title: Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data.
abstract: BACKGROUND
  A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification.
  
  
  METHODS
  MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data.
  
  
  RESULTS
  The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79% in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible.
  
  
  CONCLUSIONS
  FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.
authors.affiliation: Medical Physics Department, Medical School, University of Thessaly, 41110 , Biopolis, Larissa, Greece.
authors.forename: Evangelia
authors.initials: E
authors.lastname: Tsolaki
authors.forename: Patricia
authors.initials: P
authors.lastname: Svolos
authors.forename: Evanthia
authors.initials: E
authors.lastname: Kousi
authors.forename: Eftychia
authors.initials: E
authors.lastname: Kapsalaki
authors.forename: Ioannis
authors.initials: I
authors.lastname: Fezoulidis
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Fountas
authors.forename: Kyriaki
authors.initials: K
authors.lastname: Theodorou
authors.forename: Constantine
authors.initials: C
authors.lastname: Kappas
authors.forename: Ioannis
authors.initials: I
authors.lastname: Tsougos
mesh_terms: D001921:Brain / Q000378:metabolism / Q000473:pathology*; D001932:Brain Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D020000:Decision Support Systems, Clinical; D005910:Glioma / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D060388:Support Vector Machine