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

Summary

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.

Conditions

• Rectum Neoplasm
• Rectum Polyp
• Rectal Cancer

Sponsors & Collaborators

• Institut de recherche Contre Les Cancers de L'appareil Digestif

  collaborator UNKNOWN

• Stitchting EAES

  collaborator UNKNOWN

• Pintail LTD

  collaborator UNKNOWN

• Københavns Universitet

  collaborator OTHER

• Universita Degli Studi di Torino

  collaborator UNKNOWN

• Ziekenhuis Oost-Limburg Autonome Verzorginginstelling

  collaborator UNKNOWN

• Arctur Racunalniski Inzeniring Doo

  collaborator UNKNOWN

• Stitchting VUMC

  collaborator UNKNOWN

• Penn State University

  collaborator OTHER

• Krankenhaus der Barmherzigen Bruder Graz

  collaborator UNKNOWN

• Horizon Europe

  collaborator UNKNOWN

• Mater Misericordiae University Hospital

  lead OTHER

Principal Investigators

• Ronan Cahill · University College Dublin

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2023-03-31

Primary Completion

2027-03-31

Completion

2027-03-31

Countries

• Ireland

Study Locations