Brain tumour surgeons currently remove cancer tissue while trying to avoid damaging healthy brain areas, but 41% of operations cause new neurological problems because existing tools cannot reliably distinguish tumour from functional tissue in real time. This project builds a single platform that combines two imaging technologies already tested in the operating room. Multispectral imaging identifies tumour boundaries and highlights eloquent brain regions at the whole-organ scale, while a microscopic probe scans residual cancer cells at the cellular level. An artificial intelligence system fuses both signals to guide the surgeon continuously during resection. The goal is to remove more tumour—improving the <20% five-year survival rate for high-grade gliomas—without causing permanent deficits. If successful, the platform could transform brain tumour surgery by giving surgeons real-time, multi-scale tissue characterisation that current tools cannot provide. The same approach could later be adapted for other cancers where precise margin control matters. For patients, more complete resection means longer survival and better quality of life; for health systems, it means fewer repeat surgeries and reduced long-term disability costs.
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Brain tumours kill more children and adults under the age of 40 than any other cancer and five-year survival is <20%. Increasing the extent of tumour resection improves survival in high grade gliomas (HGG), but should not be pursued at the expense of neurological function. 41% of primary tumour resections result in postoperative neurological complications, with the main cause being resection of eloquent tissue. Current technologies used intraoperatively to identify tumour and evaluate brain function have significant limitations. The corollary is that technologies that allow for simultaneous identification of cancer tissue and assessment of neurological functionality, can enable more precise resection and improve patient outcomes. Multispectral Imaging (MSI) has shown promise in neurosurgical oncology for intraoperative margin delineation at macroscopic scale. Our current operative pilot study on 47 patients has verified that MSI can also detect structural differences between functional and non-functional brain areas. Probe-based Confocal Laser Endomicroscopy (pCLE) allows identification of residual brain tumours at microscopic scale and improves resection rates. The integration of macro- and microscopic intraoperative techniques for brain tumour identification may allow for more accurate tumour margin delineation. This proposal will develop a multimodal platform for intraoperative navigation and in vivo real-time tissue characterisation in neurosurgery. Macroscopic guidance with MSI will assist the surgeon to efficiently remove the bulk of the tumour and highlight eloquent brain tissue while microscopic screening with pCLE will be deployed to characterise tumour margins, help to define the infiltration of the tumour, improving the extent of resection. The proposed fusion of multimodal tissue characteristics will guide resection across scales and enable surgical excision at cellular level whilst preventing permanent neurological deficits. The proposed platform is in response to the current clinical demand for bringing into the operating theatre the latest technologies in intraoperative imaging and Artificial Intelligence (AI) to allow for real-time tissue analysis and provide surgical guidance during tumour resection. The project will advance surgical outcome and safety of brain tumour surgery and also transform surgery for other cancers, too. It will grant surgeons exceptional navigational and cognitive cues for tissue characterisation, assisting them to reduce surgical errors. More complete tumour removal will improve patient quality of life and life expectancy, with obvious impact on society and the economy.
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