title: Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data.
abstract: OBJECTIVE
  Differentiation of glioblastomas from metastases is clinical important, but may be difficult even for expert observers. To investigate the contribution of machine learning algorithms in the differentiation of glioblastomas multiforme (GB) from metastases, we developed and tested a pattern recognition system based on 3T magnetic resonance (MR) data.
  
  
  METHODS
  Single and multi-voxel proton magnetic resonance spectroscopy (1H-MRS) and dynamic susceptibility contrast (DSC) MRI scans were performed on 49 patients with solitary brain tumors (35 glioblastoma multiforme and 14 metastases). Metabolic (NAA/Cr, Cho/Cr, (Lip [Formula: see text] Lac)/Cr) and perfusion (rCBV) parameters were measured in both intratumoral and peritumoral regions. The statistical significance of these parameters was evaluated. For the classification procedure, three datasets were created to find the optimum combination of parameters that provides maximum differentiation. Three machine learning methods were utilized: Naïve-Bayes, Support Vector Machine (SVM) and [Formula: see text]-nearest neighbor (KNN). The discrimination ability of each classifier was evaluated with quantitative performance metrics.
  
  
  RESULTS
  Glioblastoma and metastases were differentiable only in the peritumoral region of these lesions ([Formula: see text]). SVM achieved the highest overall performance (accuracy 98%) for both the intratumoral and peritumoral areas. Naïve-Bayes and KNN presented greater variations in performance. The proper selection of datasets plays a very significant role as they are closely correlated to the underlying pathophysiology.
  
  
  CONCLUSIONS
  The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.
authors: Medical Physics Department, Medical School, University of Thessaly, 41110 , Biopolis, Larissa, Greece, etsolaki@med.uth.gr.
authors: Evangelia
authors: E
authors: Tsolaki
authors: Patricia
authors: P
authors: Svolos
authors: Evanthia
authors: E
authors: Kousi
authors: Eftychia
authors: E
authors: Kapsalaki
authors: Konstantinos
authors: K
authors: Fountas
authors: Kyriaki
authors: K
authors: Theodorou
authors: Ioannis
authors: I
authors: Tsougos
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000473:pathology*; D003937:Diagnosis, Differential; D005260:Female; D005909:Glioblastoma / Q000175:diagnosis* / Q000556:secondary; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009362:Neoplasm Metastasis / Q000175:diagnosis; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results