Recurrent Neural Networks for anatomy and dose nowcasting in radiotherapy

目的：癌症占英国死亡人数的25%以上。各种各样的治疗，如化疗和放疗，分别是可用的。放射治疗涉及针对患者疾病的放射治疗，同时旨在最大限度地减少照射健康组织引起的副作用。放射治疗作为治疗的一部分提供给>50%的癌症患者。目前，在患者的治疗前图像上设计的一个放射治疗计划在患者的4-6周治疗期间被递送。然而，解剖结构会发生变化，并且需要完全在线自适应放射治疗，以最好地适应患者每天的解剖结构。对于身体的某些部位，如子宫颈、膀胱和腹部肿瘤，这种方法可以减少副作用。为了每天创建一个新的计划，标准的放射治疗工作流程必须从几天压缩到几分钟。当病人在治疗床上等待时，必须进行适应：这个过程花费的时间越长，他们的解剖结构就越有可能发生变化，适应的任何好处都会失去。因此，该项目将设计使用机器学习技术实现全自动快速安全检查的方法，以确保在线自适应放射治疗的准确性和安全性。方法：治疗前，患者将进行计划CT扫描，以定义靶体积，邻近正常组织并创建参考计划。这是根据每天的解剖结构调整治疗的基线。患者数据集的纵向性质（即随后几天的图像）自然适合使用递归神经网络（RNN）作为确保适应过程准确性的基础。RNN以前没有应用于医学图像数据，因此需要探索最佳的网络架构和训练方法。在治疗的第一天，网络没有先验信息，这意味着它的预测将是基于人群的;在治疗过程的后期，有关个体患者的信息被纳入，创建个性化的预测。由于从基于人群的预测过渡到个性化预测，量化模型的准确性至关重要。设计一种鲁棒的方法来处理RNN中的纵向医学图像将成为方法学论文的基础（论文1）。RNN将应用于我们在多个时间点拥有放疗患者MR图像的数据集。(18x 4个时间点的宫颈癌患者，4个时间点的15例膀胱癌患者，3个时间点的10例肺癌患者，3个时间点的10例胰腺癌患者）。将我们的RNN应用于一组不同的治疗部位将显示该方法的普遍性，并将形成论文2。重要的是，我们将比较使用RNN与手动检查检查解剖结构分类的速度。接下来，RNN将用于预测在给定治疗日最佳治疗解剖结构所需的辐射剂量。我们将应用我们的RNN方法来预测每个治疗日的最佳剂量（使用初始参考计划+解剖结构和网络中任何可用的后续日期）。该预测将形成与临床适应工作流程创建的内容进行比较的基础。上述数据集将用于多个治疗部位的测试（论文3）。最后，我们将展示完整临床工作流程的原理证明（使用生成的数据），说明这些模型如何在临床环境中工作以及节省时间的潜力（论文4）。新奇：RNN通常用于其他领域，例如天气预报，但尚未应用于放射治疗期间的解剖预测。这项研究将是RNN技术在3D体积数据中的首次应用，也是该技术在解剖和剂量预测中的首次应用。

Objectives: Cancer accounts for >25% of deaths in the UK. A wide variety of therapies, such as chemotherapy and radiotherapy respectively, are available. Radiotherapy involves the delivery of radiation, targeted at the patient's disease, while aiming to minimise side effects caused by irradiating healthy tissue. Radiotherapy is delivered to >50% of cancer patients as part of their treatment. Currently, one radiotherapy plan designed on the patient's pre-treatment image is delivered throughout a patient's 4-6-week treatment. However, anatomy changes, and there is a need for full online adaptive radiotherapy to best fit the patient's anatomy each day. For some parts of the body where large day-to-day changes occur, e.g. cervix, bladder and abdominal tumours, this approach can reduce side effects. To create a new plan each day, the standard radiotherapy workflow must be compressed from days to minutes. Adaptation must happen while the patient waits on the treatment couch: the longer this process takes the more likely their anatomy is to change, and any benefit of adaption is lost. Therefore, this project will devise methods to implement fully automatic, rapid safety checks using machine learning techniques to ensure the accuracy and safety of online adaptive radiotherapy.Methods: Prior to treatment, patients will have a planning CT scan to define the target volume, neighbouring normal tissue and create a reference plan. This acts as a baseline for adapting the treatment to each day's anatomy. The longitudinal nature of a patient's dataset (i.e. images on subsequent days) naturally lends itself to the use of recurrent neural networks (RNNs) as a basis for ensuring the accuracy of the adaptation process. RNNs have not been applied to medical image data before, therefore the optimal network architecture and training methodology needs to be explored. On the first day of treatment the network has no prior information, meaning its predictions will be population-based; later in the treatment process information about the individual patient is incorporated, creating a personalised prediction. Because of the transition from population-based to personalised prediction, quantifying the accuracy of the model is essential. Devising a robust methodology to handle longitudinal medical images within an RNN will form the basis of a methodological paper (paper 1). The RNN will be applied to datasets where we have MR images of radiotherapy patients at multiple timepoints. (18x Cervical cancer patients at 4 time-points, 15x bladder cancer patients at 4 timepoints, 10 lung cancer patients at 3 timepoints, 10x pancreas cancer patients at 3 timepoints). The application of our RNN to a diverse set of treatment sites will show the generalisability of the approach and will form paper 2. Importantly, we will compare the speed of checking the classification of the anatomy with the RNN versus manual checks.Next, an RNN will be applied to predict the radiation dose required to best treat the anatomy on a given treatment day. We will apply our RNN methodology to predict the optimal dose for each treatment day (using the initial reference plan + anatomy and any available subsequent days in the network). This prediction will form the basis for comparison against what the clinical adaption workflow creates. The datasets described above will be used for testing across multiple treatment sites (paper 3). Finally, we will show proof-of-principle of a full clinical workflow (using generated data), illustrating how these models would work within the clinical environment and the potential for time-saving (paper 4).Novelty: RNNs are commonly used in other fields, such as weather forecasting, but as yet have not been applied to the prediction of anatomy during radiotherapy treatment. This research will be the first application of RNN techniques in 3D volumetric data, and the first application of the techniques to the prediction of anatomy and dose.