Future of Colorectal Cancer Surgery

Colorectal cancer is the third most common cancer in the UK and Ireland, it is the second commonest cancer in both men and women. Very often the diagnosis is made by either endoscopy/colonoscopy and the surgical treatment is carried out by a minimally invasive approach ("Keyhole"surgery). Tissue samples gathered by either approach are sent to the pathologist to confirm the nature of their content. At present this takes some time (days) and so the information cannot guide the procedure being done or indeed any other investigations or processes that need implementation as soon as possible until the pathology process is completed. Fluorescence guided surgery uses an approved dye along with approved cameras to add more information regarding tissue characteristics then is available by normal viewing alone. It has already been shown to be associated with an improvement in safety related to healing after colorectal surgery and the investigators are sooning in a randomised trial examining this in rectal cancer to prove it. Whether or not this trial proves this or not, the ability to better understand tissue health during investigation/operation needs further examination and development. In this study, the investigators will examine the role of computer vision and machine learning in determining the nature of the tissue being seen in real-time additive to the surgeons' own opinion and experience. This is needed because the dynamic phases of fluorescence inflow into any tissue is difficult to interpret most especially when it relates to microvasculature as is present within a cancer site or deposit. By this means the investigators hope to better understand the dynamic perfusion in and out of tissue whether normal or abnormal and define signatures that can speed up and/or help inform the surgeon regarding the actual nature of the tissue being seen. The investigators will compare the data being generated with that already being captured with regard to standard pathology and radiology and other laboratory measures of clinical course. Tissue resected from a patient will also be examined in the laboratory under near-infrared microscopy and analysed for fluorescence intensity to understand where exactly and how much of the dye accumulates in specific regions of tissue. There are no new operations in this study and no new interventions are being made on the basis of the information being gathered- it's a comparative study to see how this added information can add value to interventionalists during surgery. There are four collaborating groups involved in this research consortium, two are commercial partners as they add value in both this advanced field of analytics and in the ensuring a clinical business case is included so that findings of this work can be useful for patients.

Sex

All

Minimum age

18 Years

Inclusion Criteria:

* • Participant is willing and able to give informed consent for participation in the study.

  * Male or Female, aged 18 years or above.
  * Clinical features suspicious of or diagnosed with colorectal neoplasia or other colorectal disease requiring segmental resection with anastomosis.
  * No prior allergy to indocyanine green or iodine.
  * If female and of potential child-bearing age, have a negative pregnancy test at time of study participation.
  * 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.

Exclusion Criteria:

* • Female 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.

• DERIVEDBoland PA, MacAonghusa P, Singaravelu A, McEntee PD, Cucek J, Erzen S, Aigner F, Arezzo A, Burke JP, Hompes R, Tuynman JB, Neary PM, Cahill RA. Artificial intelligence classification of rectal neoplasia by endoscopic fluorescence perfusion analysis. Sci Rep. 2026 Jan 6;16(1):4761. doi: 10.1038/s41598-026-35233-x. PMID 41495165
• DERIVEDHardy NP, MacAonghusa P, Dalli J, Gallagher G, Epperlein JP, Shields C, Mulsow J, Rogers AC, Brannigan AE, Conneely JB, Neary PM, Cahill RA. Clinical application of machine learning and computer vision to indocyanine green quantification for dynamic intraoperative tissue characterisation: how to do it. Surg Endosc. 2023 Aug;37(8):6361-6370. doi: 10.1007/s00464-023-09963-2. Epub 2023 Mar 9. PMID 36894810