Advanced image analysis to track daily biological changes in radiotherapy.

背景：高能X射线在癌症治疗中的放射治疗。对于前列腺癌患者，放射治疗是主要的治疗选择之一，与激素治疗相结合。前列腺癌的治疗结果很好，许多男性都治愈了。这很可能是因为，对于一些男性，我们对他们的疾病治疗过度，而对另一些男性，我们对他们的疾病治疗不足，这导致了治疗失败。放射治疗是分几个阶段进行的，分几天进行治疗。两个主要的分割计划是治疗20天以上或在5天以上的较短时间内给予大剂量治疗。在每个治疗片段上，都会拍摄一张患者的图像，以确保它们在治疗过程中正确排列。该图像引导可以是锥束CT，或者现在可以是MR引导。这些图像可能包含关于前列腺癌对治疗的反应的附加信息，可用于调整治疗中的男性的治疗(治疗升级或降级)。假设：在治疗过程中拍摄的MR图像包含前列腺癌对治疗的反应信息，可用于为每个患者个性化治疗。该项目将开发执行纵向医学图像数据分析所需的图像分析流水线。这将使用我们的内部研究平台，该平台包括图像配准(刚性和非刚性)、轮廓传播和图像处理工具，并将与(1)提取常见预定义图像特征的开源PYRADIODY包和(2)机器学习方法相链接。目标：本项目将解决以下目标(每个都是一篇计划中的论文)：1.开发一种处理纵向成像数据的方法，选择最能描述肿瘤环境的图像特征。时间序列数据中的图像特征将用高斯过程建模，以处理时间序列中的任何不规则现象。层次聚类将具有相似轨迹的特征分组，并且交叉相关将为每个聚类选择最好的、具有代表性的特征。2.需要对图像进行归一化，以消除成像数据中与采集相关的任何偏移。这确保了图像特征代表组织的真实变化，而不是日常机器获取的变化。将对几种标准化方法进行测试，以确定最佳实践方法。使用上面定义的方法，我们将首先研究前列腺癌与正常前列腺组织之间的纵向变化。一位临床专家描绘了前列腺癌的主要区域，允许提取图像特征。4.调查已知的前列腺生物学状态之间的相关性(即，如果前列腺是低氧、低氧，根据从癌症活检计算出的基因组签名定义的)。拟议的项目符合医疗保健技术的主题，然后与这一主题中的两个战略保持一致：物理干预的前沿+优化干预：通过将患者的成像表型与其疾病的潜在生物学联系起来，我们可以最大限度地减少侵入性样本收集的需求，最大限度地增加可用的时间点数量，并加快数据的可用性，以优化治疗干预。

Background: Radiotherapy in the treatment of cancer using high-energy x-rays. For patients with prostate cancer, radiotherapy is one of the main treatment options, used in combination with hormonal treatments. Treatment outcomes are good for prostate cancer with many men cured. It is likely that for some men we are over treating their disease and for other men we are under treating their disease which results in treatment failure. Radiotherapy is delivered to a patient across a number of fractions, treated over a number of days. The two main fractionation schedules are to treat over 20 days or a high dose given in a shorter time over 5 days. On each treatment fraction an image of the patient is taken to ensure they are aligned correctly for treatment. This image guidance may be a cone beam CT or now, may be MR guided. These images may contain additional information about how the prostate cancer is responding to treatment which can be used to adapt the treatment (treatment escalation or de-escalation) for men on treatment.Hypothesis: MR images taken during treatment contain information on how the prostate cancer is responding to treatment which can be used to personalize treatment for every patient.This project will develop the image analysis pipeline required to perform the analysis longitudinal medical image data. This will use our in-house research platform which includes image registration (rigid and non-rigid), contour propagation and image processing tools and will be linked to both (1) pyradiomics, an open-source python package, which extracts common pre-defined image features and (2) machine learning approaches. Objectives: The following objectives will be addressed in this project (each is a planned paper):1. Develop a methodology to handle longitudinal imaging data, selecting the image features that best describe the tumour environment. Image features in time-series data will be modelled with Gaussian process to handle any irregularities in the time-series. Hierarchical clustering will group features with similar trajectories and cross-correlations will select the best, representative, feature for each cluster. 2. Image normalisation is needed to remove any acquisition related offsets in the imaging data. This ensures that image features represent true changes in the tissue and not day-to-day machine acquisition changes. Several normalisation approaches will be tested to define a best practice approach.3. Using the above defined methodologies we will first investigate longitudinal changes in the prostate cancer versus normal prostate tissue. A clinical expert has delineated the dominate prostate cancer region allowing image features to be extracted. 4. Investigate correlations between known biological states of the prostate (i.e., if the prostate is hypoxic, low oxygenation, defined from a genomic signature calculated form the cancer biopsy). The proposed project aligns with the healthcare technology's theme, and then with two strategies within this theme:Frontiers of Physical Intervention + Optimising interventions: By linking the patients imaging phenotype with underlying biology of their disease we can minimise the need for invasive sample collection, maximise the number of time-points available and speed the availability of data for optimising treatment interventions.