Nanoparticles have been investigated for use as contrast agents in a variety of disease settings. Unlike conventional small molecule contrast agents that exhibit short blood half-life and rapid wash-in/wash-out kinetics in pathological tissues, nanoparticle contrast agents exhibit long blood circulation times and often heterogenous uptake and distribution in pathological tissues yielding contrast patterns that are influenced by tissue architecture and tissue composition. In this talk, we will review novel approaches for data mining of nanoparticle contrast-enhanced images. This process, termed ‘nano-radiomics’, data mines in vivo medical images acquired using a nanoparticle contrast agent to extract quantitative ‘features’ about disease phenotype using data characterization algorithms augmented with machine learning techniques. We will review the applications of nano-radiomics for disease phenotyping that is otherwise not feasible using imaging-based conventional quantitative metrics.

Learning objectives:

• Understanding uptake and distribution of nanoparticle contrast agents in pathological tissues
• Quantitative approaches to data mining nanoparticle contrast-enhanced images using nano-radiomics
• Application of nano-radiomics for interrogation of disease phenotype

Presenter Biography:
Dr. Ketan Ghaghada is Associate Professor of Radiology at Texas Children’s Hospital and Baylor College of Medicine. Dr. Ghaghada’s research interests are in translational nanomedicine with a specific emphasis on the development of novel nanoparticle imaging agents, quantitative imaging methodologies and nano-therapeutics.

Author