Matsuura Takaaki, Kawahara Daisuke, Saito Akito, Yamada Kiyoshi, Ozawa Shuichi, Nagata Yasushi
Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan.
Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Med Phys. 2023 Apr;50(4):2488-2498. doi: 10.1002/mp.16210. Epub 2023 Jan 15.
Artificial intelligence (AI)-based gamma passing rate (GPR) prediction has been proposed as a time-efficient virtual patient-specific QA method for the delivery of volumetric modulation arc therapy (VMAT). However, there is a limitation that the GPR value loses the locational information of dose accuracy.
The objective was to predict the failing points in the gamma distribution and the GPR using a synthesized gamma distribution of VMAT QA with a deep convolutional generative adversarial network (GAN).
The fluence maps of 270 VMAT beams for prostate cancer were measured using an electronic portal imaging device and analyzed using gamma evaluation with 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances. The 270 gamma distributions were divided into two datasets: 240 training datasets for creating a model and 30 test datasets for evaluation. The image prediction network for the fluence maps calculated by the treatment planning system (TPS) to the gamma distributions was created using a GAN. The sensitivity, specificity, and accuracy of detecting failing points were evaluated using measured and synthesized gamma distributions. In addition, the difference between measured GPR (mGPR) and predicted GPR (pGPR) values calculated from the synthesized gamma distributions was evaluated.
The root mean squared errors between mGPR and pGPR were 1.0%, 2.1%, 3.5%, and 3.6% for the 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The accuracies for detecting failing points were 98.9%, 96.9%, 94.7%, and 93.7% for 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The sensitivity and specificity were the highest for 1%/0.5-mm and 3%/2-mm tolerances, which were 82.7% and 99.6%, respectively.
We developed a novel system using a GAN to generate a synthesized gamma distribution-based patient-specific VMAT QA. The system is promising from the point of view of quality assurance in radiotherapy because it shows high performance and can detect failing points.
基于人工智能（AI）的伽马通过率（GPR）预测已被提出作为一种高效的虚拟患者特异性质量保证方法，用于容积调强弧形放疗（VMAT）的实施。然而，存在一个局限性，即GPR值丢失了剂量准确性的位置信息。
目的是使用深度卷积生成对抗网络（GAN）通过VMAT质量保证的合成伽马分布来预测伽马分布中的失败点和GPR。
使用电子门静脉成像设备测量270个前列腺癌VMAT射束的注量图，并使用3%/2毫米、2%/1毫米、1%/1毫米和1%/0.5毫米容差的伽马评估进行分析。将270个伽马分布分为两个数据集：240个用于创建模型的训练数据集和30个用于评估的测试数据集。使用GAN创建了从治疗计划系统（TPS）计算的注量图到伽马分布的图像预测网络。使用测量和合成的伽马分布评估检测失败点的灵敏度、特异性和准确性。此外，评估了从合成伽马分布计算的测量GPR（mGPR）和预测GPR（pGPR）值之间的差异。
对于3%/2毫米、2%/1毫米、1%/1毫米和1%/0.5毫米容差，mGPR和pGPR之间的均方根误差分别为1.0%、2.1%、3.5%和3.6%。对于3%/2毫米、2%/1毫米、1%/1毫米和1%/0.5毫米容差，检测失败点的准确率分别为98.9%、96.9%、94.7%和93.7%。对于1%/0.5毫米和3%/2毫米容差，灵敏度和特异性最高，分别为82.7%和99.6%。
我们开发了一种使用GAN的新型系统，以生成基于合成伽马分布的患者特异性VMAT质量保证。从放射治疗质量保证的角度来看，该系统很有前景，因为它显示出高性能并且可以检测失败点。
