Association of Graph-based Spatial Features with Overall Survival Status of Glioblastoma Patients

Background and purpose: Glioblastoma multiforme (GBM) is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics from habitats and propose two graph-based methods (minimum spanning tree and graph run-length matrix) to characterize spatial heterogeneity over tumor MRI-derived intensity habitats and assess their relationships with overall survival as well as immune signature status of patients with GBM. Material and methods: A data set of 74 patients was studied based on the availability of post-contrast T1-weighted and T2-weighted fluid attenuated inversion recovery (FLAIR) image data in The Cancer Image Atlas (TCIA). We assessed the predictive value of MST- and GRLM-derived features from 2D images for prediction of 12-month survival status and immune signature status of patients with GBM via a receiver operating characteristic curve analysis. Results: For 12-month survival prediction using MST-based method, sensitivity and specificity were 0.82 and 0.79 respectively. For GRLM-based method, sensitivity and specificity were 0.73 and 0.77 respectively. For immune status, sensitivity and specificity were 0.91 and 0.69, respectively, for the GRLM-based method with an immune effector. Conclusion: Our results show that the proposed MST- and GRLM-derived features are predictive of 12-month survival status as well as the immune signature status of patients with GBM. To our knowledge, this is the first application of MST- and GRLM-based proximity analyses for the study of radiologically-defined tumor habitats in GBM.