Novel Radiomic Features based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time.

This paper presents a novel set of image texture features, generalizing standard grey-level co-occurrence matrices (GLCM) to multi-modal image data through joint intensity matrices (JIMs). These are used to predict the survival of glioblastoma multiforme (GBM) patients from multi-modal MRI data. The scans of 73 GBM patients from the Cancer Imaging Archive are used in our study. Necrosis, active tumor and edema/invasion sub-regions of GBM phenotypes are segmented using the co-registration of contrast enhanced T1-weighted (CET1) images and its corresponding fluid-attenuated inversion recovery (FLAIR) images. Texture features are then computed from the JIM of these GBM sub-regions, and a random forest model employed to classify patients into short or long survival groups. Our survival analysis identified JIM features in necrotic (e.g., entropy and inverse-variance) and edema (e.g., entropy and contrast) sub-regions that are moderately correlated with survival time (i.e., Spearman rank correlation of 0.35). Moreover, 9 features were found to be associated with GBM survival, with a Hazard-ratio range of 0.382.1 and a significance level of p < 0.05 following Holm-Bonferroni correction. These features also led to the highest accuracy in a univariate analysis for predicting the survival group of patients, with AUC values in the range of 68- 70%. Considering multiple features for this task, JIM features led to significantly higher AUC values than those based on standard GLCMs and gene expression. Furthermore, an AUC of 77.56% with p=0.003 was achieved when combining JIM, GLCM and gene expression features into a single radiogenomic signature. In summary, our study demonstrated the usefulness of modeling the joint intensity characteristics of CE-T1 and FLAIR images for predicting the prognosis of patients with GBM.