A multimodal risk prediction approach for breast cancer using MRI and genomic data


Breast magnetic resonance imaging is a new technology that offers high sensitivity for diagnosis and treatment of breast cancer. In particular, breast MRI allows visual assessment of breast density, which relates strongly to breast cancer risk. The goal of this project is to develop new biomarkers for breast cancer using breast MR images concurrently with genomic data. Combining texture analysis methods to describe the appearance of breast images simultaneously with features derived from the genetic profiles of individuals, we will develop new methods for characterizing outcomes such as response to treatment and predicting future malignancy.

CS Collaborators: 

Charles Dyer

Campus Collaborators: 

Vikas Singh (Biostatistics & Medical Informatics), Beth Burnside (Radiology)