MICA: InterdisciPlInary Collaboration for efficienT and effective Use of clinical images in big data health care RESearch: PICTURES

MICA：跨学科合作，在大数据医疗保健中高效、有效地使用临床图像 研究：图片

• 批准号: MR/S010351/1
• 负责人: Emily Jefferson
• 金额: $ 363.39万
• 依托单位: University of Dundee
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS010351%2F1
• 关键词: MICA; InterdisciPlInary; Collaboration; efficienT; effective

Clinical imaging including X-rays, CT, MRI, ultrasound and nuclear medicine scans are core diagnostic technologies. These images can support many important areas of research to improve any or all of diagnosis, monitoring of disease progression and response to treatment. Currently most research using images is based on those collected specifically for a particular research project. The images are of "research" quality. That means that they are captured at high resolution using standardised procedures to reduce the variability of images. Research data collection is expensive so studies tend to be small, and the people who take part in research studies are different to those seen in normal clinical care. It is therefore often uncertain whether research findings can be translated to "real world" images or patients.Each year millions of clinical images are generated in Scotland through routine examinations at hospitals and stored in a huge database. The Scottish national imaging database currently has ~23 million different images collected since 2010. Access to these "real world" images would be extremely valuable for research, but there are a number of big challenges. Firstly, it is very important that all data is kept confidential. Secondly, imaging datasets are very large which it technically challenging. Thirdly, the software which manages these images is optimised for retrieval of images by NHS staff specifically for an individual patient's clinical care (e.g. return Mrs Jones' scan taken on the 28th of May 2016) rather than for research (e.g. return all the CT chest scans of smokers between age 55 and 65 where a contrast agent has been used). What will be delivered? This 5 year programme will enable secure access to routinely collected imaging data for research. Using the foundation blocks already in place from previous research grants, PICTURES will extend, scale and enhance innovative open source software to query a research copy of the Scottish National imaging database securely hosted by the University of Edinburgh and provide anonymised extracts of hundreds of thousands of images for research. PICTURES will also develop this software to query imaging data linked to genomic data securely hosted by the University of Dundee.There are 3 main areas of research required within the core programme: (1) Data science research for complex cohort building from real-world, messy data. (2) Engineering required for scaling and handling big data within a Safe Haven environment. (3) Cybersecurity research needed to ensure that the patient data is securely held and de-identified appropriately for research.PICTURES will support 2 major exemplar research projects to guide and shape the underpinning resources. Exemplar one will develop a method to detect lung nodules and coronary artery calcification using hundreds of thousands of CT chest scans provided by the core programme. It will also predict the risk of getting lung cancer based upon the presence of lung nodules and the risk of cardiovascular disease based upon the presence of coronary artery calcification. This exemplar will work in partnership with an industrial partner, Aidence, to validate and test the method directly in NHS clinical workstations within the course of the programme.Exemplar 2 will predict individual risk of dementia in people with diabetes using MRI brain scans, genetic data and medical records. The most important variables will be found. The predictive tool will be validated on the large image dataset provided by the core programme.Both of our exemplars will determine new information from routinely collected data that would otherwise have been ignored. Predicting and therefore treating diseases at an early stage improves patient outcomes and reduces the cost to the NHS.PICTURES is truly interdisciplinary requiring expertise in Radiomics, AI, Cybersecurity, Software Engineering, Data Science, Data Governance and Medicine.

