Brain tumors constitutes the second most common malignancy in children. Management of these tumors with surgical resection, radiation therapy and chemotherapy presents significant challenges, with cure rates lagging compared to other pediatric cancers. While the introduction of radiation therapy (RT) has significantly improved patient outcome, survivors are never the less prone to cognitive impairment and other radiation-induced side effects. Therefore early detection of treatment resistance and treatment side effects are important for treatment planning and patient prognosis. Monitoring of brain tumor’s response is commonly done using medical imaging techniques such as magnetic resonance (MR) and positron emission tomography (PET). In addition to the clinical value of providing information regarding tumor location, size, and metabolism, these images can also be further analyzed to extract quantitative imaging features which can provide additional information for tumor characterization that preserves the spatial and temporal heterogeneity of the tumor. In this work, texture analysis will be utilized to establish quantitative image features that will assist in understanding and predicting RT response of tumors and detection of radiation-induced normal tissue injury. Using preclinical models, quantitative image features will be mined from MR and PET scans in radioresponsive and radioresistant tumors to establish universal and tumor-specific imaging markers of treatment response. Furthermore we will establish imaging markers that will provide immediate readout of normal tissue injury and map out the long term changes caused by RT. The outcome of our research will provide clinicians with a toolset to predict, detect, and understand RT response in both tumor and normal tissue for the personalization of treatment for affected children.In this work, texture analysis will be utilized to establish quantitative image features that will assist in understanding and predicting RT response of tumors and detection of radiation-induced normal tissue injury. Using preclinical models, quantitative image features will be mined from MR and PET scans in radioresponsive and radioresistant tumors to establish universal and tumor-specific imaging markers of treatment response. Furthermore we will establish imaging markers that will provide immediate readout of normal tissue injury and map out the long term changes caused by RT. The outcome of our research will provide clinicians with a toolset to predict, detect, and understand RT response in both tumor and normal tissue for the personalization of treatment for affected children.