Department of Radiation Medicine, Guangdong Provincial Key Laboratory of Tropical Disease Research, NMPA Key Laboratory for Safety Evaluation of Cosmetics, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China.
Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
J Appl Clin Med Phys. 2024 Aug;25(8):e14372. doi: 10.1002/acm2.14372. Epub 2024 May 6.
Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks.
The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations.
We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators.
The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors.
When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.
为了确保强度调制放射治疗（IMRT）和容积调制弧形治疗（VMAT）的个体化治疗计划的质量，需要进行事先验证。然而，标准方法在分析位置剂量分布数据方面存在缺陷，缺乏敏感性，导致难以准确识别计划验证失败的原因。这一问题使 QA 任务的效率复杂化和受阻。
本研究的主要目的是利用深度学习算法提取 3D 剂量分布图，并为跨多个机器模型、治疗方法和肿瘤位置的误差分类创建预测模型。
我们设计了五类验证计划（正常、机架误差、准直器误差、治疗床误差和剂量误差），符合不同精度水平的容差限制，并使用来自 94 例肿瘤患者的 3D 剂量分布数据。然后构建了一个 CNN 模型来预测不同的误差类型，预测结果与伽马通过率（GPR）标准进行比较，采用不同的阈值（3%，3mm；3%，2mm；2%，2mm）来评估模型的性能。此外，我们通过评估模型在不同加速器上的功能来评估模型的稳健性。
CNN 模型的准确性、精度、召回率和 F1 分数分别为 0.907、0.925、0.907 和 0.908。同时，在另一台设备上的性能为 0.900、0.918、0.900 和 0.898。此外，与 GPR 方法相比，CNN 模型在预测不同类型的误差方面具有更好的性能。
与 GPR 方法相比，CNN 模型在不同设备上的放射治疗计划验证中具有更好的分类预测能力。通过使用该模型，可以更快速和高效地检测计划验证失败，最大限度地减少 QA 任务所需的时间，并作为克服 GPR 方法限制的有价值的辅助手段。
