A computer model that uses medical images to reproduce the growth patterns of cancer on a patient’s prostate could lead to a more accurate system for early detection, diagnosis and treatment.

“There is a lot of room for improvement in both the diagnosis and management of prostate cancer,” says Michael Scott, professor of civil and environmental engineering at Brigham Young University (BYU) and co-author of a study detailing the modeling system that has been published in Proceedings of the National Academy of Sciences. “We’re using computer modeling to capture the behavior of prostate tumor growth, which will hopefully lead to minimally invasive predictive procedures that can be used in clinical practice.”

Michael Scott, professor of civil and environmental engineering at BYU. Image credit: Aaron Cornia/BYU.Current diagnosis methods include invasive biopsy procedures that often lead to patients being over-treated or under-treated. Complicating matters is the fact that prostate cancer can remain undiagnosed because early stages of the disease may not produce symptoms until a tumor is either very large or has invaded other tissues.

Scott and BYU Assistant Professor of Information Technology and Services Kevin Tew teamed up with colleagues at the University of Coruna, the University of Texas, Austin and Carnegie Mellon University to propose a mathematical model for the growth of prostate cancer that includes an equation for its reference biomarker: the prostate-specific antigen. The model reproduces features of prostatic tumor growth observed in experiments and clinical practice and captures a known shift in the growth pattern of prostate cancer—from spheroidal to fingered geometry. Their research indicates that this shape instability is a tumor response to escape starvation, hypoxia and eventually necrosis.

Scott says the research is still in its infancy and that validation and refinement of the model must occur before it is ready for clinical application. Nevertheless, “it’s likely that these types of models will eventually turn up in medical practice,” he adds. “We are entering an age where we will see the emergence of tools that leverage computation to improve diagnosis of disease."