title: Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma.
abstract: BACKGROUND
  Current methods for evaluation of treatment response in glioblastoma are inaccurate, limited and time-consuming. This study aimed to develop a multi-modal MRI automatic classification method to improve accuracy and efficiency of treatment response assessment in patients with recurrent glioblastoma (GB).
  
  
  METHODS
  A modification of the k-Nearest-Neighbors (kNN) classification method was developed and applied to 59 longitudinal MR data sets of 13 patients with recurrent GB undergoing bevacizumab (anti-angiogenic) therapy. Changes in the enhancing tumor volume were assessed using the proposed method and compared with Macdonald's criteria and with manual volumetric measurements. The edema-like area was further subclassified into peri- and non-peri-tumoral edema, using both the kNN method and an unsupervised method, to monitor longitudinal changes.
  
  
  RESULTS
  Automatic classification using the modified kNN method was applicable in all scans, even when the tumors were infiltrative with unclear borders. The enhancing tumor volume obtained using the automatic method was highly correlated with manual measurements (N=33, r=0.96, p<0.0001), while standard radiographic assessment based on Macdonald's criteria matched manual delineation and automatic results in only 68% of cases. A graded pattern of tumor infiltration within the edema-like area was revealed by both automatic methods, showing high agreement. All classification results were confirmed by a senior neuro-radiologist and validated using MR spectroscopy.
  
  
  CONCLUSIONS
  This study emphasizes the important role of automatic tools based on a multi-modal view of the tissue in monitoring therapy response in patients with high grade gliomas specifically under anti-angiogenic therapy.
authors.affiliation: Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
authors.forename: Gilad
authors.initials: G
authors.lastname: Liberman
authors.forename: Yoram
authors.initials: Y
authors.lastname: Louzoun
authors.forename: Orna
authors.initials: O
authors.lastname: Aizenstein
authors.forename: Deborah T
authors.initials: DT
authors.lastname: Blumenthal
authors.forename: Felix
authors.initials: F
authors.lastname: Bokstein
authors.forename: Mika
authors.initials: M
authors.lastname: Palmon
authors.forename: Benjamin W
authors.initials: BW
authors.lastname: Corn
authors.forename: Dafna
authors.initials: D
authors.lastname: Ben Bashat
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D020533:Angiogenesis Inhibitors / Q000627:therapeutic use; D061067:Antibodies, Monoclonal, Humanized / Q000627:therapeutic use*; D001185:Artificial Intelligence; D000068258:Bevacizumab; D001932:Brain Neoplasms / Q000188:drug therapy* / Q000473:pathology*; D005260:Female; D005909:Glioblastoma / Q000188:drug therapy* / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: D020533:Angiogenesis Inhibitors; D061067:Antibodies, Monoclonal, Humanized; D000068258:Bevacizumab