Cancer Imaging

Program Information

The Cancer Imaging Program is a transdisciplinary program that integrates imaging and engineering research with the study of cancer biology. The goals of this program are to build novel imaging technologies for discerning mechanisms of cancer biology, designing new targeted therapies, and developing innovative imaging modalities for improving patient care. Our research spans the spectrum, from fundamental biomarker identification for molecular imaging to development of new diagnostic and theranostic probes and image acquisition and data processing methods.

Program Aims

Develop targeted small molecules and nanoparticles to examine fundamental biology, facilitate diagnosis and treatment of cancer

By leveraging basic discoveries in cancer biology, our program creates novel targeted agents across all imaging modalities. Investigators utilize these agents to study the pathobiology, develop approaches for early diagnosis and image-guided therapeutic (theranostic) interventions of a broad range of diseases including prostate, breast, ovarian, and pancreatic cancers, brain and liver tumors and melanoma. Many of these developments have proceeded into clinical trials.

Generate enabling quantitative technologies to transform cancer research, diagnosis and treatments

The CI program generates quantitative imaging methods, advances imaging hardware and develops computational tools and imaging analytics. A broad team of investigators across the center utilizes these technologies to improve clinical cancer diagnosis and prognosis. Members are also combining imaging and therapeutic approaches to develop novel tools for personalized medicine and therapy response evaluation. We further leverage our technology to inform clinical trials and advance clinical discoveries.

Create machine learning tools to improve cancer characterization, therapy, and to predict patient outcomes with imaging

Starting with radiomics, quantitative features (not obvious to human eyes) extracted from medical imaging of tumors are analyzed to train a model for tumor characterization, prognosis and treatment response. Parallel work on digital pathology further broadens the application of the technologies. These developments of imaging analytics are greatly enhanced through machine learning. 
 

Program Members