Surgeons currently operate on the liver through a large open incision because they cannot see what lies beneath the surface during keyhole surgery. Around 150,000 patients worldwide each year could benefit from liver resection for cancer, but only about 10% currently qualify for the less invasive laparoscopic approach—typically those with small tumours on the liver’s edge. Larger or deeper tumours near major blood vessels are considered too risky because the surgeon cannot see hidden vessels or the tumour’s exact position in real time. This project builds a smart surgical system that overlays the location of internal anatomy onto the surgeon’s view, accurate to within 3 millimetres. It also adds a method to characterise the tissue type ahead of cutting. The team will test the system in a pig model and then in at least 25 patients. If successful, the same approach could be adapted for other laparoscopic operations, including partial removal of the pancreas, kidney, or gallbladder. The immediate impact would be safer, less painful surgery for more patients, shorter hospital stays, and lower costs for the health service.
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In the UK approximately 1800 liver resections are performed annually for primary or metastatic cancer. However this is a major global health problem and 150,000 patients per year could benefit from liver resection. Currently only about 10% of patients are considered suitable for laparoscopic liver resection, mainly those with small cancers on the periphery of the liver. Laparoscopic resection potentially has significant benefits in reduced pain and cost savings due to shorter hospital stays. Larger lesions and those close to major vascular/biliary structures are generally considered high risk for the laparoscopic approach. Pre-operative imaging modalities can clearly identify the location of vessels and the tumour. We propose an image guided surgery system capable of presenting the current location of hidden vessels and the tumour in real time to the surgeon enabling safe laparoscopic procedures. The system will also include novel methods to characterise tissue ahead of resection. We will deliver a clinical system that will show key anatomy accurate to better than 3 mm. The system will be validated in a porcine model, in-vivo, and applied in at least 25 patients. The system will subsequently be adapted for other laparoscopic operations including partial pancreatectomy, partial nephrectomy and cholecystectomy.
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