Clinical trial NCT06447012: Artificial Intelligence Development for Colorectal Polyp Diagnosis
Official title: Development of a Novel Real Time Computer Assisted Colonoscopy Diagnostic Tool for Colorectal Polyps: Lesion Diagnosis and Personalised Patient Management
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Polyp of Colon
Colorectal Polyp
Keywords: Artificial Intelligence
Computer assisted diagnosis (CADx)
Colorectal cancer
Colorectal polyp
Machine learning
Lead sponsor: King's College Hospital NHS Trust
Sponsor class: OTHER
First posted: 2024-06-06
Enrollment: 4000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All adult (over 18 years) presenting for screening or symptomatic colonoscopy

Brief summary: Accurate classification of growths in the large bowel (polyps) identified during colonoscopy is imperative to inform the risk of colorectal cancer. Reliable identification of the cancer risk of individual polyps helps determine the best treatment option for the detected polyp and determine the appropriate interval requirements for future colonoscopy to check the site of removal and for further polyps elsewhere in the bowel.

Current advanced endoscopic imaging techniques require specialist skills and expertise with an associated long learning curve and increased procedure time. It is for these reasons that despite being introduced in clinical practice, uptake of such techniques is limited and current methods of polyp risk stratification during colonoscopy without Artificial intelligence (AI) is suboptimal. Approximately 25% of bowel polyps that are removed by major surgery are analysed and later proved to be non-cancerous polyps that could have been removed via endoscopy thus avoiding anatomy altering surgery and the associated risks. With accurate polyp diagnosis and risk stratification in real time with AI, such polyps could have been removed non-surgically (endoscopically). Current Computer Assisted Diagnosis (CADx, a form of AI) platforms only differentiate between cancerous and non cancerous polyps which is of limited value in providing a personalised patient risk for colorectal cancer. The development of a multi-class algorithm is of greater complexity than a binary classification and requires larger training and validation datasets. A robust CADx algorithm should also involve global trainable data to minimise the introduction of bias. It is for these reasons that this is a planned international multicentre study.

The Investigators aim to develop a novel AI five class pathology prediction risk prediction tool that provides reliable information to identify cancer risk independent of the endoscopists skill.

These 5 categories are chosen because treatment options differ according to the polyp type and future check colonoscopy guidelines require these categories

Detailed description: The use of artificial intelligence in computer-assisted detection (CADe) to detect polyps (pre-cancerous growths) during colonoscopy is gaining increasing interest and acceptance with multiple devices already in the mainstream market. The Investigator know already from work in other countries that detecting more polyps results in a reduced risk of bowel cancer for the patient having the procedure, in the years following their colonoscopy (ie. pre-cancerous growths were detected and removed). This has formed the basis of national bowel cancer screening programmes. With increased detection of colorectal polyps, there is a growing need to correctly identify the nature of the polyp to inform the risk of colorectal cancer with the polyp detected and also the potential future risk to the patient. Accurate polyp diagnosis is also required to determine the correct mode or removal-whether this does require removal at all (leading to conservation of costs and resources in a challenging current climate), whether endoscopic removal is possible and if so by what procedure, whether surgery is required.

Published data demonstrates that approximately one quarter of surgically removed colorectal polyps with patients undergoing major surgery were benign and therefore major surgery could have been avoided with these polyps removed endoscopically reducing the risk of complication and organ preservation for the patient.

Current polyp diagnosis techniques involve the use and interpretation of specialist dyes and magnification endoscopes which come with gaining expertise expertise with an associated learning curve and increased procedure time. It is for these reasons that despite being introduced in clinical practice, uptake of such techniques is limited and current methods of polyp risk stratification during colonoscopy without AI is suboptimal.

Current polyp diagnosis AI (CADx) algorithms are limited to smaller classification Current CADx platforms differentiate between cancerous and non-cancerous polyps which is of limited value in providing a personalised patient risk for colorectal cancer. The development of a multiclass algorithm is of greater complexity than a binary classification and requires larger training and validation datasets. A robust CADx algorithm should also involve global trainable data to minimise the introduction of bias. It is for these reasons that this is a planned international multicentre study

Prospective collection of data:

This study will be conducted alongside usual patient care, but will require research staff to enter data onto a secure web-based report form (REDCAP database). This means that participants will undergo exactly the same procedure, with no differences and no extra visits or data, than would have otherwise have occurred. Participants will be those patients that have been scheduled to have a colonoscopy for the standard reasons. Patients will be invited in the usual way for colonoscopy.

They may - where possible - be sent the PIS with their appointment letter (up to 6 weeks in advance). On arrival in the endoscopy unit, they will be approached by a member of the research team and given a copy of the PIS to read - up to an hour before their procedure. They will be provided face-to-face information and explanation, prior to written consent to allow their data to be collected in the database. As the study does not require any change or additional procedures, The investigator feel that an initial approach on arrival into the endoscopy unit will provide sufficient, appropriate time to consent, even if the PIS has not been read in advance (although it will be sent if possible). The only additional consideration will be the consent to recording of the video (no patient identifiable data will be transferred as part of this aspect).

Once the colonoscopy has been completed, there will be no additional visits.

Eligibility criteria: Inclusion Criteria:

- Above 18 years at inclusion Symptomatic or screening colonoscopy

Exclusion Criteria:

* Unable to provide informed consent.
* Colitis Associated Dysplasia
* Polyps at surgical anastomosis sites
* Pregnancy

Primary outcomes:
measure: To achieve an overall accuracy of 85% for the five-classification lesion prediction algorithm.
timeFrame: 24 months
measure: Positive and negative predicted value
timeFrame: 24 months
Secondary outcomes:
measure: Interobserver agreement of the endoscopists' prediction of histology of polyps during the annotation process.
timeFrame: 36 months
measure: Sub-analysis of the polyp characteristics focused on different gender and ethnicity.
timeFrame: 36 months
measure: This will be a sub analysis of an AI algorithm that is trained to predict polyp histology using the prospective data cohort.
timeFrame: 36 months
measure: Learned effects of AI augmented endoscopy on endoscopist optical diagnosis
timeFrame: 36 months
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: King's College Hospital NHS Foundation Trust
Study officials:
References: