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

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.