title: Classification of 1H MR spectra of human brain neoplasms: the influence of preprocessing and computerized consensus diagnosis on classification accuracy.
abstract: We study how classification accuracy can be improved when both different data preprocessing methods and computerized consensus diagnosis (CCD) are applied to 1H magnetic resonance (MR) spectra of astrocytomas, meningiomas, and epileptic brain tissue. The MR spectra (360 MHz, 37 degrees C) of tissue specimens (biopsies) from subjects with meningiomas (95; 26 cases), astrocytomas (74; 26 cases), and epilepsy (37; 8 cases) were preprocessed by several methods. Each data set was partitioned into training and validation sets. Robust classification was carried out via linear discriminant analysis (LDA), artificial neural nets (NN), and CCD, and the results were compared with histopathological diagnosis of the MR specimens. Normalization of the relevant spectral regions affects classification accuracy significantly. The spectra-based average three-class classification accuracies of LDA and NN increased from 81.7% (unnormalized data sets) to 89.9% (normalized). CCD increased the classification accuracy of the normalized sets to an average of 91.8%. CCD invariably decreases the fraction of unclassifiable spectra. The same trends prevail, with improved results, for case-based classification. Preprocessing the 1H MR spectra is essential for accurate and reliable classification of astrocytomas, meningiomas, and nontumorous epileptic brain tissue. CCD improves classification accuracy, with an attendant decrease in the fraction of unclassifiable spectra or cases.
authors.affiliation: Institute for Biodiagnostics, National Research Council, Winnipeg, Manitoba, Canada.
authors.forename: R L
authors.initials: RL
authors.lastname: Somorjai
authors.forename: B
authors.initials: B
authors.lastname: Dolenko
authors.forename: A K
authors.initials: AK
authors.lastname: Nikulin
authors.forename: N
authors.initials: N
authors.lastname: Pizzi
authors.forename: G
authors.initials: G
authors.lastname: Scarth
authors.forename: P
authors.initials: P
authors.lastname: Zhilkin
authors.forename: W
authors.initials: W
authors.lastname: Halliday
authors.forename: D
authors.initials: D
authors.lastname: Fewer
authors.forename: N
authors.initials: N
authors.lastname: Hill
authors.forename: I
authors.initials: I
authors.lastname: Ross
authors.forename: M
authors.initials: M
authors.lastname: West
authors.forename: I C
authors.initials: IC
authors.lastname: Smith
authors.forename: S M
authors.initials: SM
authors.lastname: Donnelly
authors.forename: A C
authors.initials: AC
authors.lastname: Kuesel
authors.forename: K M
authors.initials: KM
authors.lastname: Brière
mesh_terms: D001254:Astrocytoma / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D001706:Biopsy; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D004827:Epilepsy / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation*; D009682:Magnetic Resonance Spectroscopy / Q000295:instrumentation*; D008577:Meningeal Neoplasms / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D008579:Meningioma / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity