NIMG-67COMPUTER EXTRACTED TEXTURE DESCRIPTORS FROM DIFFERENT TISSUE COMPARTMENTS WITHIN THE TUMOR HABITAT ON TREATMENT-NAÏVE MRI PREDICT CLINICAL SURVIVAL IN GLIOBLASTOMA PATIENTS

Abstract

PURPOSE: Glioblastoma Multiforme (GBM) is a highly aggressive and heterogeneous brain tumor. Currently all GBM patients are given “one-dose-fit-all” treatment. Identification of prognostic markers can allow for personalized therapy options for GBM. The underlying hypothesis of this study is that the heterogeneity in GBM (due to subtle variations in tumor enhancement, cellular density, necrosis that are not visually appreciable on volumetric analysis of MRI) is prognostic and can be captured using computerized texture descriptors extracted from within different tumor compartments (enhancing tumor, necrotic core, edema). These compartments together define tumor “habitat”, and computerized texture features from within the habitat can be predictive of short-term (STS) ( 24-months). METHODS: A total of 58 3Tesla MRI studies (27 STS, 35 LTS) with GdT1C, FLAIR, and T2w protocols were obtained from the TCIA repository. Enhancing tumor, and necrotic regions on Gd-T1 and edematous region jointly on T2w and FLAIR, were segmented by an expert using Slicer 3D. 135 2-D texture descriptors, and volumetric measurements were extracted on a per-voxel basis from each of the 3 sub-regions (tumor, necrosis, edema) on every MRI protocol. Feature selection was used to identify most discriminative features with a random forest classifier trained via 3-fold cross validation. Kaplan-Meier (KM) survival curves were used for analysis, with correction for multiple hypothesis testing to identify features that were significantly (p < 0.05) correlated with survival. RESULTS: Contributions from top 15 texture features within the tumor habitat, (edema, necrotic core, enhanced tumor), were together most significantly associated with survival, across Gd-T1-C (p = 0.003), FLAIR (p = 0.006), and T2w (p = 0.04) as compared to individual features, and volumetric measurements from the tumor habitat. CONCLUSION: Computerized texture features when jointly interrogated across compartments within the tumor habitat appear more prognostic of clinical survival in GBMs than features from enhancing tumor and tumor volume alone.