title: Machine learning in preoperative glioma MRI: survival associations by perfusion-based support vector machine outperforms traditional MRI.
abstract: OBJECTIVE
  To retrospectively evaluate the performance of an automatic support vector machine (SVM) routine in combination with perfusion-based dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) for preoperative survival associations in patients with gliomas and compare our results to traditional MRI.
  
  
  METHODS
  The study was approved by the Ethics Committee and informed consent was signed. Structural, diffusion- and perfusion-weighted MRI was performed at 1.5-T preoperatively in 94 adult patients (49 males, 45 females, 23-82 years; mean 51 years) later diagnosed with a primary glioma. Patients were randomly assigned in training and test datasets and the resulting DSC-based survival associations by SVM were compared to traditional MRI features including contrast-agent enhancement, perfusion- and diffusion-weighted imaging, tumor size, and location. The results were adjusted for age, neurological status, and postoperative factors associated with survival, including surgery and adjuvant therapy.
  
  
  RESULTS
  For 1- (26/33 alive, 11/14 deceased), 2- (15/21, 21/26), 3- (12/16, 27/31) and 4- (12/15, 28/32) year survival associations in the test dataset (47 patients), the SVM routine was the only biomarker to consistently associate with survival (Cox; P < 0.001).
  
  
  CONCLUSIONS
  The automatic machine learning routine presented in our study may provide the operator with a reliable instrument for assessing survival in patients with glioma.
authors.affiliation: Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Intervention Centre, Rikshospitalet, Oslo University Hospital, Oslo, Norway.
authors.forename: Kyrre E
authors.initials: KE
authors.lastname: Emblem
authors.forename: Paulina
authors.initials: P
authors.lastname: Due-Tonnessen
authors.forename: John K
authors.initials: JK
authors.lastname: Hald
authors.forename: Atle
authors.initials: A
authors.lastname: Bjornerud
authors.forename: Marco C
authors.initials: MC
authors.lastname: Pinho
authors.forename: David
authors.initials: D
authors.lastname: Scheie
authors.forename: Lothar R
authors.initials: LR
authors.lastname: Schad
authors.forename: Torstein R
authors.initials: TR
authors.lastname: Meling
authors.forename: Frank G
authors.initials: FG
authors.lastname: Zoellner
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001932:Brain Neoplasms / Q000401:mortality / Q000473:pathology* / Q000601:surgery*; D005260:Female; D005910:Glioma / Q000401:mortality / Q000473:pathology* / Q000601:surgery*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011300:Preoperative Care / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D015996:Survival Rate; D016896:Treatment Outcome; D055815:Young Adult
keywords: computer aided diagnosis (CAD); glioma; histogram analysis; perfusion MRI; survival associations