■精确的波束建模对于立体定向放射治疗(SRT)中的剂量计算至关重要,比如射波刀治疗。然而,当前的深度学习方法仅涉及患者解剖图像和用于训练的描绘掩模。这些研究通常集中在传统的调强放射治疗(RT)计划上。然而,本文旨在开发一种基于深度CNN的脑肿瘤患者Cyberknife计划剂量预测方法。它利用了建模的光束信息,目标轮廓,和病人的解剖信息.
■这项研究提出了一种方法,该方法增加了光束信息,以预测大脑病例中CyberKnife的剂量分布。对88名用Ray追踪算法治疗的脑和腹部癌症患者进行了回顾性数据集。数据集包括患者的解剖信息(计划CT),用于危险器官(OAR)和目标的二元面具,和临床计划(包含波束信息)。将数据集随机分为68、6和14例大脑病例进行训练,验证,和测试,分别。
■我们提出的方法在SRT剂量预测中表现良好。首先,对于脑癌病例的伽马通过率,使用2毫米/2%的标准,我们得到了96.7%±2.9%的身体,计划目标体积为98.3%±3.0%,对于涉及临床计划剂量的小体积OAR,为100.0%±0.0%。其次,对于这些病例,模型预测结果与临床计划的剂量-体积直方图相当吻合.在目标区域的关键指标的差异通常低于1.0Gy(相对于处方剂量大约3%的差异)。
■选定的14例脑癌病例的初步结果表明,基于均匀肿瘤组织的精确波束建模,可以实现对射波刀脑癌的准确三维剂量预测。在进一步的研究中需要更多的患者和其他癌症部位来充分验证所提出的方法。
■通过精确的光束建模,深度学习模型可以快速生成Cyberknife病例的剂量分布。该方法加速了RT计划过程,显著提高了其运营效率,并优化它。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.