The use of advanced medical imaging can provide a non-invasive means for accurately assessing molecular subtypes overcoming the limitations of biopsies. Medical images can capture a full picture of tumor phenotypes and their environments throughout treatment with a low risk to patients. Therefore, the combination of radiomics; which refers to the extraction and storage of quantitative data from digital images with clinical data in a shared database, and radiogenomics; which correlates genomic and radiomic data is poised to play a critical role in the diagnosis of individual lesions. The goal of this project is to develop an innovative platform combining radiomics and radiogenomics for diagnosing and characterizing breast cancer tumors throughout therapy, creating an efficient and robust prognostic model to aid clinicians making personalized treatment decisions for cancer patients. (1) Develop radiogenomics model combining radiomic and genomic features capable of inferring breast cancer subtype, stage, and response criteria for tumors captured within MRI images. (2) Validate the model's diagnostic accuracy, utility for treatment planning and its generality across multiple sites and vendor platforms to show potential for widespread impact on personalized cancer treatment. (3) Develop intuitive user experience supporting the inspection and interpretation of model outputs in the context of source MRI images to support personalized treatment decisions.