title: Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification.
abstract: The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel. Water intensities were calculated from the unsuppressed MRSI data. Features were extracted from the MR images which were acquired with four different contrasts to comply with the spatial resolution of the MRSI. Evaluation was performed by investigating different combinations of the MRSI features, the MRI features and the water intensities. For each data set, the isolation in feature space of the tumor classes, healthy brain tissue and cerebrospinal fluid was calculated and visualized. A test set was used to calculate classification results for each data set. Finally, the effect of the selected feature reduction procedures on the MRSI data was investigated to ascertain whether it was more important than the addition of MRI information. Conclusions are that the combination of features from MRSI data and MRI data improves the classification result considerably when compared with features obtained from MRSI data alone. This effect is larger than the effect of specific feature reduction procedures on the MRSI data. The addition of water intensities to the data set also increases the classification result, although not significantly. We show that the combination of data from different MR investigations can be very important for brain tumor classification, particularly if a large number of tumors are to be classified simultaneously.
authors.affiliation: Laboratory for Analytical Chemistry, University of Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands.
authors.forename: Arjan W
authors.initials: AW
authors.lastname: Simonetti
authors.forename: Willem J
authors.initials: WJ
authors.lastname: Melssen
authors.forename: Fabien
authors.initials: F
authors.lastname: Szabo de Edelenyi
authors.forename: Jack J A
authors.initials: JJ
authors.lastname: van Asten
authors.forename: Arend
authors.initials: A
authors.lastname: Heerschap
authors.forename: Lutgarde M C
authors.initials: LM
authors.lastname: Buydens
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001932:Brain Neoplasms / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor