Personalising Image-Guidance for Paediatric Abdominal Radiotherapy with Artificial Intelligence

1)简要描述研究背景，包括潜在影响质子束治疗的靶点和组织保留能力使其成为改善儿童腹部癌症(如神经母细胞瘤、肾母细胞瘤和盆腔肉瘤)的长期预后的一种有前途的方法。然而，质子治疗的实施具有挑战性，因为胃肠道(GI)系统内容物的日常变化以及患者体重的波动会导致密度变化，这可能会导致肿瘤剂量不足和/或向健康器官过度。室内成像可以监控日常的解剖变化，并可能触发室内治疗适应的需要。额外的体积成像通常与额外的辐射暴露有关，这对儿童特别有害，因为相关的长期增加了患新癌症的风险。因此，当今世界各地的中心对于儿童最合适的影像引导方案缺乏共识，因为在获得的信息和曝光成本之间实现平衡是至关重要的。该项目旨在开发儿科放射治疗环境中的个性化影像引导方案。我们将结合人工智能和不同类型的室内成像来监测腹部质子治疗中的内部日常解剖变化，并提出新的图像引导工作流程，以减少相关的长期风险。2)目的和目的本项目的目的是开发用于儿科质子治疗的新的图像引导方案，以确保在治疗腹部癌症时高精度地提供辐射，同时减少不必要的放射诊断暴露。具体目标是：-研究在室内可用的成像方式的最佳选择，结合人工智能图像分析方法来检测内部的三维解剖变化，通过模拟研究。-量化提供的质子束治疗剂量的解剖变化的影响。-建议、开发和测试个性化图像引导方案的红绿灯系统。-从成像剂量及其相关的长期风险方面评估所建议方法的好处。3)研究方法的新颖性学生将开发一种应用于儿童放射治疗图像引导工作流程的人工智能方法。这是一种针对研究不足的临床队列的新方法。4)与EPSRC的战略和研究领域保持一致本研究与EPSRC的医疗保健技术主题以及医学成像和人工智能技术的研究领域保持一致。5)参与这项研究的任何公司或合作者来自伦敦大学学院医院NHS基金会信托基金的临床儿科肿瘤学家正在合作进行这项研究。

1) Brief description of the context of the research including potential impactThe target and tissue-sparing capabilities of proton beam therapy make it a promising modality to improve long-term outcomes in paediatric abdominal cancers, such as neuroblastoma, Wilms' tumour, and pelvic sarcomas. However, proton treatments are challenging to deliver because day-to-day variations in the contents of the gastrointestinal (GI) system, as well as weight fluctuations of the patient result in density changes that may lead to underdosage of the tumour and/or overshoot into healthy organs.In-room imaging allows to monitor day-to-day anatomical variation and may allow triggering the need for treatment adaption in-room. Extra volumetric imaging is often associated with additional radiation exposure that is particularly detrimental in children, due to the associated long-term increased risk of developing new cancers. Consequently, nowadays there is a lack of consensus between centres worldwide on what the most adequate image-guided protocolsfor children are, since it is of utmost importance to achieve a balance between information gained and exposure costs.This project aims to develop personalised image-guidance protocols in paediatric radiotherapy settings. We will combine artificial intelligence and different types of in-room imaging to monitor internal day-to-day anatomical change in bdominal proton beam therapy and propose novel workflows for image-guidance with fewer associated long-term risks.2) Aims and ObjectivesThe aim of this project is to develop novel image guidance protocols for paediatric protonbeam therapy that ensure radiation is delivered with high accuracy in the treatment ofabdominal cancers while reducing unnecessary diagnostic exposures to radiation.The specific objectives are to:- Investigate the optimal choice of imaging modalities available in room, combined withartificial intelligence image analysis methodology to detect internal, three-dimensionalanatomical change, through simulation studies.- Quantify the impact of anatomical change in the proton beam therapy dose delivered.- Propose, develop, and test a traffic light system for personalised image-guidedprotocols.- Evaluate the benefits of the methodology proposed in terms of imaging doses and theirassociated long-term risks.3) Novelty of Research MethodologyThe student will develop an artificial intelligence methodology applied to image-guidance workflows tailored to radiotherapy treatments in children. This is a novel methodological approach targeted to an under-researched clinical cohort.4) Alignment to EPSRC's strategies and research areasThis research is aligned with EPSRC's Theme of Healthcare Technologies and with the Research Areas of Medical Imaging and Artificial Intelligence Technologies.5) Any companies or collaborators involvedClinical paediatric oncologists from University College London Hospitals NHS Foundation Trust are collaborating on this research.