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Prescription Value-Based Automatic Optimization of Importance Factors in Inverse Planning

Abstract : Objective An automatic method for the optimization of importance factors was proposed to improve the efficiency of inverse planning. Methods The automatic method consists of 3 steps (1) First, the importance factors are automatically and iteratively adjusted based on our proposed penalty strategies. (2) Then, plan evaluation is performed to determine whether the obtained plan is acceptable. (3) If not, a higher penalty is assigned to the unsatisfied objective by multiplying it by a compensation coefficient. The optimization processes are performed alternately until an acceptable plan is obtained or the maximum iteration N-max of step (3) is reached. Results Tested on 2 kinds of clinical cases and compared with manual method, the results showed that the quality of the proposed automatic plan was comparable to, or even better than, the manual plan in terms of the dose-volume histogram and dose distributions. Conclusions The proposed algorithm has potential to significantly improve the efficiency of the existing manual adjustment methods for importance factors and contributes to the development of fully automated planning. Especially, the more the subobjective functions, the more obvious the advantage of our algorithm.
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Submitted on : Tuesday, January 7, 2020 - 10:02:47 AM
Last modification on : Tuesday, January 14, 2020 - 1:31:19 AM

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Caiping Guo, Pengcheng Zhang, Zhiguo Gui, Huazhong Shu, Lihong Zhai, et al.. Prescription Value-Based Automatic Optimization of Importance Factors in Inverse Planning. Technology in Cancer Research and Treatment, Adenine Press, 2019, 18, pp.1-13. ⟨10.1177/1533033819892259⟩. ⟨hal-02430080⟩

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