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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.

BACKGROUND: Radiomic texture analysis (TA) from standard MRI imaging may be able to discriminate between responders versus non-responders in glioblastoma patients treated with pembrolizumab immunotherapy. METHODS: 14 patients (5 males; mean age 58 years; range: 32–72 years), with pathologically-proven recurrent GBM, enrolled in a pembrolizumab clinical trial, were retrospectively evaluated. Immunotherapy Response Assessment in Neuro-Oncology(iRANO) were performed. Patients were categorized based on: 1) best response or 2) overall response (OR) using the iRANO status at the last scan time in the trial. Patients with progressive disease (PD) were classified as non-responders, while patients with partial response (PR) or stable disease (SD) were classified as responders. T2-FLAIR (edema/invasion) and post-contrast T1WI (enhancing tumor) of baseline scans were co-registered and segmented (3D Slicer, v.4.3.1) to create a volume of interest for Radiomic TA. A total of 4880 texture features were extracted. Feature selection was performed using Lasso regularization. For classification and predictive model building, gbtree booster of XGboost with Leave-One-Out Cross-Validation (LOOCV) was used on the selected texture features to build a binary logistic regression model and classify the patients into respective groups RESULTS: Using the best response classification, 10 patients were classified as non-responders and four patients classified as responders (1 SD; 3 PR). Using 13 radiomic features, these patients could be classified into their respective responding groups with a sensitivity, specificity and accuracy of 100%, p-value=0.0089. Based on OR, 12 patients were classified as non-responders and two as responders (2 SD). Seven features were able to differentiate the responding patients with a sensitivity, specificity and accuracy of 100%, p-value=0.0089. CONCLUSION: Radiomic TA was able to discriminate and predict those GBM patients that are responders versus non-responders to pembrolizumab with high robustness. Of note, given the small number of patients in this cohort, a larger cohort of patients is needed to minimize overfitting.

INTRODUCTION: Patterns of progression following bevacizumab (bev) treatment and associated outcomes remain poorly characterized. In patients (pts) with glioblastoma (GB) treated with bev, we describe radiographic patterns of progression and their association with outcome. METHODS: 64 pts treated at MD Anderson matched the predetermined inclusion criteria. Tumor progression after bev treatment was assessed according to the RANO criteria and pts categorized into groups based on previously published data: Group1:exclusively T2-diffuse hyperintense tumor (T2-diffuse), Group2:initial decrease and subsequent flare-up of contrast enhancement (CE) at progression (cT1 Flare-up), Group3:no decrease in CE or development of new lesions at first follow-up imaging (non- responders), Group4:exclusively T2-circumscribed hyperintense tumor progression (T2-circumscribed). In addition, we screened for new diffusion-restricted lesions or pre-contrast T1-hyperintense lesions or both (double-positive). RESULTS: Pts were categorized into Group1:11%, Group2: 33%, Group3: 45%, Group4: 11%. 16 pts had T1-hyperintense lesions and 37 had restricted diffusion;10 pts had double-positive lesions. There was no significant difference in time-to-initiation of bev treatment in the 4 groups. After starting bev, median OS and PFS (months) was Group1:8.6, 4.2 Group2:12.3,3.9 Group3:5.6,1.4 and Group4:7.0,3.2 respectively. Comparing non-responders vs the rest of the groups (responders), OS from initiation of bev was 5.6 vs 10 months (p =