CLASSICA: Validating AI in Classifying Cancer in Real-Time Surgery

Cancer of the lowermost part of the intestine (the rectum) is a common disease and both this disease and its treatment can have major impact on patients. Unless treated early, the disease can progress, spread to other parts of the body and ultimately cause death. Treatment often involves radical surgery, but this too has consequences and risks major complications. Best outcomes regarding cure with least impact depend on the disease being detected at an early stage as rectal cancer tends to start first as a non-cancerous polyp. The smallest of these precursor polyps can be easily removed during a routine colonoscopy but once the polyp grows over 2cm in size it is much harder to categorise correctly as the risk of it containing cancer somewhere in it increases markedly. If there is definitely cancer present in such a polyp it is best treated from the outset as a cancer with major surgery, but if there is definitely not a cancer in it then it can be removed from inside the bowel with minimally invasive techniques. Unfortunately, despite our current very best methods, up to 20% of tumours initially thought to be benign are found to be malignant only after they are excised We have previously shown that cancerous and non-cancerous tissues can be visually differentiated by analysis of their perfusion during the examination. For this we use a specific approved fluorescent dye, indocyanine green (ICG). ICG is commonly used in bowel surgery anyway to assess the blood supply to the bowel and has a very good safety profile. ICG is injected into the bloodstream during surgery and the rate at which it is taken up by various tissue types is detected by specific and approved cameras which can reveal fluorescence in tissue. We have previously found that the rate of uptake of this dye is different in cancer tissue compared to non-cancer tissue and have used artificial intelligence algorithms to measure this difference. However, we now need to ensure that this method can work also in other patients, in other centres and indeed in other countries to ensure it is indeed a valid and useful way of assessing rectal polyps. The goal of this observational study is to validate the use of fluorescence pattern analysis in the classification of rectal tumours. Patients enrolled in the study will attend for a visual examination of the rectal tumour in theatre as is standard practice. During this examination a video recording of the fluorescence perfusion will be taken following ICG administration. Patients will then have the tumour excised or treated as is standard of care by their surgeon. The video will later be analysed to determine the pattern of fluorescence perfusion within the tumour, and a classification will be assigned based on the pattern seen. All tumours that are excised are examined under the microscope by a pathologist to determine the final diagnosis. The fluorescence based classification will be compared to this pathological diagnosis to determine the accuracy of the method. So, patients will still have the exact same standard of care as currently happens, the hope is that in future this method can be developed to the point where it could be useful by means of a useable, accurate automated software process. If so, that will form the basis of another study in the future to look to see if it can guide or even replace biopsies and help with ensuring complete removal ('clear margins') after excision.

Sex

All

Minimum age

18 Years

Inclusion Criteria:

* Participants with a confirmed or suspected rectal polyp/tumour measuring greater than 2cm undergoing surgical intervention or assessment OR Patients with a known rectal cancer undergoing surgical intervention or assessment, including those post neo adjuvant therapy.
* Participant is willing and able to give informed consent for participation in the study. ● Male or Female, aged 18 years or above.
* Clinically fit for elective intervention

Exclusion Criteria:

* Female participant who is pregnant, lactating or planning pregnancy within three months 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 iodides
* Other contraindications to ICG including concurrent use of anticonvulsants, bisulphite containing drugs, methadone and nitrofurantoin.

• BACKGROUNDCahill RA, O'Shea DF, Khan MF, Khokhar HA, Epperlein JP, Mac Aonghusa PG, Nair R, Zhuk SM. Artificial intelligence indocyanine green (ICG) perfusion for colorectal cancer intra-operative tissue classification. Br J Surg. 2021 Jan 27;108(1):5-9. doi: 10.1093/bjs/znaa004. No abstract available. PMID 33640921
• 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
• DERIVEDBoland PA, McEntee PD, Cucek J, Erzen S, Niemiec E, Galligan M, Petropoulou T, Burke JB, Knol J, Hompes R, Tuynman J, Aigner F, Arezzo A, Cahill RA. Protocol for CLASSICA software as medical device trial. Minim Invasive Ther Allied Technol. 2025 Dec;34(6):441-446. doi: 10.1080/13645706.2025.2540482. Epub 2025 Aug 25. PMID 40852969
• DERIVEDMoynihan A, Hardy N, Dalli J, Aigner F, Arezzo A, Hompes R, Knol J, Tuynman J, Cucek J, Rojc J, Rodriguez-Luna MR, Cahill R. CLASSICA: Validating artificial intelligence in classifying cancer in real time during surgery. Colorectal Dis. 2023 Dec;25(12):2392-2402. doi: 10.1111/codi.16769. Epub 2023 Nov 6. PMID 37932915