Exploring Brain Tumor Heterogeneity for Survival Time Prediction

Brain tumor heterogeneity is well recognized in clinical MRI imaging and it is a challenging problem to quantitatively explore the underlying variations. It is known that brain tumors in different patients can have remarkably diverse visual appearances. In this paper, we propose a novel concept to categorize brain tumors with emphasis on spatial "habitats": a tumor can be quantified into distinctive sub-regions where the potential dynamics of tumor evolution may be evident. Our work is aimed at discovering spatially distinctive habitats within the tumor region, which may be useful in clinical practice for image-guided therapy. In particular, the heterogeneity can be well captured by two main steps: (a) intra-tumor segmentation, (b) spatial mapping scheme from a multi-modality MRI imaging dataset (Tl-weighted, FLAIR and T2-weighted MRI slices). A tumor region is initially segmented into high and low signal groups and then a joint mapping scheme is used to consider the correlation between different input modalities. In addition, focusing on signal contrast, we propose a set of quantitative features to measure differences between sub-regions. We further examined the application of survival time prediction for patients with malignant Glioblastoma multiforme (GBM). Experimental results showed that these features enabled classifiers to predict survival groups.