title: Improving the classification of brain tumors in mice with perturbation enhanced (PE)-MRSI.
abstract: Classifiers based on statistical pattern recognition analysis of MRSI data are becoming important tools for the non-invasive diagnosis of human brain tumors. Here we investigate the potential interest of perturbation-enhanced MRSI (PE-MRSI), in this case acute hyperglycemia, for improving the discrimination between mouse brain MRS patterns of glioblastoma multiforme (GBM), oligodendroglioma (ODG), and non-tumor brain parenchyma (NT). Six GBM-bearing mice and three ODG-bearing mice were scanned at 7 Tesla by PRESS-MRSI with 12 and 136 ms echo-time, during euglycemia (Eug) and also during induced acute hyperglycemia (Hyp), generating altogether four datasets per animal (echo time + glycemic condition): 12Eug, 136Eug, 12Hyp, and 136Hyp. For classifier development all spectral vectors (spv) selected from the MRSI matrix were unit length normalized (UL2) and used either as a training set (76 GBM spv, four mice; 70 ODG spv, two mice; 54 NT spv) or as an independent testing set (61 GBM spv, two mice; 31 ODG, one mouse; 23 NT spv). All Fisher's LDA classifiers obtained were evaluated as far as their descriptive performance-correctly classified cases of the training set (bootstrapping)-and predictive accuracy-balanced error rate of independent testing set classification. MRSI-based classifiers at 12Hyp were consistently more efficient in separating GBM, ODG, and NT regions, with overall accuracies always >80% and up to 95-96%; remaining classifiers were within the 48-85% range. This was also confirmed by user-independent selection of training and testing sets, using leave-one-out (LOO). This highlights the potential interest of perturbation-enhanced MRSI protocols for improving the non-invasive characterization of preclinical brain tumors.
authors.affiliation: Bioquímica i Biologia Molecular, Facultat de Biociències, Universitat Autònoma de Barcelona, Spain.
authors.forename: Rui Vasco
authors.initials: RV
authors.lastname: Simões
authors.forename: Sandra
authors.initials: S
authors.lastname: Ortega-Martorell
authors.forename: Teresa
authors.initials: T
authors.lastname: Delgado-Goñi
authors.forename: Yann
authors.initials: Y
authors.lastname: Le Fur
authors.forename: Martí
authors.initials: M
authors.lastname: Pumarola
authors.forename: Ana Paula
authors.initials: AP
authors.lastname: Candiota
authors.forename: Juana
authors.initials: J
authors.lastname: Martín
authors.forename: Radka
authors.initials: R
authors.lastname: Stoyanova
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Cozzone
authors.forename: Margarida
authors.initials: M
authors.lastname: Julià-Sapé
authors.forename: Carles
authors.initials: C
authors.lastname: Arús
mesh_terms: D000818:Animals; D001786:Blood Glucose / Q000378:metabolism; D001932:Brain Neoplasms / Q000097:blood / Q000145:classification* / Q000473:pathology; D005260:Female; D005909:Glioblastoma / Q000097:blood / Q000145:classification* / Q000473:pathology; D006651:Histocytochemistry; D006943:Hyperglycemia / Q000378:metabolism; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D008822:Mice, Transgenic; D009837:Oligodendroglioma / Q000097:blood / Q000145:classification* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods
chemical_list: D001786:Blood Glucose