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Sheldon Greenfield, MD, Executive Co-Director, Health Policy Research Institute, School of Medicine, UC Irvine
Prostate cancer is the most common cancer in men, with over 200,000 new cases diagnosed each year in the U.S. It is the second leading cause of cancer death in men, and affects roughly 1 in 7 men over their lifetime.
An important goal toward achieving better outcomes is to be able to predict, prior to treatment, which therapy will work best for each patient.
This proposal focuses on improving these predictions for patients with early stage prostate cancer, based on diverse information, including (i) detailed patient characteristics and patient reported outcomes such as socio-demographic information, health status and disease management burden, (ii) traditional prostate cancer severity indicators and (iii) an already established genomic test that measures the probability of cancer spread after surgery.
Another important objective of this project is to understand the validity of these predictive measures in an ethnically diverse patient population.
The final combined prediction model will aid doctors and patients in personalizing prostate cancer treatment decisions to maximize effectiveness, and choose the treatment optimal for individual patients.
To potentially improve the predictive power of their prediction model, the team will collect additional data from each patient. They will add a 6-month time point to supplement the 12-month time point collecting detailed patient characteristics, and they will include a second genomic test from Ambry genetics to assess an inherited predisposition to prostate cancer