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. In an effort to improve treatment outcomes, this project focused on improving the ability to predict–prior to treatment-which therapy would work best for each early-stage prostate cancer patient. To predict outcomes, researchers used diverse information, including (1) detailed patient characteristics and patient reported outcomes such as socio-demographic information, health status and disease management burden, (2) traditional prostate cancer severity indicators, and (3) an existing genomic test that measures the probability of cancer spread after surgery.

Another important objective of this project was to understand the accuracy of these predictive measures in an ethnically diverse patient population.

The final combined prediction model could aid doctors and patients in personalizing prostate cancer treatment decisions by choosing the most optimal treatment for a particular patient.

Supplemental Research

With supplemental funding from CIAPM, the team worked to improve the predictive power of its model. The team collected additional data from each patient and extended the study period by six months. They also included a second genomic test from Ambry Genetics to assess an inherited predisposition to prostate cancer.

Research Team and Collaborators

Research Team

  • University of California, Irvine
    • Sheldon Greenfield, MD
    • Sherrie H. Kaplan, PhD, MPH
    • Edward Uchio, MD, FACS, CPI
    • Hal Stern, PhD
  • Vanderbilt University Medical Center
    • David Penson, MD, MPH


  • University of California, Los Angeles
    • Mark S. Litwin, MD, MPH
  • Veterans Affairs Los Angeles
    • Isla Garraway, MD, PhD
  • Cedars Sinai Medical Center
    • Timothy Daskivich, MD, MSHPM
  • Veterans Affairs Long Beach Healthcare