Advanced image analysis to track daily biological changes in radiotherapy.

先进的图像分析可跟踪放射治疗中的日常生物变化。

• 批准号: 2787427
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2787427
• 关键词: Advanced; image; analysis; track; daily

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.

背景：利用高能x射线放射治疗癌症。对于前列腺癌患者，放射治疗是主要的治疗选择之一，与激素治疗联合使用。前列腺癌的治疗结果很好，很多人都治愈了。很可能对一些人来说，我们过度治疗了他们的疾病，而对另一些人来说，我们治疗不足，导致治疗失败。放射治疗是在数天内通过若干部分向患者进行治疗。两种主要的分治方案是在20天以上治疗或在5天以上的较短时间内给予高剂量。在每个治疗部位都要拍摄患者的图像，以确保他们正确地排列以进行治疗。这种图像引导可能是锥束CT，也可能是磁共振引导。这些图像可能包含有关前列腺癌对治疗反应的额外信息，可用于调整治疗（治疗升级或降级）。假设：在治疗期间拍摄的MR图像包含了前列腺癌对治疗反应的信息，可用于每位患者的个性化治疗。本项目将开发纵向医学图像数据分析所需的图像分析管道。这将使用我们的内部研究平台，包括图像配准（刚性和非刚性），轮廓传播和图像处理工具，并将链接到(1)pyradiomics，一个开源的python包，提取常见的预定义图像特征和(2)机器学习方法。目标：以下目标将在这个项目中解决（每一个都是一个计划的论文）：发展一种方法来处理纵向成像数据，选择最能描述肿瘤环境的图像特征。将时间序列数据中的图像特征用高斯过程建模，以处理时间序列中的不规则性。分层聚类将具有相似轨迹的特征分组，相互关联将为每个聚类选择最佳的、具有代表性的特征。2. 为了消除成像数据中与采集相关的偏移，需要对图像进行归一化处理。这确保了图像特征代表了组织的真实变化，而不是日常机器采集的变化。将测试几种标准化方法，以定义最佳实践方法。使用上述定义的方法，我们将首先研究前列腺癌与正常前列腺组织的纵向变化。一位临床专家描绘了前列腺癌的主要区域，从而可以提取图像特征。4. 研究前列腺已知生物学状态之间的相关性（即，如果前列腺缺氧，低氧合，从癌症活检计算的基因组特征定义）。拟议的项目与医疗技术的主题保持一致，然后在这个主题中有两个策略：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对侵入性样本收集的需求，最大限度地增加可用时间点的数量，并加快优化治疗干预的数据可用性。