Bedford James L, Hanson Ian M
Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5PT, UK.
Phys Imaging Radiat Oncol. 2022 Apr 20;22:36-43. doi: 10.1016/j.phro.2022.03.004. eCollection 2022 Apr.
Real-time portal dosimetry compares measured images with predicted images to detect delivery errors as the radiotherapy treatment proceeds. This work aimed to investigate the performance of a recurrent neural network for processing image metrics so as to detect delivery errors as early as possible in the treatment.
Volumetric modulated arc therapy (VMAT) plans of six prostate patients were used to generate sequences of predicted portal images. Errors were introduced into the treatment plans and the modified plans were delivered to a water-equivalent phantom. Four different metrics were used to detect errors. These metrics were applied to a threshold-based method to detect the errors as soon as possible during the delivery, and also to a recurrent neural network consisting of four layers. A leave-two-out approach was used to set thresholds and train the neural network then test the resulting systems.
When using a combination of metrics in conjunction with optimal thresholds, the median segment index at which the errors were detected was 107 out of 180. When using the neural network, the median segment index for error detection was 66 out of 180, with no false positives. The neural network reduced the rate of false negative results from 0.36 to 0.24.
The recurrent neural network allowed the detection of errors around 30% earlier than when using conventional threshold techniques. By appropriate training of the network, false positive alerts could be prevented, thereby avoiding unnecessary disruption to the patient workflow.
实时射野剂量测定在放射治疗过程中将测量图像与预测图像进行比较，以检测剂量投送误差。本研究旨在探讨递归神经网络处理图像指标的性能，以便在治疗过程中尽早检测到剂量投送误差。
使用6例前列腺癌患者的容积调强弧形治疗（VMAT）计划生成预测射野图像序列。在治疗计划中引入误差，并将修改后的计划投送至水等效体模。使用4种不同的指标检测误差。这些指标应用于基于阈值的方法，以便在投送过程中尽早检测到误差，同时也应用于一个由4层组成的递归神经网络。采用留二法设置阈值并训练神经网络，然后测试所得系统。
当结合使用指标和最佳阈值时，检测到误差时的中位段指数为180段中的107段。使用神经网络时，检测误差的中位段指数为180段中的66段，且无假阳性。神经网络将假阴性结果的发生率从0.36降低至0.24。
与使用传统阈值技术相比，递归神经网络能够提前约30%检测到误差。通过对网络进行适当训练，可以防止出现假阳性警报，从而避免对患者工作流程造成不必要的干扰。
