Award Title: An artificial intelligence disease monitoring tool for patients with brain tumours
Funder: Department of Health and Social Care

Abstract: Background Slow growing brain tumours (such as meningiomas or low-grade gliomas) are usually managed with repeated follow up imaging. This is both expensive for the NHS and causes anxiety to the patient who lives knowing their brain tumour may grow. It is not possible to predict speed of growth or really measure this in a clinical setting. Aims and Objectives The aim of this project is to be able to tell patients if their tumour will grow and how quickly. The objective of the project is to develop an AI software prototype for efficient brain tumour monitoring and progression prediction. The prototype will re-align heterogeneous MRI scans, segment the tumour MRI scans, probabilistic tumour progression prediction with reliable uncertainty quantification, and enhanced clinical interpretation. Work plan and methods used There will be three work packages (WP) designed in this project: WP1 will address the challenges of tumour segmentation in heterogeneous clinical datasets, where transferable deep learning models are lacking due to the significant distribution shift of MRI acquired from different scanners. WP2 will focus on the prediction for tumour progression based on the segmented tumour, where the robustness of the prediction model relies on both the dynamic modelling of progression and reliable uncertainty quantification. Explainable AI methodology will also be developed in WP2 to improve clinical interpretation of the proposed AI models. WP3 will work with the NIHR Brain Injury MIC, Cambridge Enterprise and the Office for Translational Research to start commercialising this product and, with PPI involvement, inform future stages of development. Timeline for delivery: This is a one year project to develop a prototype. Anticipated impact and dissemination: By the end of this stage we will have a working prototype and a commercialisation plan that can proceed to acquiring CE marking.

Lay Abstract: How is this research important?   There are many people who live with a slowly-growing brain tumours – such as meningiomas or low-grade gliomas. These tumours are frequently found when they have a brain scan for unrelated problems. Patients will have regular brain scans while living with the knowledge their tumour may grow and cause them problems. Waiting for the results of these scans causes a lot of worry for patients and their families.      How will this research help?   Currently we don t know which tumours may grow (so need careful watching) and which will not. We want to use artificial intelligence to work out which patients have tumours that are likely to grow. If we could even measure the volume of a tumour and see how it changes over time would be a great help. Even better would be to find those tumours that are high risk of growing before they do.       What will we do?   Our plan is to take the standard MRI images that we use, enhance them using methods we have already developed. We will then use artificial intelligence to outline the tumour and measure its volume. We plan to study scans that have already been performed on patients, so we know which tumours grew and which did not. This will let us train the computer to work out which patient is at high risk of their tumour growing.      How are we involving patients and the public?   We hope this study will be the first step of using this method to guide us treating patients. We will hold a workshop to guide our research in the future. We are especially interested asking patients and the public how they would accept decisions about their treatment that come from a computer program. We also wish to understand the best way of showing the results to patients.      What we hope we will get from this research   By the end of this study we will have a working method to accurately measure tumour volume and predict patients at high risk of tumour growth. We hope we can use this to work out how often and individual patient is scanned – so that those at risk are carefully watched, and those at low risk have less worry.

Research Activity Codes: 7.3 MANAGEMENT AND DECISION MAKING
Health Categories: CANCER AND NEOPLASMS