Advancing Uterine Cancer Prevention: Overcoming Challenges in Clinical Studies and Enhancing Risk-Reduction Strategies

Details

We have demonstrated the feasibility and acceptability of a multi-phase, fully remote approach in British Columbia to identifying people at increased risk for endometrial cancer (EC) and connecting them to risk-reducing interventions. Our approach leverages a risk assessment model and the Progesterone Challenge Test (PCT) to identify individuals who may require further evaluation. Further validation is needed to determine these methods’ predictive value and utility in guiding preventive strategies.

As we move into the next phase, our goal is to assess the effectiveness of these approaches in reducing EC risk, address barriers to participation, optimize intervention strategies, and validate a surrogate marker for EC incidence. This talk will explore key lessons from recruitment, retention, and participant feedback while discussing potential study refinements, including the integration of novel pharmacologic interventions such as semaglutide inhibitors. By incorporating these insights, we aim to enhance long-term adherence to risk-reducing interventions and improve health outcomes for high-risk individuals.

Aline Talhouk, PhD

Assistant Professor OBGYN, UBC

Dr. Aline Talhouk is an assistant professor in the Department of Obstetrics and Gynecology in the Faculty of Medicine at the University of British Columbia (UBC). She is principal investigator at OVCARE, where she directs a data science and informatics laboratory. Dr. Talhouk holds a PhD from UBC and has expertise in computational statistics and machine learning, specifically in the development and implementation of predictive models in women’s health and oncology. Her research leverages statistical computing, machine learning and artificial intelligence to translate -omics discoveries to clinical applications and bring individualized care to ovarian and endometrial cancer patients. Dr. Talhouk has developed a nationally funded precision prevention program that uses prediction modeling to identify those at high risk for uterine cancer and direct them to risk-reducing interventions, targeted screening and surveillance.