title: Particle swarm optimizer for weighting factor selection in intensity-modulated radiation therapy optimization algorithms.
abstract: In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human planner intervention. A comparison of the results with the optimized solution obtained using a similar optimization model but with human planner intervention revealed that the proposed algorithm produced optimized plans superior to that developed using the manual plan. The proposed algorithm can generate admissible solutions within reasonable computational times and can be used to develop fully automated IMRT treatment planning methods, thus reducing human planners' workloads during iterative processes.
authors.affiliation: National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan, Shanxi 030051, China; School of Medicine Management, Shanxi University of TCM, Taiyuan, Shanxi 030619, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
authors.affiliation: National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan, Shanxi 030051, China.
authors.forename: Pengcheng
authors.initials: P
authors.lastname: Zhang
authors.affiliation: National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan, Shanxi 030051, China.
authors.forename: Liyuan
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Centre de Recherche en Information Médecine Sino-francais (CRIBs), Rennes F-35042, France.
authors.forename: Huazhong
authors.initials: H
authors.lastname: Shu
authors.affiliation: Shandong Tumor Hospital, Jinan 250117, China.
authors.forename: Baosheng
authors.initials: B
authors.lastname: Li
authors.affiliation: National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan, Shanxi 030051, China; Key Laboratory of Instrumentation Science & Dynamic Measurement, North University of China, Taiyuan, Shanxi 030051, China. Electronic address: gzgtg@163.com.
authors.forename: Zhiguo
authors.initials: Z
authors.lastname: Gui
mesh_terms: D000465:Algorithms; D005240:Feasibility Studies; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*
keywords: Automated planning; Intensity-modulated radiation therapy; Optimization; Particle swarm optimization; Treatment planning