Matsuura Takaaki, Kawahara Daisuke, Saito Akito, Miura Hideharu, Yamada Kiyoshi, Ozawa Shuichi, Nagata Yasushi
Hiroshima High-Precision Radiotherapy Cancer Center, 732-0057, Hiroshima, Japan.
Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 734-8551, Hiroshima, Japan.
Phys Eng Sci Med. 2022 Dec;45(4):1073-1081. doi: 10.1007/s13246-022-01172-w. Epub 2022 Oct 6.
To predict the gamma passing rate (GPR) of the three-dimensional (3D) detector array-based volumetric modulated arc therapy (VMAT) quality assurance (QA) for prostate cancer using a convolutional neural network (CNN) with the 3D dose distribution. One hundred thirty-five VMAT plans for prostate cancer were selected: 110 plans were used for training and validation, and 25 plans were used for testing. Verification plans were measured using a helical 3D diode array (ArcCHECK). The dose distribution on the detector element plane of these verification plans was used as input data for the CNN model. The measured GPR (mGPR) values were used as the training data. The CNN model comprises eighteen layers and predicted GPR (pGPR) values. The mGPR and pGPR values were compared, and a cumulative frequency histogram of the prediction error was created to clarify the prediction error tendency. The correlation coefficients of pGPR and mGPR were 0.67, 0.69, 0.66, and 0.73 for 3%/3-mm, 3%/2-mm, 2%/3-mm, and 2%/2-mm gamma criteria, respectively. The respective mean±standard deviations of pGPR-mGPR were -0.87±2.18%, -0.65±2.93%, -0.44±2.53%, and -0.71±3.33%. The probabilities of false positive error cases (pGPR < mGPR) were 72%, 60%, 68%, and 56% for each gamma criterion. We developed a deep learning-based prediction model of the 3D detector array-based VMAT QA for prostate cancer, and evaluated the accuracy and tendency of prediction GPR. This model can provide a proactive estimation for the results of the patient-specific QA before the verification measurement.
利用卷积神经网络（CNN）结合三维剂量分布来预测基于三维（3D）探测器阵列的容积调强弧形放疗（VMAT）对前列腺癌进行质量保证（QA）时的伽马通过率（GPR）。选取了135例前列腺癌VMAT计划：110例计划用于训练和验证，25例计划用于测试。使用螺旋三维二极管阵列（ArcCHECK）测量验证计划。这些验证计划在探测器元件平面上的剂量分布用作CNN模型的输入数据。测量得到的GPR（mGPR）值用作训练数据。CNN模型由18层组成并预测GPR（pGPR）值。比较mGPR和pGPR值，并创建预测误差的累积频率直方图以阐明预测误差趋势。对于3%/3毫米、3%/2毫米、2%/3毫米和2%/2毫米的伽马标准，pGPR与mGPR的相关系数分别为0.67、0.69、0.66和0.73。pGPR - mGPR各自的平均±标准差分别为-0.87±2.18%、-0.65±2.93%、-0.44±2.53%和-0.71±3.33%。对于每个伽马标准，假阳性误差情况（pGPR < mGPR）的概率分别为72%、60%、68%和56%。我们开发了一种基于深度学习的针对前列腺癌的基于三维探测器阵列的VMAT QA预测模型，并评估了预测GPR的准确性和趋势。该模型可以在验证测量之前对患者特定QA的结果进行前瞻性估计。
