| Liu Jiawen,Li Yongbao,Li Huali,et al.Automatic optimization of prognosis-guided intensity-modulated radiation therapy plans for lung cancer based on a gradient-enhanced swarm intelligence algorithm[J].Chinese Journal of Radiological Medicine and Protection,2025,45(4):302-308 |
| Automatic optimization of prognosis-guided intensity-modulated radiation therapy plans for lung cancer based on a gradient-enhanced swarm intelligence algorithm |
| Received:September 10, 2024 |
| DOI：10.3760/cma.j.cn112271-20240910-00343 |
| KeyWords:Intensity-modulated radiation therapy Prognosis-guided radiotherapy Large-scale nonlinear programming Swarm intelligence algorithm |
| FundProject:国家自然科学基金(82472117);广东省基础与应用基础研究基金(2024A1515011831,2024A1515010820) |
| Author Name | Affiliation | E-mail | | Liu Jiawen | School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China | | | Li Yongbao | State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou 510060, China | | | Li Huali | School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China | | | Zhou Linghong | School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China | | | Song Ting | School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China | tingsong2015@smu.edu.cn |
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| 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. |
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