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Our group develops novel medical image analytics (radiomics) and machine learning tools for disease diagnosis, intervention, and treatment evaluation. Uniquely, we attempt to integrate information across multiple length scales of Big Data by spatially resolving and cross-linking imaging (macro-scale) with molecular and pathology (micro-, nano- scales) data. As a result, we can obtain a more comprehensive assessment of disease in vivo, as opposed to analyzing any one data stream in isolation.
 
Applications of our tools are being examined in: (a) decision support for treatment (e.g. choice of therapy), (b) targeting therapeutic procedures (e.g. guiding ablation, radiotherapy, surgery), and (c) biological quantitation for treatment response characterization in vivo. This multi-disciplinary, multi-pronged approach is being applied to colorectal, renal, and prostate cancers, as well as digestive diseases; with a focus on near-term clinical impact and translation.