CLASSICA: Validating AI in Classifying Cancer in Real-Time Surgery
NCT ID: NCT05793554
Last Updated: 2025-01-17
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
RECRUITING
600 participants
OBSERVATIONAL
2023-03-31
2027-03-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
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.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Generation of Organoids of Neuroendocrine Neoplasms of the Gastro-Entero-Pancreatic Tract Obtained From Patients Undergoing Surgery
NCT06519500
Patient Experiences Following Urinary Diversion as Part of Surgery for Advanced and Recurrent Rectal Cancer
NCT04715308
Snapshot Rectumcarcinoom 2016
NCT05539417
Symptomatic and Incidental RCC Detection
NCT07004426
Detection and Automatic Segmentation of Liver Nodules in Patients With Colorectal Adenocarcinoma
NCT04834596
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Endoscopic biopsies are notoriously inaccurate in up to 20% of such lesions (rectal cancers commence most often as adenomatous lesions and so superficial biopsies may miss a malignant focus). Mistakenly identifying a cancer as a benign lesion and treating it by local excision significantly worsens prognosis and compromises subsequent cancer surgery - including potentially converting a reconstructable site of resection (i.e. a lesion suitable for anterior resection) to an unreconstructable one (i.e. needing an abdominoperineal resection with permanent colostomy) and by seeding cancer cells into a deep margin or different plane, particularly as in the case for anteriorly positioned lesions. Additionally, transanal excision techniques continue to have relatively high rates of positive margins; this risks regrowth in benign lesions and limits effective local therapy for earliest stage cancers due to the presence of inapparent disease close to the main tumour bulk.
We have previously demonstrated, through the use of fluorescent indocyanine green (ICG), that perfusion is visibly different, between tumour and healthy tissue. This difference can be captured via infrared video and mathematical analysis can differentiate the perfusion pattern of malignant areas from any benign/normal tissue also visible in the same endoscopic view. In brief, the saturation of fluorescence in each region of interest (ie tumour or area of normal mucosa), can be measured from the recorded video using existing software developed by IBM. The change in fluorescence over time can be plotted on a curve, demonstrating the inflow, peak and outflow of ICG, which is depending on the perfusion patters within the region of interest. These curves differ depending on the tissue being examined and so can be used to classify benign from malignant tumours through calculating the slope of the uptake and area under the curve to measure outflow. Therefore, in a location (such as the rectum) where a cancer is suspected, analysis of the video can be used to differentiate between healthy and cancerous tissues. This discovery can be made exploited for clinical use by the application of AI methods including computer vision and machine learning. In essence, the fluorescence intensity of pixels displaying tissues of interest varies with blood flow (perfusion), when the blood is dyed with ICG and lit by near-infra-red (NIR) light. The intensity is captured over time, from multiple video frames, and this intensity is plotted as a curve. The intensity curves of tumour tissue are different from those of healthy tissue, and those of benign tumours are different from malignant tumours. Analysis of the curve features for each pixel in a region of interest can thus lead to a classification.
Such an AI system has been prototyped and trained in the Mater Hospital previously with videos from a population of Irish cancer patients from two regional centres, so that it can automatically identify malignant tumours and benign lesions from healthy tissue by their perfusion patterns. This prototype has previously demonstrated accuracy of \>80%.
In this study, we clinically validate the basic concept or method of classifying tissue by its fluorescence signal characteristics while also seeing if a device can be built on the basis of this that can extrapolate the data being generated from the videos by UCD staff. We also address the question of generalisability - can other surgeons use the system and get similar results from their specific patient cohorts? This will pave the way for future studies which are planned to determine the roles of biopsy (can the system enable optimal choice of biopsied tissue, and thus reduce biopsy error?); and tumour resection (can the system increase the completeness and accuracy of tumour resection?).
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
COHORT
PROSPECTIVE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
Rectal tumour without prior evidence of cancer
400 patients with a known rectal tumour that has not demonstrated evidence of cancer to date - may be benign or indeterminate on biopsy or without biopsy performed to date.
Patients in this cohort will undergo examination under anaesthesia as is standard of care. During this examination the pattern of fluorescence seen in NIR camera within the tumour will be observed following administration of ICG (dose 0.25mg/kg) and recorded. Following this, patients will continue with standard of care at the discretion of their surgeon.
The operative video will be uploaded to a secure cloud based system and annotated by the surgeon where further mathematical analysis will be carried out for the purposes of tissue classification.
No interventions assigned to this group
Rectal tumour previously confirmed as cancerous
200 patients with a rectal tumour that has proven previously to contain cancer. Both patients who have and have not undergone neoadjuvant therapy are suitable for inclusion in this group.
Patients in this group will undergo the same processes as the patients in cohort 1.
No interventions assigned to this group
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* 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
* 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.
18 Years
ALL
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Institut de recherche Contre Les Cancers de L'appareil Digestif
UNKNOWN
Stitchting EAES
UNKNOWN
Pintail LTD
UNKNOWN
Københavns Universitet
OTHER
Universita Degli Studi di Torino
UNKNOWN
Ziekenhuis Oost-Limburg Autonome Verzorginginstelling
UNKNOWN
Arctur Racunalniski Inzeniring Doo
UNKNOWN
Stitchting VUMC
UNKNOWN
Penn State University
OTHER
Krankenhaus der Barmherzigen Bruder Graz
UNKNOWN
Horizon Europe
UNKNOWN
Mater Misericordiae University Hospital
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Ronan Cahill
Professor of Surgery
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Ronan Cahill
Role: PRINCIPAL_INVESTIGATOR
University College Dublin
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Mater Misericordiae University Hospital
Dublin, Ireland, Ireland
Countries
Review the countries where the study has at least one active or historical site.
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
References
Explore related publications, articles, or registry entries linked to this study.
Cahill 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.
Boland 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 Aug 25:1-6. doi: 10.1080/13645706.2025.2540482. Online ahead of print.
Moynihan 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.
Related Links
Access external resources that provide additional context or updates about the study.
Official project website
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
101057321
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.