Abstract 1392: Machine Learning Radiomic Biomarkers Non-invasively Assess Genetic Characteristics of Glioma Patients
Authors: rathore saima , Bakas Spyridon , Akbari Hamed , Nasrallah MacLean P. , Bagley Stephen , Davatzikos Christos 
Published: August 2019

Abstract
Genetic heterogeneity of gliomas, both across and within patients, is a significant challenge in precision diagnostics and treatment planning. The emerging field of radiogenomics focuses on imaging signatures reflecting underlying genomic characteristics. In this study, we use advanced radiomic analysis of clinically acquired multi-parametric MRI (mpMRI) sequences to non-invasively detect clinically-relevant genetic alterations in gliomas, including isocitrate dehydrogenase (IDH1), epidermal growth factor receptor variant-III (EGFRvIII), and promoter methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene. Such non-invasive, in vivo determination of tumor genetic alterations could assist in assessing spatial heterogeneity (currently not captured via single-specimen analyses), as well as prognostic stratification, and treatment planning. We delineated all tumors, from a retrospective cohort of 202 glioma patients with available pre-operative mpMRI (T1, T1-Gd, T2, T2-FLAIR, DTI, DSC-MRI), into sub-regions of enhancement, non-enhancement, and peritumoral edema/invasion. We used CaPTk (www.cbica.upenn.edu/captk) to extract quantitative imaging phenomic (QIP) features across sub-regions from all mpMRI, describing size, morphology, texture, and intensity. Cross-validated (CV) sequential feature selection determined the most discriminative QIP features. The predicted classifications, following a 10-fold CV, were compared with the results of a next-generation sequencing panel performed on specimens from the patients in our cohort, as well as pyrosequencing for MGMT. The CV accuracy of the radiomic assessment was 85% (spec = 86%, sens = 83%, area under the curve [AUC] = 0.85), 87% (spec = 90%, sens = 79%, AUC = 0.86), and 83% (spec = 85%, sens = 83%, AUC = 0.84) for mutations in IDH1, EGFRvIII, and MGMT methylation, respectively. These signatures were consistent with EGFRvIII-mutated, IDH1-wildtype, and MGMT-methylated tumors having increased neovascularization and cell density, as well as a distinctive spatial pattern involving relatively more frontal and parietal regions in EGFRvIII-mutants, more frontal regions in IDH1-mutants, and relatively right spherical regions in MGMT-methylated tumors. These detections were independent of age, gender, and additional genetic changes. By non-invasively capturing the tumor in its entirety, our proposed QIP signatures can assist in evaluating the tumor’s spatial heterogeneity, hence overcoming common sampling limitations of tissue-based analyses. These signatures can non-invasively stratify patients for therapies targeting specific genes, and potentially monitor the dynamic mutational changes during treatment. Given the routine use of imaging in clinical practice, our QIP signatures may provide an unprecedented opportunity to improve decision-support in cancer treatment at low cost.
Citation Format: saima rathore, Spyridon Bakas, Hamed Akbari, MacLean P. Nasrallah, Stephen Bagley, Christos Davatzikos. Machine Learning Radiomic Biomarkers Non-invasively Assess Genetic Characteristics of Glioma Patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1392.