基于深度卷积神经网络的赛博刀脑癌三维剂量预测:均质组织的精确射束建模。

BJR open Pub Date : 2024-08-16 eCollection Date: 2024-01-01 DOI:10.1093/bjro/tzae023
Yuchao Miao, Ruigang Ge, Chuanbin Xie, Xiangkun Dai, Yaoying Liu, Baolin Qu, Xiaobo Li, Gaolong Zhang, Shouping Xu
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引用次数: 0

摘要

目的:精确的射束建模对于立体定向放射治疗(SRT)(如 CyberKnife 治疗)的剂量计算至关重要。然而,目前的深度学习方法只涉及病人的解剖图像和用于训练的划定掩模。这些研究一般侧重于传统的调强放射治疗(RT)计划。然而,本文旨在开发一种基于深度 CNN 的方法,用于预测脑癌患者的 CyberKnife 计划剂量。该方法利用了建模射束信息、靶点划分和患者解剖信息:本研究提出了一种添加射束信息的方法,用于预测 CyberKnife 在脑部病例中的剂量分布。研究对 88 名使用射线追踪算法治疗的脑癌和腹腔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(规划 CT)、风险器官(OAR)和目标的二进制掩膜以及临床计划(包含射束信息)。数据集随机分为 68、6 和 14 个脑部病例,分别用于训练、验证和测试:结果:我们提出的方法在 SRT 剂量预测方面表现良好。首先,对于脑癌病例的伽马通过率,以2毫米/2%为标准,我们得到了96.7%±2.9%的身体通过率,98.3%±3.0%的规划靶体积通过率,100.0%±0.0%的小体积OAR通过率,参照了临床计划剂量。其次,在这些病例中,模型预测结果与临床计划的剂量-体积直方图相当吻合。靶区关键指标的差异一般低于 1.0 Gy(相对于处方剂量的差异约为 3%):对选定的 14 个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,可以在 CyberKnife 中对脑癌进行精确的三维剂量预测。还需要对更多患者和其他癌症部位进行进一步研究,以充分验证所提出的方法:有了精确的射束建模,深度学习模型可以快速生成 CyberKnife 病例的剂量分布。这种方法加快了 RT 计划流程,显著提高了其运行效率,并对其进行了优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Three-dimensional dose prediction based on deep convolutional neural networks for brain cancer in CyberKnife: accurate beam modelling of homogeneous tissue.

Objectives: 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.

Methods: 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.

Results: 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).

Conclusions: 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.

Advances in knowledge: 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.

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