ANGI-16. EARLY DETECTION OF TUMOR CELL PROLIFERATION IS ASSOCIATED WITH A UNIQUE RADIOMIC BIOMARKER IN PRECLINICAL GLIOBLASTOMA XENOGRAFT AND PATIENTS

PURPOSE: The mainstay imaging technique in brain tumor is Magnetic resonance imaging (MRI). However, early detection of tumor cell proliferation using MRI is limited due to inapparent disruption of normal brain architecture. Radiomics and machine learning techniques can quantitate thousands of imaging features that can depict neoplastic changes in apparently normal brain. Herein, we investigate the potential role radiomics can play in early detection of tumor cell proliferation in apparently normal MRI using a preclinically trained radiomic. METHODS: Two glioblastoma stem-like cell lines were transformed to stably express luciferase under a constitutive promoter. A stereotactic injection of tumor cells was performed to generate orthotopic mouse models (N=48). Tumor cell engraftment and in-vivo proliferation were assessed using bio-luminescence imaging (BLI) along with a weekly MRI (Bruker 7T). Images were analyzed, and ROIs were placed using 3D slicer software and radiomic features were extracted using Matlab. ROIs (0.75 mm) were placed on tumor injection sites and normal appearing contralateral brain. Radiomic features were compared for their significant alterations over time using comparative marker selection (CMS). Genomics and Histopathology of tumors were performed ex-vivo. Validation was performed in a cohort of brain cancer patients. RESULTS: Three stages of post-implantation tumor cell presence and proliferation were identified: 1. Immediate post implantation lag/engraftment phase. 2. Linear cellular proliferation phase (normal on conventional MRI). 3. Exponential cellular proliferation phase (apparent tumor on conventional MRI). Our data showed that 43% of extracted radiomic features were significantly changing (P Conclusion: Radiomic texture analysis and machine learning detects tumor cell presence and proliferation in normal-appearing brain prior to tumor development on conventional imaging. CLINICAL RELEVANCE: Radiomics and machine learning algorithms are predictive of tumor presence in seemingly normal MRIs. Early detection of tumors can allow earlier intervention, more extensive radiation planning and appropriately dose chemotherapeutic regimens.