| Li Chen,Yao Yuan,Wu Yongchang,et al.Pareto set of intensity-modulated radiotherapy plans for prostate cancer based on a multi-criteria evolutionary algorithm[J].Chinese Journal of Radiological Medicine and Protection,2026,46(4):367-375 |
| Pareto set of intensity-modulated radiotherapy plans for prostate cancer based on a multi-criteria evolutionary algorithm |
| Received:December 25, 2024 |
| DOI：10.3760/cma.j.cn112271-20241225-00494 |
| KeyWords:Multi-criteria optimization|Evolutionary algorithm|Radiotherapy planning|Intensity-modulated radiation therapy |
| FundProject:国家自然科学基金(12475348) |
| Author Name | Affiliation | E-mail | | Li Chen | School of Physics and Technology, Wuhan University, Wuhan 430072, China
Department of Radiation Physics Technical Center, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China | | | Yao Yuan | College of Computer Science, Sichuan University, Chengdu 610065, China | | | Wu Yongchang | Department of Radiation Physics Technical Center, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China | | | Luo Ran | School of Physics and Technology, Wuhan University, Wuhan 430072, China
Department of Radiation Physics Technical Center, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China | | | Hu Junjie | College of Computer Science, Sichuan University, Chengdu 610065, China | | | Hu Wang | School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China | | | Quan Hong | School of Physics and Technology, Wuhan University, Wuhan 430072, China | 00007962@whu.edu.cn | | Li Guangjun | Department of Radiation Physics Technical Center, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China | | | Bai Sen | Department of Radiation Physics Technical Center, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China | |
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| Abstract:: |
| Objective To explore the Pareto set in multi-criteria optimization for radiotherapy by facilitating Pareto surface navigation using an evolutionary algorithm and to generate a series of diverse plans based on a single preferential expression. Methods A novel posterior-based multi-criteria evolutionary algorithm was developed. This algorithm, combined with anchor plans-each obtained from the Pareto surface using a gradient-based algorithm and corresponding to a specific constraint, was employed to explore the linear combinations of these anchor plans. Following the generation of a single preferential expression of the generated population, the proposed algorithm yielded a set of plans near the Pareto surface, offering more optimal dosimetric parameters. The performance of the algorithm was tested using 15 prostate cancer cases, with planning target volumes (PTVs) including the prostate (PTV1) and lymph node basins (PTV2). For each case, a 7-field intensity-modulated radiotherapy (IMRT) plan was generated and optimized. Subsequently, planned doses were calculated using the matRad toolkit, and the algorithm performance was assessed based on dosimetric indicators. Results The multi-criteria evolutionary algorithm generated diverse plan sets. For organs at risk (OARs) bladder, rectum and small intestine, the average dose differences between the maximum and minimum of mean doses were determined at 10.8, 10.4, and 2.9 Gy, those of V50 (ΔV50) were 33.7%, 31.4%, and 8.4%, and those of V30 (ΔV30)were 25.7%, 21.6%, and 7.4%, respectively. In contrast, for the PTVs, the Pareto set yielded by the algorithm showed narrow search ranges. Specifically, the average differences in D95% (ΔD95%) for PTV1 and PTV2 were 4.0 and 3.4 Gy, respectively. Conclusions This study, grounded in a posterior-based approach, employs an evolutionary algorithm to explore the MCO-derived Pareto set of radiotherapy plans for the first time. A series of diverse and feasible IMRT plans are generated by searching the linear combination of anchor plans, thereby effectively assisting physicists in retrieving and selecting the optimal treatment plans. |
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