Ignite Creation Date:
2024-06-16 @ 11:50 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06447012
Status:
RECRUITING
Last Update Posted:
2024-06-06
First Post:
2024-05-07
Brief Title:
Artificial Intelligence Development for Colorectal Polyp Diagnosis
Sponsor:
Kings College Hospital NHS Trust
Organization:
Kings College Hospital NHS Trust
Study Overview
Official Title:
Development of a Novel Real Time Computer Assisted Colonoscopy Diagnostic Tool for Colorectal Polyps Lesion Diagnosis and Personalised Patient Management
Status:
RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None