title: Molecular and metabolic pattern classification for detection of brain glioma progression.
abstract: OBJECTIVE
  The ability to differentiate between brain tumor progression and radiation therapy induced necrosis is critical for appropriate patient management. In order to improve the differential diagnosis, we combined fluorine-18 2-fluoro-deoxyglucose positron emission tomography ((18)F-FDG PET), proton magnetic resonance spectroscopy ((1)H MRS) and histological data to develop a multi-parametric machine-learning model.
  
  
  METHODS
  We enrolled twelve post-therapy patients with grade 2 and 3 gliomas that were suspicious of tumor progression. All patients underwent (18)F-FDG PET and (1)H MRS. Maximal standardized uptake value (SUVmax) of the tumors and reference regions were obtained. Multiple 2D maps of choline (Cho), creatine (Cr), and N-acetylaspartate (NAA) of the tumors were generated. A support vector machine (SVM) learning model was established to take imaging biomarkers and histological data as input vectors. A combination of clinical follow-up and multiple sequential MRI studies served as the basis for assessing the clinical outcome. All vector combinations were evaluated for diagnostic accuracy and cross validation. The optimal cutoff value of individual parameters was calculated using Receiver operating characteristic (ROC) plots.
  
  
  RESULTS
  The SVM and ROC analyses both demonstrated that SUVmax of the lesion was the most significant single diagnostic parameter (75% accuracy) followed by Cho concentration (67% accuracy). SVM analysis of all paired parameters showed SUVmax and Cho concentration in combination could achieve 83% accuracy. SUVmax of the lesion paired with SUVmax of the white matter as well as the tumor Cho paired with the tumor Cr both showed 83% accuracy. These were the most significant paired diagnostic parameters of either modality. Combining all four parameters did not improve the results. However, addition of two more parameters, Cho and Cr of brain parenchyma contralateral to the tumor, increased the accuracy to 92%.
  
  
  CONCLUSIONS
  This study suggests that SVM models may improve detection of glioma progression more accurately than single parametric imaging methods.
  National Cancer Institute, Cancer Center Support Grant Supplement Award, Imaging Response Assessment Teams.
authors: Department of Radiology, University of Pittsburgh Medical Center, PA, United States. Electronic address: imanif@upmc.edu.
authors: Farzin
authors: F
authors: Imani
authors: Department of Radiology, University of Pittsburgh Medical Center, PA, United States.
authors: Fernando E
authors: FE
authors: Boada
authors: Department of Neurology, University of Pittsburgh Medical Center, PA, United States.
authors: Frank S
authors: FS
authors: Lieberman
authors: Department of Radiology, University of Pittsburgh Medical Center, PA, United States.
authors: Denise K
authors: DK
authors: Davis
authors: Department of Radiology, University of Pittsburgh Medical Center, PA, United States.
authors: James M
authors: JM
authors: Mountz
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D019788:Fluorodeoxyglucose F18 / Q000493:pharmacokinetics*; D005910:Glioma / Q000175:diagnosis* / Q000378:metabolism*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D008297:Male; D008875:Middle Aged; D057054:Molecular Imaging / Q000379:methods; D049268:Positron-Emission Tomography / Q000379:methods; D011522:Protons; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D014408:Biomarkers, Tumor; D011522:Protons; D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
keywords: (1)H MRS; (18)F-FDG PET; Glioma; Support vector machine