AUGUR-AI - Indocyanine Green Fluorescence Angiography Representer

NCT06708819 | Not Yet Recruiting

• 1 other identifier: 1/378/2450
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Colorectal Surgery; Colorectal Resection; Anastomosis, Surgical; Fluorescence Guided Surgery; Indocyanine Green; Perfusion Imaging; Anastomotic Leaks

Keywords

Fluorescence; Indocyanine Green; Artificial Intelligence; Bowel Transection; Anastomotic Leak

Outcome Measures

Primary Outcomes (1)

• 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.

  Within 12 months of study commencement

Secondary Outcomes (1)

• Methods to display the information generated from the model to the operating surgeon

  Within 12 months of study commencement

Study Arms (1)

Participants undergoing resection of colorectal disease by segmental resection and anastomosis

Participants will be having indocyanine green fluorescence angiography (ICGFA) performed during their procedure as per routine practice of the operating surgeon. Once presented for ICGFA, with the surgeon's confirmation of the level clinically judged appropriate for transection, the segment(s) of interest is positioned in focus and the recording is begun. ICG is administered and the angiography is interpreted by the operating surgeon to guide the site of transection, which is indicated within the same recording. The video image will be subjected to real-time analysis by the AI model with a representative interpretation of the ICGFA signal generated by the model, however the surgeon will be blinded to this interpretation. The procedure will then continue and conclude as per routine practice.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• 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.
• In the Investigator's opinion, is able and willing to comply with all study requirements.
• Willing to allow his or her General Practitioner and consultant, if appropriate, to be notified of participation in the study.

You may not qualify if:

• The participant may not enter the study if ANY of the following apply:
• 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.
• Concurrent use of anticonvulsants, bisulphite containing drugs, methadone and nitrofurantoin.

Sponsors & Collaborators

• Mater Misericordiae University Hospital: lead
• 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

Study Sites (1)

Mater Misericordiae University Hospital

Dublin, Ireland

Location

Related Links

• A link to the article describing the exploratory work that resulted in the AUGUR study

MeSH Terms

Conditions

Anastomotic Leak

Condition Hierarchy (Ancestors)

Postoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Ronan Cahill

  Mater Misericordiae University Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Philip Mc Entee

CONTACT

+353 1 716 4000; philip.mcentee@ucd.ie

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 90 Days
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Surgery

Study Record Dates

• First Submitted: November 26, 2024
• First Posted: November 27, 2024
• Study Start: January 1, 2025
• Primary Completion: January 1, 2026
• Study Completion: January 1, 2026
• Last Updated: November 27, 2024

Record last verified: 2024-11

Data Sharing

• IPD Sharing: Will not share

Locations

IE (1)