title: Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition.
abstract: Clinical data have shown that survival rates vary considerably among brain tumor patients, according to the type and grade of the tumor. Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS (1)H NMRS) can provide important information on tumor biology and metabolism. These metabolic fingerprints can then be used for tumor classification and grading, with great potential value for tumor diagnosis. We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies, including two astrocytomas (grade I), 12 astrocytomas (grade II), eight anaplastic astrocytomas (grade III), three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS (1)H NMRS. The results were correlated with pathological features using multivariate data analysis, including principal component analysis (PCA). There were significant differences in the levels of N-acetyl-aspartate (NAA), creatine, myo-inositol, glycine and lactate between tumors of different grades (P<0.05). There were also significant differences in the ratios of NAA/creatine, lactate/creatine, myo-inositol/creatine, glycine/creatine, scyllo-inositol/creatine and alanine/creatine (P<0.05). A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%. HRMAS (1)H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.
authors: State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, 430071, China. wxchen@fudan.edu.cn
authors: WenXue
authors: W
authors: Chen
authors: HaiYan
authors: H
authors: Lou
authors: HongPing
authors: H
authors: Zhang
authors: Xiu
authors: X
authors: Nie
authors: WenXian
authors: W
authors: Lan
authors: YongXia
authors: Y
authors: Yang
authors: Yun
authors: Y
authors: Xiang
authors: JianPin
authors: J
authors: Qi
authors: Hao
authors: H
authors: Lei
authors: HuiRu
authors: H
authors: Tang
authors: FenEr
authors: F
authors: Chen
authors: Feng
authors: F
authors: Deng
mesh_terms: D001932:Brain Neoplasms / Q000145:classification / Q000175:diagnosis / Q000378:metabolism / Q000473:pathology*; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D018302:Neoplasms, Neuroepithelial / Q000145:classification / Q000175:diagnosis / Q000378:metabolism / Q000473:pathology*; D025341:Principal Component Analysis; D012680:Sensitivity and Specificity