Paper
24 March 2016 Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype
Nicholas M. Czarnek, Kal Clark, Katherine B. Peters, Leslie M. Collins, Maciej A. Mazurowski
Author Affiliations +
Abstract
Genomic subtype has been shown to be an important predictor of therapy response for patients with glioblastomas. Unfortunately, obtaining the genomic subtype is an expensive process that is not typically included in the standard of care. It is therefore of interest to investigate potential surrogates of molecular subtypes that use standard diagnostic data such as magnetic resonance (MR) imaging. In this study, we analyze the relationship between tumor genomic subtypes, proposed by Verhaak et al, 2010, and novel features that capture the shape of abnormalities as seen in fluid attenuated inversion recovery (FLAIR) MR images. In our study, we used data from 54 patients with glioblastomas from four institutions provided by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). We explore five shape features calculated by computer algorithms implemented in our laboratory that assess shape both in individual slices and in rendered three-dimensional tumor volumes. The association between each feature and molecular subtype was assessed using area under the receiver operating characteristic curve analysis. We show that the two dimensional measures of edge complexity are significant discriminators between mesenchymal and classical tumors. These preliminary findings show promise for an imaging-based surrogate of molecular subtype and contribute to the understanding of the relationship between tumor biology and its radiology phenotype.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nicholas M. Czarnek, Kal Clark, Katherine B. Peters, Leslie M. Collins, and Maciej A. Mazurowski "Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850V (24 March 2016); https://doi.org/10.1117/12.2217084
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Cited by 6 scholarly publications.
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KEYWORDS
Tumors

Magnetic resonance imaging

Cancer

Shape analysis

Feature extraction

Receivers

Data archive systems

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