title: Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine.
abstract: OBJECTIVE
  Current imaging assessment of high-grade brain tumors relies on the Response Assessment in Neuro-Oncology criteria, which measure gross volume of enhancing and nonenhancing lesions from conventional MRI sequences. These assessments may fail to reliably distinguish tumor and nontumor. This study aimed to classify enhancing and nonenhancing lesion areas into tumor-versus-nontumor components.
  
  
  METHODS
  A total of 140 MRI scans obtained from 32 patients with high-grade gliomas and 6 patients with brain metastases were included. Classification of lesion areas was performed using a support vector machine classifier trained on 4 components: enhancing and nonenhancing, tumor and nontumor, based on T1-weighted, FLAIR, and dynamic-contrast-enhancing MRI parameters. Classification results were evaluated by 2-fold cross-validation analysis of the training set and MR spectroscopy. Longitudinal changes of the component volumes were compared with Response Assessment in Neuro-Oncology criteria.
  
  
  RESULTS
  Normalized T1-weighted values, FLAIR, plasma volume, volume transfer constant, and bolus-arrival-time parameters differentiated components. High sensitivity and specificity (100%) were obtained within the enhancing and nonenhancing areas. Longitudinal changes in component volumes correlated with the Response Assessment in Neuro-Oncology criteria in 27 patients; 5 patients (16%) demonstrated an increase in tumor component volumes indicating tumor progression. These changes preceded Response Assessment in Neuro-Oncology assessments by several months. Seven patients treated with bevacizumab showed a shift to an infiltrative pattern of progression.
  
  
  CONCLUSIONS
  This study proposes an automatic classification method: segmented Response Assessment in Neuro-Oncology criteria based on advanced imaging that reliably differentiates tumor and nontumor components in high-grade gliomas. The segmented Response Assessment in Neuro-Oncology criteria may improve therapy-response assessment and provide earlier indication of progression.
authors.affiliation: From the Neuro-Oncology Service (D.T.B., F.B.).|Sackler Faculty of Medicine (D.T.B., M.A., F.B., D.B.B.).
authors.forename: D T
authors.identifier: http://orcid.org/0000-0002-8868-0148
authors.initials: DT
authors.lastname: Blumenthal
authors.affiliation: Functional Brain Center (M.A., O.A., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.|Sackler Faculty of Medicine (D.T.B., M.A., F.B., D.B.B.).
authors.forename: M
authors.identifier: http://orcid.org/0000-0003-4214-1875
authors.initials: M
authors.lastname: Artzi
authors.affiliation: Department of Chemical Physics (G.L.), Weizmann Institute, Tel Aviv, Israel.
authors.forename: G
authors.identifier: http://orcid.org/0000-0002-2325-1879
authors.initials: G
authors.lastname: Liberman
authors.affiliation: From the Neuro-Oncology Service (D.T.B., F.B.).|Sackler Faculty of Medicine (D.T.B., M.A., F.B., D.B.B.).
authors.forename: F
authors.initials: F
authors.lastname: Bokstein
authors.affiliation: Functional Brain Center (M.A., O.A., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
authors.forename: O
authors.identifier: http://orcid.org/0000-0003-3422-0901
authors.initials: O
authors.lastname: Aizenstein
authors.affiliation: Functional Brain Center (M.A., O.A., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel dafnab@tlvmc.gov.il.|Sackler Faculty of Medicine (D.T.B., M.A., F.B., D.B.B.).|Sagol School of Neuroscience (D.B.B.), Tel Aviv University, Tel Aviv, Israel.
authors.forename: D
authors.identifier: http://orcid.org/0000-0001-8396-1031
authors.initials: D
authors.lastname: Ben Bashat
mesh_terms: D000328:Adult; D001932:Brain Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D005910:Glioma / Q000145:classification* / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D009682:Magnetic Resonance Spectroscopy; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D012680:Sensitivity and Specificity; D060388:Support Vector Machine