Developing a Combined Radiomic/Genomic Signature for Prediction of Survival in Glioblastoma

Purpose/Objective(s): Extraction of multiscale radiomic features from preoperative MRI scans provides an opportunity for quantitative, non-invasive, image-based phenotyping of glioblastoma (GBM). Upon obtaining tumor tissue, genomic sequencing can further enhance predictive value. This study aims to predict overall survival (OS) following gross total resection of primary glioblastoma using a combination of MRI radiomics, tumor genomics, and patient clinical factors. Materials/Methods: In this retrospective study, preoperative image data from 61 patients from the 2018 Multimodal Brain Tumor Image Segmentation (BraTS) Competition publicly-available dataset were used to generate a radiomic signature. A total of 968 radiomic features were extracted from three manually delineated structures (Enhancing Tumor, Tumor Core, and Tumor Edema) on the T1, T2, T1-contrast, and FLAIR sequences. The features associated with overall survival were selected using the univariate Cox Proportional Hazards (CPH) model (p-value\u3c0.05) and 10 cross-fold SVM recursive feature elimination (SVM-RFE). These features were then tested on a validation set of patients with GBM treated at our institution over five years (2013-2018). A total of 152 patients were identified, of which 20 had radiologically confirmed gross total resection on postoperative MRI as well as genomic sequencing information available. Institutional preoperative MRI images—T1, T2, T1-contrast, and FLAIR sequences—were manually contoured according to established BraTS benchmarks. Genomic features included expression levels of nine genes involved in neuroactive ligand-receptor interaction, cysteine metabolism, and ephrin A reverse signaling. Clinical variables included in the analysis were age, sex, tumor location, and Karnofsky Performance Score (KPS). A multivariate Cox proportional hazards analysis was performed to assess the association between OS and the radiomic, genomic, and clinical features. Results: Ten radiomic features were found to be statistically relevant with OS from a training set of 61 patients. Features associated with OS on the univariate analysis included age, tumor location, KPS, LDHA gene expression and EPHA5 gene expression. In the multivariate analysis, the features significantly associated with OS included tumor location (p=0.03, Hazard ratio (HR) = 1.09), LDHA (p\u3c0.005, HR=0.14), and two radiomics features from gray-level size-zone matrix: minimum value of large-zone-low-gray-level emphasis from T2 (p=0.04, HR=0.49) and kurtosis of small-zone-low-grey-level-emphasis from FLAIR (p=0.01, HR=2.02). Conclusion: A more robust validation set of patients is needed to draw more meaningful conclusions, but there exist potential imaging features that can provide clinical prognostic information. One aspect that necessitates further investigation is the failure of well-established clinical and genomics factors to reach significance in the present study