Bio-Inspired Strategies for Optimizing Radiation Therapy under Uncertainties

Abstract

Radiation therapy is a critical component of cancer treatment. However, the delivery of radiation poses inherent challenges, particularly in minimizing radiation exposure to healthy organs surrounding the tumor site. One significant contributing factor to this challenge is the patient's respiration, which introduces uncertainties in the precise targeting of radiation. Managing these uncertainties during radiotherapy is essential to ensure effective tumor treatment while minimizing the adverse effects on healthy tissues. This research addresses the crucial objective of achieving a balanced dose distribution during radiation therapy under conditions of respiration uncertainty. To tackle this issue, we begin by developing a motion uncertainty model employing probability density functions that characterize breathing motion patterns. This model forms the foundation for our efforts to optimize radiation dose delivery. Next, we employ three bio-inspired optimization techniques: Cuckoo search optimization (CSO), flower pollination algorithm (FPA), and bat search Optimization (BSO). Our research evaluates the dose distribution in Gy on both the tumor and healthy organs by applying these bio-inspired optimization methods to identify the most effective approach. This research ultimately aids in refining the strategies used in radiation therapy planning under the challenging conditions posed by respiration uncertainty. Through the application of bio-inspired optimization techniques and a comprehensive evaluation of dose distribution, we seek to improve the precision and safety of radiation therapy, thereby advancing cancer treatment outcomes