title: Predicting survival of children with CNS tumors using proton magnetic resonance spectroscopic imaging biomarkers.
abstract: Using brain proton magnetic resonance spectroscopic imaging (MRSI) in children with central nervous system (CNS) tumors, we tested the hypothesis that combining information from biologically important metabolites, at diagnosis and prior to treatment, would improve prediction of survival. We evaluated brain proton MRSI exams in 76 children (median age at diagnosis: 74 months) with brain tumors. Important biomarkers, choline-containing compounds (Cho), N-acetylaspartate (NAA), total creatine (tCr), lipids and/or lactate (L), were measured at the "highest Cho region" and normalized to the tCr of surrounding healthy tissue. Neuropathological grading was performed using World Health Organization (WHO) criteria. Fifty-eight of 76 (76%) patients were alive at the end of the study period. The mean survival time for all subjects was 52 months. Univariate analysis demonstrated that Cho, L, Cho/NAA and tumor grade differed significantly between survivors and non-survivors (P< or =0.05). Multiple logistic regression and stepwise multivariate Cox regression indicated that Cho + 0.1L was the only independent predictor of survival (likelihood ratio test = 10.27, P<0.001; Cox regression, P=0.004). The combined index Cho + 0.1L was more accurate and more specific predictor than Cho or Cho/NAA. Accuracy and specificity for Cho + 0.1L were 80% and 86%, respectively. We conclude that brain proton MRSI biomarkers predict survival of children with CNS tumors better than does standard histopathology. More accurate prediction using this non-invasive technique represents an important advance and may suggest more appropriate therapy, especially when diagnostic biopsy is not feasible.
authors.affiliation: Department of Radiation Oncology, Children's Hospital Boston, 51 Blossom Street, Boston, MA 02114, USA.
authors.forename: Karen J
authors.initials: KJ
authors.lastname: Marcus
authors.forename: Loukas G
authors.initials: LG
authors.lastname: Astrakas
authors.forename: David
authors.initials: D
authors.lastname: Zurakowski
authors.forename: Maria K
authors.initials: MK
authors.lastname: Zarifi
authors.forename: Dionyssios
authors.initials: D
authors.lastname: Mintzopoulos
authors.forename: Tina Young
authors.initials: TY
authors.lastname: Poussaint
authors.forename: Douglas C
authors.initials: DC
authors.lastname: Anthony
authors.forename: Umberto
authors.initials: U
authors.lastname: De Girolami
authors.forename: Peter McL
authors.initials: PM
authors.lastname: Black
authors.forename: Nancy J
authors.initials: NJ
authors.lastname: Tarbell
authors.forename: A Aria
authors.initials: AA
authors.lastname: Tzika
mesh_terms: D000293:Adolescent; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001932:Brain Neoplasms / Q000401:mortality* / Q000473:pathology; D016543:Central Nervous System Neoplasms / Q000401:mortality* / Q000473:pathology; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D016016:Proportional Hazards Models; D011522:Protons; D012044:Regression Analysis; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor; D011522:Protons