Exploring advanced FDG PET-CT imaging feature analysis for more accurate diagnosis and outcome prediction in suspected large vessel vasculitis

探索先进的 FDG PET-CT 成像特征分析，以更准确地诊断和预测疑似大血管炎

• 批准号: 1958590
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1958590
• 关键词: Exploring; advanced; FDG; PET; CT

The aim of this project is to discover and validate predictive parameters extracted from FDG PET-CT images of patients with and without LVV that will allow the development of a radiomic model that can determine the prognosis of LVV patients effectively.Objectives:- Collate outcome data for a large single centre cohort of patients with baseline FDG PET-CT data- Use advanced feature analysis and machine learning to develop putative diagnostic and predictive imaging biomarkers in test and validation cohorts derived from FDG PET-CT images of patients with and without GCA from dataset from a single centre - Correlate outcomes with extracted parameters to determine which parameters have the best performance characteristics- Validate test performance within a multi-centre FDG PET-CT imaging cohort allowing for harmonisation of the process across different scanners. Data pooling facilitated as part of an established GCA multi-centre trial in the UK (TARGET Consortium, MRC)- Explore the potential of this process in (a) other forms of LVV, and (b) MRA and CTA imagingThe facilities used for this project will include:- Deep learning software to build a radiomic model- Image analysis software (LifeX)- Baseline FDG PET-CT data from the 2011-2017 Leeds LVV cohort - FDG PET-CT from patients who were tested but not diagnosed with LVV- FDG PET-CT data from other centres to validate the model - MRA & CTA data taken during the treatment of the 2011-2017 Leeds LVV cohortThe analysis of medical images requires developing an understanding of interactions between modality / modalities and disease. While PET-CT, MRA and CTA imaging of LVV is well established, not all the data provided by these scans will have been analysed; the relevant extracted features could provide valuable insights into the characteristics of the disease that have not been discovered through routine / traditional clinical approaches.The radiomic features (the parameters extracted) relevant to LVV prognosis are currently unknown but could include:- the heterogeneity of the vessel (textural analysis)- size, shape and location of inflammation- relationship with neighbouring tissues (requires statistical analysis considering parameters such as energy and entropy)- intensity of signal- density of tissue- parameters that may be unique to the illness and not established previouslyOnce the parameters are determined, their relationship to the disease and its phenotype may be established. The features will be vulnerable to image quality of the data and image processing methods such as over smoothing in the case of textural analysis. Therefore the methods will require optimisation and will not necessarily be directly transferable from previous work.Once the parameters have been established, we will create a novel radiomic model that can: (a) determine predictive biomarkers in a given image and (b) make patient outcome predictions. For these purposes, we will utilise deep learning with the help of a convolutional artificial neural network. The multiple layers of this network will serve a purpose, ranging from data extraction, through to the evaluation of a given feature. The image will cascade through the layers being weighted by the criteria and the model will make a judgement about the accuracy and precision of the prognosis. The model will need to be trained to recognise the predictive biomarkers with a large data set to minimise incorrect predictions.As the project will be co-supervised by clinicians at the forefront of LVV research, it will be designed to be clinically relevant and useful.

本项目的目的是发现和验证从有和没有LVV的患者的FDG PET-CT图像中提取的预测参数，这将允许开发一种放射组学模型，可以有效地确定LVV患者的预后。目的：- 整理具有基线FDG PET-CT数据的大型单中心患者队列的结果数据-使用先进的特征分析和机器学习来开发来自FDG PET的测试和验证队列中的推定诊断和预测成像生物标志物-来自单中心数据集的患有和不患有GCA的患者的CT图像-将结果与提取的参数相关联，以确定哪些参数具有最佳性能特征-在多中心FDG PET-CT成像队列中验证性能，从而允许不同扫描仪之间的过程协调。作为英国既定GCA多中心试验的一部分，促进数据汇集（TARGET Consortium，MRC）-探索该过程在（a）其他形式的LVV和（B）MRA和CTA成像中的潜力本项目使用的设施将包括：- 用于构建放射组学模型的深度学习软件-图像分析软件（LifeX）-来自2011-2017年利兹LVV队列的基线FDG PET-CT数据-来自被测试但未被诊断患有LVV的患者的FDG PET-CT-来自其他中心的用于验证模型的FDG PET-CT数据- MRA & 2011-2017年利兹LVV队列治疗期间采集的CTA数据医学图像分析需要了解模态/模态与疾病之间的相互作用。虽然LVV的PET-CT、MRA和CTA成像已经很好地建立，但并非所有这些扫描提供的数据都将被分析;相关提取的特征可以提供对通过常规/传统临床方法尚未发现的疾病特征的有价值的见解。与LVV预后相关的（提取的参数）目前尚不清楚，但可能包括：- 血管的异质性（纹理分析）-尺寸，炎症的形状和位置-与邻近组织的关系（需要考虑诸如能量和熵之类的参数的统计分析）-信号强度-组织密度-对于疾病可能是唯一的并且先前没有建立的参数一旦确定了参数，可以确定它们与疾病及其表型的关系。这些特征将容易受到数据的图像质量和图像处理方法的影响，例如在纹理分析的情况下过度平滑。因此，该方法将需要优化，并不一定是直接从以前的工作transferable.一旦参数已经建立，我们将创建一个新的放射组学模型，可以：（a）确定在给定的图像和（B）预测患者的预后预测生物标志物。为此，我们将在卷积人工神经网络的帮助下利用深度学习。该网络的多个层将用于一个目的，从数据提取到给定特征的评估。图像将级联通过由标准加权的层，并且模型将对预测的准确性和精确度做出判断。该模型将需要进行训练，以识别具有大量数据集的预测生物标志物，以最大限度地减少错误预测。由于该项目将由LVV研究前沿的临床医生共同监督，因此它将被设计为临床相关和有用。