Automatic learning-based beam angle selection for thoracic IMRT
Automatic learning-based beam angle selection for thoracic IMRT
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DOI：
10.1118/1.4908000
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发表时间：
2015-04-01
期刊：
影响因子：
3.8
通讯作者：
Pekar, Vladimir
中科院分区：
文献类型：
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作者：
Amit, Guy;Purdie, Thomas G.;Pekar, Vladimir
目的：使用外部射束辐射的胸部癌症的治疗需要辐射射束方向的最佳选择，以确保目标体积的有效覆盖并避免正常健康组织的不必要治疗。调强放射治疗（IMRT）计划是一个漫长的过程，需要计划者在选择射束角度、指定剂量-体积目标和执行IMRT优化之间进行权衡。在胸部治疗计划中，没有针对射束放置的类解决方案，射束角度选择基于计划者的临床经验手动执行。这项工作的目的是提出和研究一个计算效率高的框架，利用机器学习自动选择治疗光束的角度。这样一个框架可能有助于减少整体规划workload.Methods：作者介绍了一种自动化的波束选择方法，基于学习波束角度和解剖特征之间的关系。使用大量临床批准的IMRT计划，训练随机森林回归算法以将大量解剖特征映射到单个射束分数中。然后建立一个优化方案来选择和调整光束角度，考虑学习到光束间的依赖关系。的有效性和质量的自动选择的光束评估使用手动选择的光束从相应的临床plannes. Results地面truth.Results：分析包括149临床批准的胸部调强放射治疗计划。对于随机选择的27个计划的测试子集，使用自动选择的射束生成IMRT计划，并与临床计划进行比较。预测和临床射束角度的比较表明两者之间具有良好的平均对应性（角距离16.8度+/- 10度，相关性0.75 +/- 0.2）。半自动和临床计划的剂量分布在主要靶体积覆盖和危及器官保留方面是等效的，并且上级使用固定组的共同射束角度产生的计划。绝大多数的自动计划（93%）被批准为临床上可接受的三个放射治疗specialists.Conclusions：结果表明，利用基于学习的方法自动选择射束角度在胸部调强放射治疗规划的可行性。所提出的方法可以帮助减少人工计划的工作量，同时保持计划质量。(C)2015年美国医学物理学家协会。
Purpose: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose-volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner's clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload.Methods: The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth.Results: The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8 degrees +/- 10 degrees, correlation 0.75 +/- 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume coverage and organ at risk sparing and were superior over plans produced with fixed sets of common beam angles. The great majority of the automatic plans (93%) were approved as clinically acceptable by three radiation therapy specialists.Conclusions: The results demonstrated the feasibility of utilizing a learning-based approach for automatic selection of beam angles in thoracic IMRT planning. The proposed method may assist in reducing the manual planning workload, while sustaining plan quality. (C) 2015 American Association of Physicists in Medicine.