Work

Fast automatic radiotherapy planning via algorithmic improvements and computational acceleration

Year: 2025

Type: article

Abstract: Intensity-Modulated Radiation Therapy enhances dose delivery by dynamically adjusting beam intensities to target tumorous tissues while preserving healthy organs. One of the most effective planning approaches uses the Generalized Equivalent Uniform Dose metric, which ensures high-quality treatment plans but requires tuning several hyperparameters for each anatomical structure. Traditionally, this process is performed manually by clinical experts, making it time-consuming and dependent on human expertise. To address these challenges, a previous method combined multi-objective evolutionary search with gradient-based optimization to automate the tuning process. However, this hybrid strategy incurs high computational cost, as each candidate solution must undergo a complete gradient-based optimization step, repeated thousands of times throughout the process. This study introduces two complementary strategies to improve the efficiency of this framework. First, we analyze alternative multi-objective evolutionary algorithms that converge more rapidly, thereby reducing the number of required function evaluations, and we compare three gradient-based optimization methods to identify the one that accelerates convergence without compromising plan quality. Second, we implement a parallel computing framework that distributes the function evaluations across heterogeneous multicore computing clusters using a static batch scheduling strategy adapted to each node’s computational capacity. Combined, these algorithmic and computational enhancements yield an acceleration factor of 4049 compared to the original implementation. As a result, high-quality radiotherapy treatment plans can be automatically generated in approximately one hour, making this approach viable for integration into time-constrained clinical workflows.

Source: Future Generation Computer Systems

DOI: 10.1016/j.future.2025.108168

Authors: Juan José Moreno, Savíns Puertas-Martín, Nelson G. Roman, Juana L. Redondo, Ester M. Garzón

Institutions: University of Almería, Universidade Federal do Rio Grande do Sul

Language: English

Topic: Advanced Radiotherapy Techniques

Subfield: Radiation

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,000