According to those behind the technique, it will be used during surgery to help determine how much of a patient’s tissue is affected by cancer and should be removed. The process will provide surgeons with real-time feedback, allowing for greater precision when differentiating normal from abnormal tissue.
During surgery, the outer boundary of tissue identified for removal is called the ‘surgical margin’. Imaging, a surgeon's experience, plus touch and feel all feed into this calculation. However, during keyhole surgery using laparoscopic, endoscopic or robotic operations, surgeons cannot rely on touch to determine tissue characteristics.
The new collaboration will allow mechanical measurements to be taken inside and around the surgical target which will be interpreted using a set of so-called 'mechanical intelligence' algorithms. According to the National Robotarium, the data will provide clinicians with a clear indication of a tissue’s disease status and determine how much tissue to remove during the operation.
The research team will work alongside industry partners IntelliPalp Dx and CMR Surgical along with clinicians working in the Western General Hospital in Edinburgh.
“This new technique will offer surgeons a quantitative, real-time, reliable and evidence-based method for determining the optimal surgical margin to make when removing a tumour,” said research lead Dr Yuhang Chen, from the National Robotarium.
“Surgeons operating along a ‘keyhole’ or using techniques for minimally invasive surgery need to identify different structures or diseased areas, even when these look very similar. Our work is aimed at identifying the optimum margin in cancer surgery, to allow the removal of a tumour together with enough tissue to ensure the cancer is completely removed, but without excess being lost.
“We’re bringing together expertise from laser manufacturing, fibre-optic sensors, micromechanical probing and computational modelling to create a mechanical 'imaging' probe capable of detecting cancerous tissue that can be used with a standard minimally-invasive surgery instrument. Coupled to this, we’ll be building a 'mechanically-intelligent' data modelling framework and will integrate it into the probe operation for tumour identification and surgical margin assessment. This will effectively eliminate the margin of error for surgeons, giving them confidence that they have removed the correct amount of tissue during the operation itself and reduce the need for further invasive surgery for patients.”