Miao Yuchao, Ge Ruigang, Xie Chuanbin, Dai Xiangkun, Liu Yaoying, Qu Baolin, Li Xiaobo, Zhang Gaolong, Xu Shouping
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China.
BJR Open. 2024 Aug 16;6(1):tzae023. doi: 10.1093/bjro/tzae023. eCollection 2024 Jan.
Accurate beam modelling is essential for dose calculation in stereotactic radiation therapy (SRT), such as CyberKnife treatment. However, the present deep learning methods only involve patient anatomical images and delineated masks for training. These studies generally focus on traditional intensity-modulated radiation therapy (RT) plans. Nevertheless, this paper aims to develop a deep CNN-based method for CyberKnife plan dose prediction about brain cancer patients. It utilized modelled beam information, target delineation, and patient anatomical information.
This study proposes a method that adds beam information to predict the dose distribution of CyberKnife in brain cases. A retrospective dataset of 88 brain and abdominal cancer patients treated with the Ray-tracing algorithm was performed. The datasets include patients' anatomical information (planning CT), binary masks for organs at risk (OARs) and targets, and clinical plans (containing beam information). The datasets were randomly split into 68, 6, and 14 brain cases for training, validation, and testing, respectively.
Our proposed method performs well in SRT dose prediction. First, for the gamma passing rates in brain cancer cases, with the 2 mm/2% criteria, we got 96.7% ± 2.9% for the body, 98.3% ± 3.0% for the planning target volume, and 100.0% ± 0.0% for the OARs with small volumes referring to the clinical plan dose. Secondly, the model predictions matched the clinical plan's dose-volume histograms reasonably well for those cases. The differences in key metrics at the target area were generally below 1.0 Gy (approximately a 3% difference relative to the prescription dose).
The preliminary results for selected 14 brain cancer cases suggest that accurate 3-dimensional dose prediction for brain cancer in CyberKnife can be accomplished based on accurate beam modelling for homogeneous tumour tissue. More patients and other cancer sites are needed in a further study to validate the proposed method fully.
With accurate beam modelling, the deep learning model can quickly generate the dose distribution for CyberKnife cases. This method accelerates the RT planning process, significantly improves its operational efficiency, and optimizes it.
精确的射束建模对于立体定向放射治疗（SRT）中的剂量计算至关重要，如射波刀治疗。然而，目前的深度学习方法仅涉及患者解剖图像和用于训练的勾画掩码。这些研究通常聚焦于传统的调强放射治疗（RT）计划。尽管如此，本文旨在开发一种基于深度卷积神经网络（CNN）的方法，用于预测脑癌患者的射波刀计划剂量。该方法利用了建模的射束信息、靶区勾画和患者解剖信息。
本研究提出一种添加射束信息以预测脑部病例中射波刀剂量分布的方法。对88例采用射线追踪算法治疗的脑癌和腹部癌患者进行了回顾性数据集分析。数据集包括患者的解剖信息（计划CT）、危及器官（OARs）和靶区的二进制掩码，以及临床计划（包含射束信息）。数据集被随机分为68例、6例和14例脑部病例，分别用于训练、验证和测试。
我们提出的方法在SRT剂量预测中表现良好。首先，对于脑癌病例中的伽马通过率，按照2毫米/2%的标准，相对于临床计划剂量，身体部位为96.7%±2.9%，计划靶区体积为98.3%±3.0%，小体积OARs为100.0%±0.0%。其次，对于这些病例，模型预测与临床计划的剂量体积直方图相当匹配。靶区关键指标的差异通常低于1.0戈瑞（相对于处方剂量约3%的差异）。
所选14例脑癌病例的初步结果表明，基于均匀肿瘤组织的精确射束建模，可以实现射波刀治疗脑癌时精确的三维剂量预测。进一步研究需要更多患者和其他癌症部位来充分验证所提出的方法。
通过精确的射束建模，深度学习模型可以快速生成射波刀病例的剂量分布。该方法加速了放疗计划过程，显著提高了其运行效率并进行了优化。
