28 February 2013 Survival time prediction of patients with glioblastoma multiforme tumors using spatial distance measurement
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Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86702O (2013) https://doi.org/10.1117/12.2007699
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Regional variations in tumor blood flow and necrosis are commonly observed in cross sectional imaging of clinical cancers. We hypothesize that radiologically-defined regional variations in tumor characteristics can be used to define distinct “habitats” that reflect the underlying evolutionary dynamics. Here we present an experimental framework to extract spatially-explicit variations in tumor features (habitats) from multiple MRI sequences performed on patients with Glioblastoma Multiforme (GBM). The MRI sequences consist of post gadolinium T1-weighted, FLAIR, and T2-weighted images from The Cancer Genome Atlas (TCGA). Our strategy is to identify spatially distinct, radiologically-defined intratumoral habitats by characterizing each small tumor regions based on their combined properties in 3 different MRI sequences. Initial tumor identification was performed by manually drawing a mask on a T1-weighted post contrast image slice. The extracted tumor was segmented into an enhancing and non-enhancing region by the Otsu segmentation algorithm, followed by a mask mapping procedure onto the corresponding FLAIR and T2-weighted images. Then Otsu was applied on the FLAIR and T2 images separately. We find that tumor heterogeneity measured through Distance Features (DF) can be used as a strong predictor of survival time. In an initial cohort of 16 cases slow progressing tumors have lower DF values (are less heterogeneous) compared to those with fast progression and short survival times.
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Mu Zhou, Lawrence O. Hall, Dmitry B. Goldgof, Robert J. Gillies, Robert A. Gatenby, "Survival time prediction of patients with glioblastoma multiforme tumors using spatial distance measurement", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86702O (28 February 2013); doi: 10.1117/12.2007699; https://doi.org/10.1117/12.2007699
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