Development of a risk model towards personalised screening for radiation-induced breast cancer using the national Breast Screening After Radiotherapy

使用国家放射治疗后乳房筛查开发针对放射诱发乳腺癌的个性化筛查的风险模型

• 批准号: 2452584
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2452584
• 关键词: Development; risk; model; towards; personalised

Radiotherapy uses radiation (x-rays, protons) to kill cancer cells, and is an important part of cancer treatment for many patients. However, radiation may also result in new cancers occurring over 15 years after the initial cancer treatment. For women diagnosed with cancer as children or young adults, this may lead to a high risk of developing breast cancer many years after treatment. In this study, we will use artificial intelligence (machine learning) to develop advanced image processing and analytical methods to refine risk prediction of radiation-induced breast cancer. We will use data from the national Breast Screening after Radiotherapy Dataset (BARD) developed and hosted in Manchester. BARD is a first-of-its-kind national registry, developed in Manchester following wide consultation and in collaboration with colleagues at Public Health England. It includes data from approximately 6,500 women across England at risk of breast cancer as a result of previous radiotherapy. The project will involve processing 2D and 3D medical images (CT, radiographs, mammograms) and combining these imaging features with other risk factors (radiation dose to the breast, breast volume, age, etc...) to develop a personalised risk model.The student will work at the intersection of medical physics, radiobiology, advance image analysis and epidemiology. They will benefit from a highly multidisciplinary supervisory team within the BARD research group, including medical physicists, computer scientists, and oncologists. They will be actively involved in collaborations at the national and international level in the field of cancer survivorship / childhood and young adult cancers. The study could ultimately lead to personalized guidelines for breast cancer screening and improved quality of life and outcomes for cancer survivors.

放射治疗使用辐射（X射线，质子）杀死癌细胞，是许多患者癌症治疗的重要组成部分。然而，辐射也可能导致新的癌症发生在最初的癌症治疗后15年以上。对于在儿童或年轻人时被诊断患有癌症的女性来说，这可能会导致治疗多年后患乳腺癌的高风险。在这项研究中，我们将使用人工智能（机器学习）来开发先进的图像处理和分析方法，以改进辐射诱发乳腺癌的风险预测。我们将使用在曼彻斯特开发和托管的全国放疗后乳腺筛查数据集（BARD）的数据。BARD是第一个此类国家登记处，在广泛咨询并与英格兰公共卫生部门的同事合作后在曼彻斯特开发。它包括来自英格兰各地约6，500名女性的数据，这些女性由于先前的放射治疗而具有乳腺癌风险。该项目将涉及处理2D和3D医学图像（CT、X光片、乳房X光片），并将这些成像特征与其他风险因素（乳房辐射剂量、乳房体积、年龄等）相结合。开发一个个性化的风险模型。学生将在医学物理学，放射生物学，先进的图像分析和流行病学的交叉点工作。他们将受益于BARD研究小组内高度多学科的监督团队，包括医学物理学家，计算机科学家和肿瘤学家。他们将积极参与癌症生存/儿童和青年癌症领域的国家和国际合作。这项研究最终可能导致乳腺癌筛查的个性化指南，并改善癌症幸存者的生活质量和结果。