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A Script-Based Automatic Intensity Modulated Radiation Therapy Planning Method With Robust Optimization for Craniospinal Irradiation

医学 稳健性(进化) 核医学 放射治疗计划 全自动 计算机科学 放射治疗 放射科 生物化学 机械工程 基因 工程类 化学
作者
Xuetao Wang,Changhu Li,Guangjun Li,Jianling Zhao,Sen Bai
出处
期刊:Practical radiation oncology [Elsevier BV]
卷期号:13 (2): e209-e215 被引量:2
标识
DOI:10.1016/j.prro.2022.08.015
摘要

This report describes a script-based automatic planning method with robust optimization for craniospinal irradiation (CSI) to reduce sensitivity to field matching errors and increase planning efficiency. The data of 10 CSI patients with planning target volume (PTV) lengths between 49.8 and 85.0 cm were retrospectively studied. Robust intensity modulated radiation therapy plans with ±5-mm longitudinal position uncertainty were generated by the automatic planning script. A simple dose prediction model and a self-adjusting method were implied in the automatic plans. The plans' robustness against setup errors was evaluated by deliberately shifting the middle beamset ±5 mm in the superior-inferior direction. Manual and nonrobust plans were also created to evaluate the automatic robust plans' quality, efficiency, and robustness. There were no significant differences between the manual and automatic plans in terms of homogeneity index; conformity index; D1%, D2%, and D98% of PTV; and average doses of organs at risk. However, the D99% of the PTV in the automatic plans was slightly inferior to that in the manual plans. Compared with the manual plans, the automatic plans greatly increased efficiency, with a reduction in planning time of approximately 48%. When ±5-mm superior-inferior errors were introduced, the average deviations of the maximum dose D1% and minimum dose D99% to the spinal cord were 4.9% (±1.1%) and -3.4% (±1.3%), respectively. However, the corresponding values of the nonrobust plans were 20.0% (±5.4%) and -21.2 (±6.3%), respectively. The script-based automatic CSI planning method, combining robust optimization and a dose prediction model, efficiently created a good-quality plan that was robust to setup errors.
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