包括X射线、CT、MRI、超声和核医学扫描在内的临床成像是核心诊断技术。这些图像可以支持许多重要的研究领域，以改善任何或所有诊断，监测疾病进展和治疗反应。目前，大多数使用图像的研究都是基于专门为特定研究项目收集的图像。这些图像具有“研究”质量。这意味着它们是使用标准化程序以高分辨率捕获的，以减少图像的可变性。研究数据收集是昂贵的，所以研究往往是小的，谁参加研究的人是不同的，在正常的临床护理。因此，研究结果是否能转化为“真实的世界”的图像或患者往往是不确定的。每年在苏格兰，通过医院的常规检查产生数百万张临床图像，并存储在一个巨大的数据库中。自2010年以来，苏格兰国家成像数据库目前收集了约2300万张不同的图像。获取这些“真实的世界”图像对研究来说是极其有价值的，但也存在一些重大挑战。首先，所有数据都必须保密。其次，成像数据集非常大，这在技术上具有挑战性。第三，管理这些图像的软件针对NHS工作人员检索图像进行了优化，特别是针对个别患者的临床护理（例如，返回Jones夫人在2016年5月28日拍摄的扫描），而不是用于研究（例如，返回所有年龄在55岁至65岁之间的吸烟者的CT胸部扫描，其中使用了造影剂）。将交付什么？这个为期5年的计划将使安全访问定期收集的成像数据用于研究。利用先前研究赠款的基础块，PICTURES将扩展，扩展和增强创新的开源软件，以查询由爱丁堡大学安全托管的苏格兰国家成像数据库的研究副本，并提供数十万张图像的匿名提取用于研究。PICTURES还将开发这款软件，用于查询与邓迪大学安全托管的基因组数据相关的成像数据。核心计划中需要研究的主要领域有3个：（1）从真实世界的杂乱数据中构建复杂队列的数据科学研究。(2)在安全港环境中扩展和处理大数据所需的工程。(3)需要进行网络安全研究，以确保患者数据的安全保存和适当的去识别研究。PICTURES将支持2个主要的示范研究项目，以指导和塑造基础资源。范例一将开发一种方法，利用核心方案提供的数十万次CT胸部扫描来检测肺结节和冠状动脉钙化。它还将根据肺结节的存在预测患肺癌的风险，并根据冠状动脉钙化的存在预测心血管疾病的风险。该范例将与工业合作伙伴Aidence合作，在项目过程中直接在NHS临床工作站验证和测试该方法。Exemplar 2将使用MRI脑部扫描，遗传数据和医疗记录预测糖尿病患者患痴呆症的个体风险。最重要的变量将被发现。预测工具将在核心计划提供的大型图像数据集上进行验证。我们的两个样本将从常规收集的数据中确定新的信息，否则这些信息将被忽略。在早期阶段预测并治疗疾病可以改善患者的预后，并降低NHS的成本。PICTURES是真正的跨学科研究，需要放射组学、人工智能、网络安全、软件工程、数据科学、数据治理和医学方面的专业知识。

项目成果

Monitoring indirect impact of COVID-19 pandemic on services for cardiovascular diseases in the UK.

监测 COVID-19 大流行对英国心血管疾病服务的间接影响。

• DOI: 10.1136/heartjnl-2020-317870
• 发表时间: 2020-12
• 期刊: Heart (British Cardiac Society)
• 作者: Ball S;Banerjee A;Berry C;Boyle JR;Bray B;Bradlow W;Chaudhry A;Crawley R;Danesh J;Denniston A;Falter F;Figueroa JD;Hall C;Hemingway H;Jefferson E;Johnson T;King G;Lee KK;McKean P;Mason S;Mills NL;Pearson E;Pirmohamed M;Poon MTC;Priedon R;Shah A;Sofat R;Sterne JAC;Strachan FE;Sudlow CLM;Szarka Z;Whiteley W;Wyatt M;CVD-COVID-UK Consortium
• 通讯作者: CVD-COVID-UK Consortium

An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis

国家 COVID-19 胸部影像数据库概述：数据质量和队列分析

• DOI: 10.1101/2021.03.02.21252444
• 发表时间: 2021
• 作者: Cushnan D

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic.

• DOI: 10.1177/20552076211048654
• 发表时间: 2021-01
• 期刊: Digital health
• 影响因子: 3.9
• 作者: Cushnan D;Berka R;Bertolli O;Williams P;Schofield D;Joshi I;Favaro A;Halling-Brown M;Imreh G;Jefferson E;Sebire NJ;Reilly G;Rodrigues JCL;Robinson G;Copley S;Malik R;Bloomfield C;Gleeson F;Crotty M;Denton E;Dickson J;Leeming G;Hardwick HE;Baillie K;Openshaw PJ;Semple MG;Rubin C;Howlett A;Rockall AG;Bhayat A;Fascia D;Sudlow C;NCCID Collaborative;Jacob J
• 通讯作者: Jacob J

A National Network of Safe Havens: Scottish Perspective.

• DOI: 10.2196/31684
• 发表时间: 2022-03-09
• 期刊: Journal of medical Internet research
• 影响因子: 7.4
• 作者: Gao C;McGilchrist M;Mumtaz S;Hall C;Anderson LA;Zurowski J;Gordon S;Lumsden J;Munro V;Wozniak A;Sibley M;Banks C;Duncan C;Linksted P;Hume A;Stables CL;Mayor C;Caldwell J;Wilde K;Cole C;Jefferson E
• 通讯作者: Jefferson E

A National Network of Safe Havens: Scottish Perspective (Preprint)

全国安全港网络：苏格兰视角（预印本）

• DOI: 10.2196/preprints.31684
• 发表时间: 2021
• 作者: Gao C