Surgery can effectively treat colorectal cancer, but it is a complex procedure with risks and complications. Surgeons often rely on cameras to visually guide their instruments during operations, especially in minimally invasive ("keyhole") and endoscopic procedures. The camera is connected to a computer and generates the internal scene onto a display screen, which the surgeon looks at throughout the procedure, helping them make informed decisions throughout the operation. Fluorescence-guided surgery uses a particular type of camera that can detect images in both normal light and in the near-infrared range. To work, it needs the administration of an agent called indocyanine green to a patient and then the camera can see if the agent is in the tissue of interest to the operation at the time of the surgery. In this way, decisions regarding blood supply ("perfusion") can be helped, especially related to safety in joining together portions of tissue after removal of disease. The equipment and agent are approved for use in this way and have very good safety profiles. Many international studies have already demonstrated that the use of fluorescence-guided surgery is associated with lower rates of leaks when disease bowel segments are removed, and the healthy ends are joined back together.
Previous work we have done has shown that sophisticated computing methods can learn to interpret the fluorescence patterns to a similar standard as a surgeon who is very experienced in fluorescence-guided surgery.
In this study, we aim to assess whether the computer system we have developed work in real-time, in theatre to provide a reliable interpretation of the fluorescence pattern, that would match how an expert would interpret the same pattern. The system's analysis will not impact on the operation; instead, video images will be recorded, processed and analysed by our computer system. The results of the interpretation will not be shown to the operating surgeon during the procedure to avoid any impact on decision-making.
Participant is willing and able to give informed consent for participation in the study.
Aged 18 years or above.
Colorectal disease requiring segmental resection with anastomosis.
Participant has clinically acceptable laboratory results, including liver function tests.
Participant who is pregnant, lactating or planning pregnancy during the course of the study.
Significant renal or hepatic impairment.
Any other significant disease or disorder which, in the opinion of the Investigator, may either put the participants at risk because of participation in the study, or may influence the result of the study, or the participant's ability to participate in the study.
Allergy to intravenous contrast agent or indocyanine green.
Adult patients with colorectal disease undergoing segmental resection via an open, laparoscopic or robotic approach will be approached for inclusion in the trial. Participants must be able and willing to comply with the terms of the protocol. Such patients will be identified from referral letters, outpatient clinics, endoscopy lists, and multidisciplinary cancer meetings. Patients will undergo standard preoperative work-up, including colonic visualisation by either colonoscopy or CT colonogram, staging CT scan of the chest, abdomen and pelvis, MRI of the rectum, and assessment of fitness for surgery as per standard practice. The optimal management of the patient will be determined based on institutional protocols.
Cohort
Prospective
Not listed
Accuracy of AI interpretation relative to intra-operative decision made by operating surgeon
Accuracy of the initial AI-based system, with concurrent development and testing of new AI models using the data generated from participating sites. The performance of the algorithm will be analysed at both object and pixel level. At the object level, accuracy will be assessed based on whether the actual stapler placement by the operating surgeon falls within the boundaries of the "expert" zone predicted by the algorithm. Pixel level analysis will measure the overlap between the predicted and actual zones.
Methods to display the information generated from the model to the operating surgeon
Surgical team members may be required to interact with a graphical user interface for annotation of the recorded imagery. Accessibility and usability of this interface will be assessed through user surveys and analysis of video quality measures.
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Mater Misericordiae University Hospital
Lead sponsor
Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
Collaborator
Humanitas Research Hospital IRCCS, Rozzano-Milan
Collaborator
Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Collaborator
University Hospital, Geneva
Collaborator
The Leeds Teaching Hospitals NHS Trust
Collaborator
Oxford University Hospitals NHS Trust
Collaborator
Frimley Park Hospital NHS Trust
Collaborator
IRCCS San Raffaele
Collaborator
UZ Leuven
Collaborator
University College Dublin
Collaborator
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