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Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study

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Abstract

In this retrospective, IRB-exempt study, we analyzed data from 68 patients diagnosed with glioblastoma (GBM) in two institutions and investigated the relationship between tumor shape, quantified using algorithmic analysis of magnetic resonance images, and survival. Each patient’s Fluid Attenuated Inversion Recovery (FLAIR) abnormality and enhancing tumor were manually delineated, and tumor shape was analyzed by automatic computer algorithms. Five features were automatically extracted from the images to quantify the extent of irregularity in tumor shape in two and three dimensions. Univariate Cox proportional hazard regression analysis was performed to determine how prognostic each feature was of survival. Kaplan Meier analysis was performed to illustrate the prognostic value of each feature. To determine whether the proposed quantitative shape features have additional prognostic value compared with standard clinical features, we controlled for tumor volume, patient age, and Karnofsky Performance Score (KPS). The FLAIR-based bounding ellipsoid volume ratio (BEVR), a 3D complexity measure, was strongly prognostic of survival, with a hazard ratio of 0.36 (95% CI 0.20–0.65), and remained significant in regression analysis after controlling for other clinical factors (P = 0.0061). Three enhancing-tumor based shape features were prognostic of survival independently of clinical factors: BEVR (P = 0.0008), margin fluctuation (P = 0.0013), and angular standard deviation (P = 0.0078). Algorithmically assessed tumor shape is statistically significantly prognostic of survival for patients with GBM independently of patient age, KPS, and tumor volume. This shows promise for extending the utility of MR imaging in treatment of GBM patients.

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Funding

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1106401. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of the National Science Foundation.

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Authors and Affiliations

  1. Electrical & Computer Engineering, Duke University, Box 90291, Durham, NC, 27708, USA

    Nicholas Czarnek & Maciej A. Mazurowski

  2. Department of Radiology, Duke University, 2424 Erwin Road (Hock Plaza), Suite 302, Durham, NC, 27705, USA

    Kal Clark & Maciej A. Mazurowski

  3. Department of Neurology, Duke University Medical Center, 3624, Durham, NC, 27710, USA

    Katherine B. Peters

  4. Applied Machine Learning Lab, Duke University, Box 90291, 129 Hudson Hall, Durham, NC, 27708, USA

    Nicholas Czarnek

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Correspondence to Nicholas Czarnek.

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Conflict of interest

Katherine B. Peters has served on advisory boards for Agios and Novocure and received research funding from Agios, AMGEN, BioMimetix, Eisai, Genentech, Merck, and VBL. None of the remaining authors have conflicts of interest to disclose.

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Czarnek, N., Clark, K., Peters, K.B. et al. Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study. J Neurooncol 132, 55–62 (2017). https://doi.org/10.1007/s11060-016-2359-7

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  • DOI: https://doi.org/10.1007/s11060-016-2359-7

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