• World Neurosurg · Jan 2018

    Histopathological Features in Relation to Pretreatment Growth in Glioblastoma Patients.

    • Vilde Elisabeth Mikkelsen, Anne Line Stensjøen, Erik Magnus Berntsen, Ivar Skjåk Nordrum, Øyvind Salvesen, Ole Solheim, and Sverre Helge Torp.
    • Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU-Norwegian University of Science and Technology, Trondheim, Norway. Electronic address: vildeem@stud.ntnu.no.
    • World Neurosurg. 2018 Jan 1; 109: e50-e58.

    BackgroundRapid growth is a well-known property of glioblastoma (GBM); however, growth rates vary among patients. Mechanisms behind such variation have not been widely studied in human patients. We sought to investigate relationships between histopathologic features and tumor growth estimated from pretreatment magnetic resonance imaging scans.MethodsIn 106 patients with GBM, 2 preoperative T1-weighted magnetic resonance imaging scans obtained at least 14 days apart were segmented to assess tumor growth. A fitted Gompertzian growth curve based on the segmented volumes divided the tumors into 2 groups: faster and slower growth than expected based on the initial tumor volume. Histopathologic features were investigated for associations with these groups, using univariable and multivariable logistic regression analyses.ResultsThe presence of high cellular density and thromboses was significantly associated with radiologic growth in the multivariable analysis (P = 0.018 and 0.019, respectively), with respective odds ratios of 3.0 (95% confidence interval, 1.2-7.4) and 4.3 (95% confidence interval, 1.3-14.5) for faster growing tumors.ConclusionsOur findings show that high cellular density and thromboses are significant independent predictors of faster growth in human GBM. This finding underlines the importance of hypercellularity as a criterion in glioma grading. Furthermore, our findings are concordant with hypotheses suggesting hypoxia triggered by thromboses to be relevant for growth of GBM.Copyright © 2017 Elsevier Inc. All rights reserved.

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