High grade gliomas (HGG) and low grade gliomas (LGG) are the commonest CNS cancers, with LGGs accounting for 6.4% of all adult cases (Ostrom 2019). Despite LGGs typically being slow-growing, over 70% of them transform into higher-grade tumours or become aggressive within a decade (Jooma 2019). Median survival for LGG patients spans 5.6 to 13.3 years (Brown 2019). Gross total resection (GTR) improves 5-year LGG survival rates from 60% to 90% when compared to subtotal resection. However, GTR (>96% tumour removal) is frequently not achieved because despite advanced techniques being available, surgeons are unable to clearly visualise the tumour and its boundaries in real-time during surgery.~There is an acute need to improve outcomes for affected brain tumour patients. Patients undergoing surgery have significantly improved outcomes and increased life expectancy if complete tumour removal is achieved. However, close to 30% of patients are left with residual tumour tissue after surgery. Successful surgery indeed mandates maximal safe tumour removal: surgeons need to avoid damaging sensitive areas that undertake vital functions and preserve crucial nerves and blood vessels. Even with the most advanced current techniques, it is not possible to always identify tumour and critical structures reliably during surgery. Furthermore, because one cannot objectively measure the blood supply and oxygenation of brain tissue during surgery, it is difficult to judge if injury is being caused during the operation.~To address the pressing clinical need of improved surgical precision and patient safety during low grade glioma surgery, we aim to develop an imaging system capable of quantitative wide-field fluorescence imaging for seamless real-time surgical guidance. This project aims to improve patient survival by delivering a precise assistive tool for neurosurgeons performing LGG surgery by evaluating this device in patients undergoing glioma surgery.
The purpose of this study is to obtain images of brain tumours during surgery using a new type of surgical camera. The study will assess how the information obtained from the images during surgery matches the removed tissue. Data will also be used to develop the system's key computer-processing features. This will enable real-time information to be given to the surgeon whilst they are performing the procedure and has the potential to make neurosurgery safer and more precise.