Our expanding knowledge of the genetic basis and molecular mechanisms of cancer is beginning to revolutionize the practice of clinical oncology. Increasingly, molecular biomarkers of prognosis and treatment response are being used to classify tumors and direct treatment decisions. Advanced medical imaging platforms such as MRI, PET, and CT provide incredibly detailed images of tumors that reflect their structure, biochemistry, physiology and perhaps genetics. Studies by the Imaging Informatics Lab at the University of Calgary, and others, show that information about a tumors molecular phenotype can be obtained by using novel algorithms and computational tools to more fully analyze tumor images. Such virtual biopsies, performed by applying these image-processing and machine learning techniques to routine diagnostic images (e.g. MRI, PET or CT), could be a rapid and powerful means of assaying important cancer biomarkers. If successfully validated, and proven to have suitable sensitivity and specificity, the use of non-invasive

imaging-based molecular diagnostic tests would offer significant advantages over conventional surgical biopsies. For example, this could be important in the context of large heterogeneous tumors, multiple metastases, surgically inaccessible tumors, and settings where disease progression needs to be monitored frequently over time. Virtual biopsy research lies at the intersection of molecular imaging, medical imaging physics, and biocomputation, and is highly complementary to these areas. This presentation will cover key enabling technologies behind virtual biopsies, and discuss some recent progress in this research.