A Novel Contour-based Machine Learning Tool for Reliable Brain Tumour Resection

Glioma is a type of aggressive brain tumor that has varying survival rates. In surgical operations, it can be difficult to strike a balance between reducing the risk of recurrence and preserving brain function. This is mainly due to the fact that manual tumor delineation is subjective, labor-intensive, and varies among practitioners, leading to unreliable segmentation and high recurrence rates. Therefore, there is an urgent need for reliable and automated tumor segmentation tools to assist surgeons in achieving an optimal balance between cancer control and functional preservation, reducing the time and resources spent by doctors, and providing quantitative data for future analysis. However, current automated segmentation approaches, including deep learning techniques, can be limited by the use of a deterministic boundary to delineate the tumor-infiltrating area, which can be problematic in cases with high uncertainty.

Therefore, the aim of this project is to develop a novel statistical machine learning approach that utilises partially labelled clinical information for more informative and accountable pre-surgery decision making in brain tumour resection. This new method is expected to provide more informative and nuanced guidance to surgeons, enhancing their ability to plan the surgery accurately, reducing the risk of tumour recurrence while preserving function and reliability. The new approach is expected to be generalised to other types of MRI-based cancer diagnostics and have the potential to significantly advance AI powered tumour resection and improve patient outcomes.

The research has two streams of beneficiaries:

(i) A large community of UK and international clinical surgeons that conduct brain tumor resection in traditional ways. The outcomes of this project would assist the pre-operative decision making for tumor resection, and substantially improve thousands of patients' quality of life after surgery, therefore achieve significant socioeconomic impact.

(ii) A large community of UK and international clinical academics/professionals who work on MRI-based tumor research. The novel statistical machine learning tool and idea generated by this project will be more widely applicable to other types of MRI-based cancer diagnostics and delineations. This will assist further investigation of accountable AI techniques for image-based tumor surgery.
A number of activities have been carefully designed to effectively engage with beneficiaries of this research. These activities include co-production and validation of knowledge with clinical academics, publishing of the results in leading academic journals/conferences, publicize up-to-date project advances and share open-source software on GitHub, and a workshop with field specialists and national academic and non-academic stakeholders in MRI-based tumor surgery.