lastname: Haegler
authors.forename: Martin
authors.initials: M
authors.lastname: Wiesmann
authors.forename: Christian
authors.initials: C
authors.lastname: Böhm
authors.forename: Jessica
authors.initials: J
authors.lastname: Freiherr
authors.forename: Oliver
authors.initials: O
authors.lastname: Schnell
authors.forename: Hartmut
authors.initials: H
authors.lastname: Brückmann
authors.forename: Jörg-Christian
authors.initials: JC
authors.lastname: Tonn
authors.forename: Jennifer
authors.initials: J
authors.lastname: Linn
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001706:Biopsy; D001783:Blood Flow Velocity; D001932:Brain Neoplasms / Q000098:blood supply / Q000473:pathology*; D002560:Cerebrovascular Circulation; D003287:Contrast Media; D005260:Female; D005910:Glioma / Q000098:blood supply / Q000473:pathology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media