title: Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma.
abstract: OBJECTIVE
  The O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. However, determination of the MGMT promoter methylation status requires tissue obtained via surgical resection or biopsy. The aim of this study was to assess the ability of quantitative and qualitative imaging variables in predicting MGMT methylation status noninvasively.
  
  
  METHODS
  A retrospective analysis of MR images from GBM patients was conducted. Multivariate prediction models were obtained by machine-learning methods and tested on data from The Cancer Genome Atlas (TCGA) database.
  
  
  RESULTS
  The status of MGMT promoter methylation was predicted with an accuracy of up to 73.6%. Experimental analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were the most significant variables in respect to the status of MGMT promoter methylation in GBM.
  
  
  CONCLUSIONS
  The obtained results provide further evidence of an association between standard preoperative MRI variables and MGMT methylation status in GBM.
authors.affiliation: Department of Electrical and Computer Engineering, University of Patras, Patras, Greece; Department of Computer Engineering and Informatics, University of Patras, Patras, Greece.
authors.forename: Vasileios G
authors.initials: VG
authors.lastname: Kanas
authors.affiliation: Department of Computer Engineering and Informatics, University of Patras, Patras, Greece; Center for Visual Computing (CVC), CentraleSupélec, INRIA, Université Paris-Saclay, France. Electronic address: evangelia.zacharaki@centralesupelec.fr.
authors.forename: Evangelia I
authors.initials: EI
authors.lastname: Zacharaki
authors.affiliation: Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Ginu A
authors.initials: GA
authors.lastname: Thomas
authors.affiliation: Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA.
authors.forename: Pascal O
authors.initials: PO
authors.lastname: Zinn
authors.affiliation: Department of Computer Engineering and Informatics, University of Patras, Patras, Greece.
authors.forename: Vasileios
authors.initials: V
authors.lastname: Megalooikonomou
authors.affiliation: Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Rivka R
authors.initials: RR
authors.lastname: Colen
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000235:genetics; D019175:DNA Methylation; D015254:DNA Modification Methylases / Q000235:genetics / Q000378:metabolism*; D045643:DNA Repair Enzymes / Q000235:genetics / Q000378:metabolism*; D005260:Female; D005909:Glioblastoma / Q000000981:diagnostic imaging* / Q000235:genetics; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D011401:Promoter Regions, Genetic; D025521:Tumor Suppressor Proteins / Q000235:genetics / Q000378:metabolism*; D055815:Young Adult
chemical_list: D025521:Tumor Suppressor Proteins; D015254:DNA Modification Methylases; C515491:MGMT protein, human; D045643:DNA Repair Enzymes
keywords: Feature extraction; Glioblastoma; MGMT promoter methylation; Multivariate analysis; Prediction model