Research Research Projects

Radiomics and radiogenomics

Radiomics is a growing field of medical image analysis where radiological images are quantitatively analyzed to identify signatures  used to diagnose disease and predict patient outcomes. Radiogenomics analysis correlates radiomic signatures with the results of genomic analyses of specimens from the same patients. Our work is focused on cancer radiomics, with emphasis on tumor heterogeneity. We are using statistical machine learning tools to develop new measures of heterogeneity, which take into account the topology of tumors. This research analyzes imaging studies from trials conducted by the American College of Radiology Imaging Network and combines imaging and clinical data to predict the course of disease and patient response to therapy.

Research Leads: 

Ani Eloyan, Assistant Professor of Biostatistics
Zhijin Wu, Associate Professor of Biostatistics
Constantine Gatsonis, Professor of Biostatistics

Funding Sources: 

NIH R21 NS093349, U10 CA180794, U01 CA190254