Big data for small patients - Building "child-size" individual predictive models for life after childhood cancer

小型患者的大数据 - 为儿童癌症后的生活建立“儿童大小”的个体预测模型

• 批准号: EP/T028017/1
• 负责人: Marianne Aznar
• 金额: $ 152.37万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Fellowship
• 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=EP%2FT028017%2F1
• 关键词: Big; data; small; patients; Building

Many children with cancer have radiation treatment as part of their care. As for all cancer treatments, there is a risk of lasting side-effects such as learning problems and reduced growth. Research is needed to reduce such side-effects, which is particularly important for children because of their long life expectancy. Radiation treatment is planned to give maximal dose to the tumour and minimal doses to nearby healthy organs. However, even with the most advanced ways of giving radiation (e.g. using the new Proton Beam Therapy machine in Manchester) it will never be possible to avoid all healthy organs. This fellowship will find which parts of healthy organs are particularly damaged by radiation ('the important regions'). This knowledge would be incredibly useful when planning radiation treatments, because it is often possible to spare the important regions of an organ close to the tumour but not the whole organ. Hence, finding these important regions would be a step toward allowing reduced side-effects in many children with cancer. The cancer centre with the most and the best documented children's health data in the world is St Jude Children's Research Hospital in Memphis, Tennessee. Children treated with radiation at St Jude have a very detailed and complete follow-up, and their side effects are measured using the most up-to-date methods. In this project, we will:(1) Set up a joint data analysis structure to show that our new method can be used on St Jude's data; with this, we will discover, for example, regions of the brain where radiation causes the most learning problems. (2) Measure the changes in organ size and shape between children of different ages and sizes. For this we will use images from St Jude patients as well as from 500 healthy children in the United States, aged from 6 months to 16 years that were scanned every 2 years (we have permission to use these data for research). This information will help us make our method even more precise and able to find smaller "important regions". We will also use those images to build models of growth of the organs of interest (e.g. language center, hormone glands) in children, which will be useful for researchers studying other childhood diseases.(3) Develop new and better ways to measure side-effects, using all the follow-up information obtained about a child's health as they grow into adulthood. This will mean, for example, that we can use images showing the health of each child even though taken many years after treatment.This will be the first project of this kind focused on understanding side effects in children with cancer. In the future, the results from this project will help doctors give 'smarter' radiation treatments, with fewer side-effects. The models of growing organs will also be useful for research in other childhood diseases.

许多癌症儿童接受放射治疗作为他们护理的一部分。对于所有的癌症治疗，都有可能产生持久的副作用，如学习问题和生长迟缓。需要进行研究以减少这种副作用，这对儿童特别重要，因为他们的预期寿命很长。放射治疗计划对肿瘤给予最大剂量，对附近的健康器官给予最小剂量。然而，即使使用最先进的放射治疗方法（例如使用曼彻斯特的新质子束治疗机），也永远不可能避免所有健康器官。 该研究将发现健康器官的哪些部分特别受到辐射的损害（“重要区域”）。在计划放射治疗时，这些知识将非常有用，因为通常可以保留靠近肿瘤的器官的重要区域，而不是整个器官。因此，找到这些重要的区域将是减少许多癌症儿童副作用的一步。田纳西州孟菲斯的圣裘德儿童研究医院是世界上拥有最多和最好的儿童健康数据的癌症中心。在圣犹达接受放射治疗的儿童有一个非常详细和完整的随访，他们的副作用是使用最先进的方法测量。在这个项目中，我们将：（1）建立一个联合数据分析结构，以表明我们的新方法可以用于圣犹达的数据;通过这个，我们将发现，例如，辐射导致最多学习问题的大脑区域。(2)测量不同年龄和大小的儿童之间器官大小和形状的变化。为此，我们将使用来自St Jude患者以及美国500名健康儿童的图像，年龄从6个月到16岁，每两年扫描一次（我们有权将这些数据用于研究）。这些信息将帮助我们使我们的方法更加精确，并能够找到更小的“重要区域”。我们还将使用这些图像来建立儿童感兴趣器官（例如语言中心，激素腺）的生长模型，这将对研究其他儿童疾病的研究人员有用。(3)开发新的和更好的方法来衡量副作用，使用所有的后续信息获得有关儿童的健康，因为他们长大成人。这意味着，例如，我们可以使用图像显示每个孩子的健康状况，即使是在治疗多年后拍摄的。这将是第一个专注于了解癌症儿童副作用的项目。在未来，该项目的结果将帮助医生提供“更智能”的放射治疗，副作用更少。生长器官的模型也将有助于其他儿童疾病的研究。

项目成果

AUTOMATIC DETECTION OF FACIAL LOCATIONS TO MEASURE FACIAL ASYMMETRY AFTER PAEDIATRIC RADIOTHERAPY

自动检测面部位置以测量小儿放射治疗后的面部不对称度

• DOI: 10.1093/neuonc/noad147.059
• 发表时间: 2023
• 期刊: Neuro-Oncology
• 影响因子: 15.9
• 作者: Dronne C

OC-0777 Automated analysis of internal facial asymmetry on MRI in children

OC-0777 儿童 MRI 面部内部不对称的自动分析

• DOI: 10.1016/s0167-8140(23)08718-2
• 发表时间: 2023
• 期刊: Radiotherapy and Oncology
• 影响因子: 5.7
• 作者: Davey A

MO-0222 A neural network to create super-resolution MR from multiple 2D brain scans of paediatric patients

MO-0222 一种神经网络，可根据儿科患者的多次 2D 脑部扫描创建超分辨率 MR

• DOI: 10.1016/s0167-8140(23)08349-4
• 发表时间: 2023
• 期刊: Radiotherapy and Oncology
• 影响因子: 5.7
• 作者: Benitez-Aurioles J

The need for consensus on delineation and dose constraints of dentofacial structures in paediatric radiotherapy: Outcomes of a SIOP Europe survey.

• DOI: 10.1016/j.ctro.2023.100681
• 发表时间: 2023-11
• 期刊: Clinical and translational radiation oncology
• 影响因子: 3.1
• 作者: Davey A;Pan S;Bryce-Atkinson A;Mandeville H;Janssens GO;Kelly SM;Hol M;Tang V;Davies LSC;Siop-Europe Radiation Oncology Working Group;Aznar M
• 通讯作者: Aznar M

Outcomes of Patients Treated in the UK Proton Overseas Programme: Central Nervous System Group

• DOI: 10.1016/j.clon.2023.01.024
• 发表时间: 2023-04-07
• 期刊: CLINICAL ONCOLOGY
• 影响因子: 3.4
• 作者: Gaito, S.;Hwang, E. J.;Smith, E.
• 通讯作者: Smith, E.