title: Lower apparent diffusion coefficients indicate distinct prognosis in low-grade and high-grade glioma.
abstract: Tumor grade and molecular variants influence the survival of patients with glioma. The apparent diffusion coefficient (ADC) map is a non-invasive tool for evaluating the outcomes and response to therapy in glioma. In this study, we investigated the correlation between the tumor grade and prognostic biomarkers with the ADC in glioma patients. Eighty-two patients with supratentorial glioma were identified via analysis of surgical specimens and neuroradiological data. Using the World Health Organization grade, histological subtype, and molecular variants (1p/19q codeletion, isocitrate dehydrogenase 1/2 mutation, Ki-67 index, O6-methylguanine DNA methyltransferase, P53, and vascular endothelial growth factor immunoactivity) as prognostic biomarkers, we performed receiver operating characteristic analysis and multiple linear regression to assess the association between the magnetic resonance diffusion parameter and mean ADC and the prognostic factors of glioma pathology. Univariate analysis and multiple linear regression revealed inverse correlations between the ADC values and the tumor grade, oligodendrocytoma histology, and 1p/19q codeletion. A threshold mean ADC value could predict the 1p/19q chromosomal status in WHO II gliomas with 72 % sensitivity and 88 % specificity (area under the curve 0.82, 95 % confidence interval 0.68-0.97) and could distinguish low-grade glioma with low-risk factors from the high-risk group (P < 0.01). The mean ADC value could be used as a non-invasive tool to evaluate the prognosis of supratentorial glioma patients. A threshold mean ADC value could be used to predict the 1p/19q codeletion and to identify low-risk low-grade gliomas (LGGs). Lower ADC values are indicative of a favorable prognosis in LGGs.
authors.affiliation: Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, People's Republic of China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Cui
authors.forename: Li
authors.initials: L
authors.lastname: Ma
authors.forename: Xuzhu
authors.initials: X
authors.lastname: Chen
authors.forename: Zhe
authors.initials: Z
authors.lastname: Zhang
authors.forename: Haihui
authors.initials: H
authors.lastname: Jiang
authors.forename: Song
authors.initials: S
authors.lastname: Lin
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000378:metabolism; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism / Q000473:pathology*; D005260:Female; D005910:Glioma / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism / Q000473:pathology*; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D011379:Prognosis; D012372:ROC Curve; D012680:Sensitivity and Specificity; D055815:Young Adult
chemical_list: D015415:Biomarkers