Rectal polyps \>2cm in size (affecting c. 10,000 patients a year in Europe alone) represent a considerable clinical challenge. While smaller polyps can be addressed by routine endoscopic polypectomy and frank clinical cancer will advance through a traditional cancer surgery paradigm, polyps of this size have the option of being locally excised, intact by transanal endoscopic resection (also referred to as Transanal Minimally Invasive Surgery, TAMIS). This is the treatment of choice in this site due to its ability to provide a single complete unfragmented specimen versus other modalities (e.g. endoscopic submucosal resection). While the technology and training to enable transanal resection has become much more available over the past decade (especially TAMIS) meaning more patients can have large benign lesions and even some early rectal cancers excised in their local specialist centres, the major brake now on such care is patient selection: i.e. how to tell if a given patient has a benign polyp or a cancer in advance of its resection.

Endoscopic biopsies are notoriously inaccurate in up to 20% of such lesions (rectal cancers commence most often as adenomatous lesions and so superficial biopsies may miss a malignant focus). Mistakenly identifying a cancer as a benign lesion and treating it by local excision significantly worsens prognosis and compromises subsequent cancer surgery - including potentially converting a reconstructable site of resection (i.e. a lesion suitable for anterior resection) to an unreconstructable one (i.e. needing an abdominoperineal resection with permanent colostomy) and by seeding cancer cells into a deep margin or different plane, particularly as in the case for anteriorly positioned lesions. Additionally, transanal excision techniques continue to have relatively high rates of positive margins; this risks regrowth in benign lesions and limits effective local therapy for earliest stage cancers due to the presence of inapparent disease close to the main tumour bulk.

We have previously demonstrated, through the use of fluorescent indocyanine green (ICG), that perfusion is visibly different, between tumour and healthy tissue. This difference can be captured via infrared video and mathematical analysis can differentiate the perfusion pattern of malignant areas from any benign/normal tissue also visible in the same endoscopic view. In brief, the saturation of fluorescence in each region of interest (ie tumour or area of normal mucosa), can be measured from the recorded video using existing software developed by IBM. The change in fluorescence over time can be plotted on a curve, demonstrating the inflow, peak and outflow of ICG, which is depending on the perfusion patters within the region of interest. These curves differ depending on the tissue being examined and so can be used to classify benign from malignant tumours through calculating the slope of the uptake and area under the curve to measure outflow. Therefore, in a location (such as the rectum) where a cancer is suspected, analysis of the video can be used to differentiate between healthy and cancerous tissues. This discovery can be made exploited for clinical use by the application of AI methods including computer vision and machine learning. In essence, the fluorescence intensity of pixels displaying tissues of interest varies with blood flow (perfusion), when the blood is dyed with ICG and lit by near-infra-red (NIR) light. The intensity is captured over time, from multiple video frames, and this intensity is plotted as a curve. The intensity curves of tumour tissue are different from those of healthy tissue, and those of benign tumours are different from malignant tumours. Analysis of the curve features for each pixel in a region of interest can thus lead to a classification.

Such an AI system has been prototyped and trained in the Mater Hospital previously with videos from a population of Irish cancer patients from two regional centres, so that it can automatically identify malignant tumours and benign lesions from healthy tissue by their perfusion patterns. This prototype has previously demonstrated accuracy of \>80%.

In this study, we clinically validate the basic concept or method of classifying tissue by its fluorescence signal characteristics while also seeing if a device can be built on the basis of this that can extrapolate the data being generated from the videos by UCD staff. We also address the question of generalisability - can other surgeons use the system and get similar results from their specific patient cohorts? This will pave the way for future studies which are planned to determine the roles of biopsy (can the system enable optimal choice of biopsied tissue, and thus reduce biopsy error?); and tumour resection (can the system increase the completeness and accuracy of tumour resection?).