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Proceedings Paper

Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape: preliminary data
Author(s): Maciej A. Mazurowski; Nicholas M. Czarnek; Leslie M. Collins; Katherine B. Peters; Kal Clark
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Paper Abstract

Glioblastoma (GBM) is the most common primary brain tumor characterized by very poor survival. However, while some patients survive only a few months, some might live for multiple years. Accurate prognosis of survival and stratification of patients allows for making more personalized treatment decisions and moves treatment of GBM one step closer toward the paradigm of precision medicine. While some molecular biomarkers are being investigated, medical imaging remains significantly underutilized for prognostication in GBM. In this study, we investigated whether computer analysis of tumor shape can contribute toward accurate prognosis of outcomes. Specifically, we implemented applied computer algorithms to extract 5 shape features from magnetic resonance imaging (MRI) for 22 GBM patients. Then, we determined whether each one of the features can accurately distinguish between patients with good and poor outcomes. We found that that one of the 5 analyzed features showed prognostic value of survival. The prognostic feature describes how well the 3D tumor shape fills its minimum bounding ellipsoid. Specifically, for low values (less or equal than the median) the proportion of patients that survived more than a year was 27% while for high values (higher than median) the proportion of patients with survival of more than 1 year was 82%. The difference was statistically significant (p < 0.05) even though the number of patients analyzed in this pilot study was low. We concluded that computerized, 3D analysis of tumor shape in MRI may strongly contribute to accurate prognostication and stratification of patients for therapy in GBM.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852T (24 March 2016); doi: 10.1117/12.2217098
Show Author Affiliations
Maciej A. Mazurowski, Duke Univ. (United States)
Nicholas M. Czarnek, Duke Univ. (United States)
Leslie M. Collins, Duke Univ. (United States)
Katherine B. Peters, Duke Univ. (United States)
Kal Clark, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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