Aims of the Project:
- Implement learning-based registration to curate a spatially-normalised dataset of MR images previously used to deliver stereotactic radiosurgery to brain metastases
- Develop data-driven deep learning frameworks to automatically detect and segment brain metastases while allowing for interactive corrections
- Develop imaging biomarkers to predict tumour response and behaviour following treatment
Approximately 25,000 patients are diagnosed with a brain tumour every year in the UK. Brain metastases affect up to 40% of patients with extracranial primary cancer. Furthermore, although there are presently no reliable data, metastatic brain tumours are thought to outnumber primary malignant brain tumours by at least 3:1. Patients with brain metastases require individualized patient management and may include surgery, stereotactic radiosurgery, fractionated radiotherapy and chemotherapy, either alone or in combination.
Brain metastases most commonly occur in patients with lung, breast, kidney, melanoma or bowel cancer. Surgery, stereotactic radiosurgery (SRS), and whole-brain radiotherapy (WBRT) continue to be the mainstay of treatment for brain metastasis. In particular, SRS has emerged as an important modality for treating intracranial metastases. The most important criteria for choosing SRS or WBRT is the overall number of metastases. SRS is the preferred treatment modality for patients with 1-10 metastases although the number of clinicians treating multiple metastases is increasing and several centres routinely treat over 10-20+ metastases. The planning of SRS treatment is significantly impacted by the number of metastases. The presence of small micro-metastases can greatly increase the length of planning time, as a result of having to carefully scrutinise the imaging data to detect and segment all the tumours. Furthermore, lifelong radiological follow-up is required for all patients with brain metastases, even after they undergo treatment, placing an additional burden on healthcare resources. An automated segmentation tool could significantly improve clinical workflow during the planning of SRS. By using an AI segmentation tool as an initialisation step in a clinician-driven interactive process, we will improve workflow and operational efficiency.
Artificial intelligence (AI) refers to computing technologies that mimic processes associated with human intelligence. We have previously developed a fully-automated AI framework to segment a vestibular schwannoma (another type of brain tumour) from MRI achieving state-of-the-art results.
This project now aims to develop deep learning models to: 1) detect and automatically segment brain metastases using MRI while allowing for user-driven corrections; and 2) develop composite clinical and imaging biomarkers to predict tumour response and behaviour following gamma knife treatment. The dataset and learning from this project will provide the foundation of an ambitious research programme aiming at translating such tools in clinical practice by making the AI tools flexible enough to seamlessly integrate into the clinical workflow. This will require the design of interactive corrections, the provision of interpretability means, and the development of proven AI trustworthiness features.
A broad understanding of the mathematical basis of deep learning and a keen interest in developing expert knowledge in neuroanatomy, neuropathology and radiosurgical treatment will be essential for the successful PhD candidate. Implementation of the research will follow open research principles to maximise the impact of the research within the multiple target audiences: computational researchers, clinical researchers, research and development engineers, etc.