In this interview, Bashir Al Hussein Al Awamlh, MD, MPH, a urologic oncologist and assistant professor of urology at Weill Cornell Medicine and NewYork-Presbyterian Hospital in New York, New York, highlights the challenges patients with localized prostate cancer face when choosing among treatment options such as active surveillance, radical prostatectomy, or radiation therapy. Although oncologic outcomes are often comparable, differences in functional outcomes—such as sexual, urinary, and bowel function—make shared decision-making critical. Al Awamlh notes that existing prediction tools are limited: They rely on outdated treatment data, offer only short-term predictions, and present results as continuous scores that are difficult for patients to interpret.
To address these shortcomings, his team leveraged data from the 10-year prospective CEASAR study, which collected patient-reported outcomes on contemporary prostate cancer treatments.1 Using advanced statistical methods, particularly random forest modeling, they developed predictive models that generate patient-centered, individualized outcomes. Instead of abstract scores, the tool provides probabilities for specific functional outcomes, such as the likelihood of maintaining erections sufficient for intercourse, urinary continence, or experiencing bowel symptoms. This approach makes the results more relatable and useful for patients.
The tool is designed as a patient-facing application that can be used both during clinic visits and independently at home. By allowing patients to input readily available clinical and functional baseline information, the model predicts likely outcomes over a 10-year horizon, supporting informed decisions that align with personal values and reducing treatment regret.
Looking ahead, Al Awamlh’s team plans to transform the prototype into a more sophisticated, mobile-friendly web application. Future development will focus on making the tool “health literacy agnostic,” ensuring accessibility for patients from diverse backgrounds. Ultimately, the goal is to integrate the tool into routine clinical practice nationwide, enhancing patient engagement and shared decision-making in prostate cancer care.
REFERENCE
1. Al Awamlh BAH, Zhao Z, Huang L-C, Koyama T, Barocas DA. Development of predictive models using patient-reported data for integration into a patient-facing functional outcomes prediction tool for localized prostate cancer treatments. J Urol. 2025;213(5S2):e257. doi:10.1097/01.JU.0001109792.00985.9d.12