Speaker: Aysenur Karagoz, Bilkent University
Title: Temporally Feathered Radiation Therapy Planning
Date: 26th September 2025, Friday
Time: 13:30
Place : EA 409
Abstract:
Intensity-modulated radiation therapy (IMRT) remains the standard-of-care radiation therapy technique for most head-and-neck cancer (HNC) patients. Despite the relatively high survival rate of HNC (65-75%), the survivors often suffer from post-treatment long-lasting side effects, which negatively affect their quality of life. In an IMRT-based treatment plan, the daily doses delivered to the tumor and organs-at-risk (OARs) remain fixed based on the premise that the one-day inter-fraction suffices for each OAR’s recovery. However, a novel treatment planning approach, referred to as temporally feathered radiation therapy (TFRT), proposes variable daily dose delivery to the OARs, aiming at increasing the recovery time of OARs at the cost of receiving higher doses once a week. While initial implementations of TFRT have shown promising results in the clinic, they do not distinguish between the inherent radiobiological differences among OARs for HNC patients, thus prescribing a longer, yet uniform recovery time for all OARs. By considering variable radiosensitivity profiles across multiple OARs for HNC radiation therapy, we develop a mixed-integer nonlinear program (MINLP) that computes specific recovery time for each OAR to minimize the toxicity burden to the patients. Our proposed MINLP leverages the linear-quadratic model, which predicts each OAR’s survival from radiation-induced damage. Our model is further flexible in addressing specific prioritization of OARs based on the clinician’s preferences. The result of our model indicates a lower overall
toxicity burden when OARs with higher radiosensitivity have longer recovery times under the TFRT framework.
Bio:
Aysenur Karagoz is an Assistant Professor in the Industrial Engineering Department at Bilkent University. She obtained her PhD from the Department of Computational Applied Mathematics and Operations Research at Rice University, with a joint appointment at MD Anderson Cancer Center. Her interdisciplinary research specializes in stochastic optimization methodologies applied to healthcare, particularly in cancer treatment planning. Her expertise includes polyhedral theory for stochastic mixed-integer programs and scenario tree design. She earned her BS and MS from Bilkent University.