Automatic optimization of prognosis-guided intensity-modulated radiation therapy plans for lung cancer based on a gradient-enhanced swarm intelligence algorithm
10.3760/cma.j.cn112271-20240910-00343
- VernacularTitle:基于梯度增强群体智能算法的预后引导肺癌调强放疗计划自动优化方法
- Author:
Jiawen LIU
1
;
Yongbao LI
;
Huali LI
;
Linghong ZHOU
;
Ting SONG
Author Information
1. 南方医科大学生物医学工程学院，广州 510515
- Publication Type:Journal Article
- Keywords:
Intensity-modulated radiation therapy;
Prognosis-guided radiotherapy;
Large-scale nonlinear programming;
Swarm intelligence algorithm
- From:
Chinese Journal of Radiological Medicine and Protection
2025;45(4):302-308
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To address large-scale nonlinear programming challenges in optimizing prognosis-guided intensity-modulated radiation therapy (IMRT) plans, to propose gradient-enhanced random contrastive interaction particle swarm optimization (GradRCIPSO). This gradient-enhanced swarm intelligence algorithm aims to enable global optimization of prognostic treatment plans in clinically efficient scenarios.Methods:The core concept of GradRCIPSO lied in achieving rapid global convergence by allowing particles to learn both swarm interaction and gradient information. Specifically, the interaction information was obtained from elite individuals in the swarm, enabling the particles to efficiently search the entire solution space, whereas the gradient information represents the direction of the steepest descent, enabling the particles to quickly explore the current neighborhood. To assess the effectiveness of the methodology, the IMRT plans for 10 cases of non-small cell lung cancer (NSCLC) were selected in this study. They were compared with the GradRCIPSO-generated prognosis-guided IMRT plans. Moreover, the interior-point method, sequential quadratic programming, active set, gradient descent method, and random contrastive interaction particle swarm optimization (RCIPSO) were employed as optimization engines and compared with GradRCIPSO in terms of optimization efficiency and accuracy.Results:GradRCIPSO successfully generated clinically viable prognosis-guided IMRT plans with comparable dosimetric statistics to original plans, while significantly reducing predicted total radiotherapy risk from 1.22(0.84, 1.51) to 0.93(0.80, 1.29) ( z=2.81, P<0.01). It demonstrated superior accuracy over the above four gradient-based method ( z=2.80-2.81, P<0.01) and achieved threefold acceleration versus RCIPSO while maintaining equivalent solution quality( P>0.05). Conclusions:The proposed GradRCIPSO demonstrates high feasibility and performance in optimizing prognosis-guided IMRT plans, laying the technical foundation for the broad clinical application of prognosis-guided IMRT plans for lung cancer.
