Decoding brain cancer dynamics: a quantitative histogram-based approach using temporal MRI

Mu Zhou; Lawrence O. Hall; Dmitry B. Goldgof; Robin Russo; Robert J. Gillies; Robert A. Gatenby

doi:10.1117/12.2075545

20 March 2015 Decoding brain cancer dynamics: a quantitative histogram-based approach using temporal MRI

Mu Zhou, Lawrence O. Hall, Dmitry B. Goldgof, Robin Russo, Robert J. Gillies, Robert A. Gatenby

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94142H (2015) https://doi.org/10.1117/12.2075545
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Brain tumor heterogeneity remains a challenge for probing brain cancer evolutionary dynamics. In light of evolution, it is a priority to inspect the cancer system from a time-domain perspective since it explicitly tracks the dynamics of cancer variations. In this paper, we study the problem of exploring brain tumor heterogeneity from temporal clinical magnetic resonance imaging (MRI) data. Our goal is to discover evidence-based knowledge from such temporal imaging data, where multiple clinical MRI scans from Glioblastoma multiforme (GBM) patients are generated during therapy. In particular, we propose a quantitative histogram-based approach that builds a prediction model to measure the difference in histograms obtained from pre- and post-treatment. The study could significantly assist radiologists by providing a metric to identify distinctive patterns within each tumor, which is crucial for the goal of providing patient-specific treatments. We examine the proposed approach for a practical application - clinical survival group prediction. Experimental results show that our approach achieved 90.91% accuracy.

Citation Download Citation

Mu Zhou, Lawrence O. Hall, Dmitry B. Goldgof, Robin Russo, Robert J. Gillies, and Robert A. Gatenby "Decoding brain cancer dynamics: a quantitative histogram-based approach using temporal MRI", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142H (20 March 2015); https://doi.org/10.1117/12.2075545

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