Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, California, USA.
Department of Radiation Oncology, University of Miami, Miami, Florida, USA.
Med Phys. 2022 Jul;49(7):4293-4304. doi: 10.1002/mp.15676. Epub 2022 May 11.
Dose deposition characteristics of proton radiation can be advantageous over photons. Proton treatment planning, however, poses additional challenges for the planners. Proton therapy is usually delivered with only a small number of beam angles, and the quality of a proton treatment plan is largely determined by the beam angles employed. Finding the optimal beam angles for a proton treatment plan requires time and experience, motivating the investigation of automatic beam angle selection methods.
A deep learning-based approach to automatic beam angle selection is proposed for the proton pencil-beam scanning treatment planning of liver lesions.
We cast beam-angle selection as a multi-label classification problem. To account for angular boundary discontinuity, the underlying convolution neural network is trained with the proposed Circular Earth Mover's Distance-based regularization and multi-label circular-smooth label technique. Furthermore, an analytical algorithm emulating proton treatment planners' clinical practice is employed in post-processing to improve the output of the model. Forty-nine patients that received proton liver treatments between 2017 and 2020 were randomly divided into training (n = 31), validation (n = 7), and test sets (n = 11). AI-selected beam angles were compared with those angles selected by human planners, and the dosimetric outcome was investigated by creating plans using knowledge-based treatment planning.
For 7 of the 11 cases in the test set, AI-selected beam angles agreed with those chosen by human planners to within 20° (median angle difference = 10°; mean = 18.6°). Moreover, out of the total 22 beam angles predicted by the model, 15 (68%) were within 10° of the human-selected angles. The high correlation in beam angles resulted in comparable dosimetric statistics between proton treatment plans generated using AI- and human-selected angles. For the cases with beam angle differences exceeding 20°, the dosimetric analysis showed similar plan quality although with different emphases on organ-at-risk sparing.
This pilot study demonstrated the feasibility of a novel deep learning-based beam angle selection technique. Testing on liver cancer patients showed that the resulting plans were clinically viable with comparable dosimetric quality to those using human-selected beam angles. In tandem with auto-contouring and knowledge-based treatment planning tools, the proposed model could represent a pathway for nearly fully automated treatment planning in proton therapy.
质子放射的剂量沉积特性优于光子。然而，质子治疗计划对规划者提出了额外的挑战。质子治疗通常只使用少量的射束角度，质子治疗计划的质量在很大程度上取决于所使用的射束角度。找到质子治疗计划的最佳射束角度需要时间和经验，这促使人们研究自动射束角度选择方法。
提出了一种基于深度学习的方法，用于肝脏病变的质子铅笔束扫描治疗计划中的自动射束角度选择。
我们将射束角度选择视为多标签分类问题。为了考虑角度边界不连续性，基础卷积神经网络通过所提出的基于圆形地球移动距离的正则化和多标签圆形平滑标签技术进行训练。此外，在后期处理中使用模拟质子治疗计划师临床实践的分析算法来改进模型的输出。将 2017 年至 2020 年间接受质子肝脏治疗的 49 名患者随机分为训练集（n=31）、验证集（n=7）和测试集（n=11）。比较 AI 选择的射束角度与人类规划师选择的角度，并使用基于知识的治疗计划创建计划来研究剂量学结果。
在测试集中的 11 个病例中的 7 个病例中，AI 选择的射束角度与人类规划师选择的角度相差 20°以内（中位数角度差=10°；平均值=18.6°）。此外，模型预测的 22 个射束角度中，有 15 个（68%）与人类选择的角度相差 10°以内。射束角度的高度相关性导致使用 AI 和人类选择的角度生成的质子治疗计划的剂量学统计数据具有可比性。对于角度差异超过 20°的病例，尽管在保护危及器官方面有所不同，但剂量学分析表明计划质量相似。
这项初步研究证明了一种新的基于深度学习的射束角度选择技术的可行性。对肝癌患者的测试表明，生成的计划在临床上是可行的，与使用人类选择的射束角度生成的计划具有可比的剂量学质量。与自动轮廓和基于知识的治疗计划工具相结合，所提出的模型可以为质子治疗中的几乎全自动治疗计划提供途径。
