Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06447012 |
| Other study ID # |
323988 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
May 4, 2024 |
| Est. completion date |
May 30, 2026 |
Study information
| Verified date |
June 2024 |
| Source |
King's College Hospital NHS Trust |
| Contact |
Shraddha B Gulati, MBBS PHD MRCP |
| Phone |
+442032996044 |
| Email |
shraddha.gulati[@]nhs.net |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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
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.
