Computer Aided Diagnosis (CADx) for Colorectal Polyps Resect-and-Discard Strategy

NCT06062095 | Active Not Recruiting

• 1 other identifier: 2022.525
• Study Type: interventional
• Target: 1,764
• Locations: 1 country
• Sites: 1

Brief Summary

Colonoscopic removal of adenomatous polyps reduce both the incidence and mortality of colorectal cancer (CRC). The common clinical management of colorectal polyp detected during colonoscopy is to remove them and send for histopathology to determine the subsequent surveillance interval. More than 80% of polyps detected during screening or surveillance colonoscopy are diminutive (≤5mm). As the chance of diminutive polyps to harbor cancer or advanced neoplasia is low, leave-in-situ and resect-and-discard strategies using optical diagnosis are recommended for non-neoplastic polyps by the American Society for Gastrointestinal Endoscopy (ASGE) and the European Society for Gastrointestinal Endoscopy (ESGE) so as to reduce the financial burden of polypectomy and histopathology. The societies proposed leave-in-situ strategy if optical diagnosis can achieve a negative predictive value (NPV) of \>90% for rectosigmoid polyp and resect-and-discard if an agreement of more than 90% concordance with histopathology-based post-polypectomy surveillance interval can be achieved. However, optical diagnosis is operator dependent and most endoscopists are reluctant to adopt this strategy in routine practice because of the need of strict training and auditing and fear of incorrect diagnosis. In the past decade, with the exponential increase in computational power, reduced cost of data storage, improved algorithmic sophistication, and increased availability of electronic health data, artificial intelligence (AI) assisted technologies were widely adopted in various healthcare settings to improve clinical outcomes, especially the quality of colonoscopy in the area of gastroenterology. Real time use of computer-aided diagnosis (CADx) for adenoma using AI systems were developed and proven to be useful to help endoscopists to distinguish neoplastic polyps from non-adenomatous polyps. However, these studies only examined diminutive polyp but not polyp of larger size (\>5mm). They were conducted with small sample size of less than few hundred subjects and the study settings were open-label and non-randomized. The investigators aim to conduct a large scale randomized controlled trial to evaluate the performance of colorectal polyp characterization of all size polyps by real-time CADx using AI system against conventional colonoscopy with optical diagnosis.

Conditions

Colonic Polyp

Outcome Measures

Primary Outcomes (1)

• diagnostic accuracy of polyp histology in %

  the NICE classification (NICE I: non-neoplastic polyp: NICE II: adenoma; NICE III: invasive tumor) given by the AI CADx and conventional optical diagnosis will be compared against the polyp histopathology (reference standard)

  24 months

Secondary Outcomes (5)

• sensitivity (%)

  24 months

• specificity (%)

  24 months

• positive predictive value (%)

  24 months

• negative predictive value (%)

  24 months

• agreement in assigning post-polypectomy surveillance intervals with pathology-based diagnoses (%)

  24 months

Study Arms (2)

AI arm

EXPERIMENTAL

AI will be used to diagnose polyps

Procedure: AI-powered computer-aided diagnosis (CADx)

Control arm

NO INTERVENTION

Conventional colonoscopy without AI will be perform to diagnose polyps

Interventions

AI-powered computer-aided diagnosis (CADx) PROCEDURE

Real time AI will be used to diagnosis polyps found during colonoscopy.

AI arm

Eligibility Criteria

• Age: 40 Years - 90 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• undergoing elective colonoscopy with any indication (screening, surveillance or diagnostic) and complete colonoscopy (caecal intubation) with at least one colorectal polyp detected will be recruited

You may not qualify if:

• personal history of CRC or inflammatory bowel disease, prior colorectal surgery, receiving anticoagulant therapy
• lack of informed consent

Sponsors & Collaborators

• Chinese University of Hong Kong: lead
• Nanyang Technological University: collaborator
• The First Hospital of Jilin University: collaborator
• Shenzhen Hospital of Southern Medical University: collaborator
• Jiangsu Province Hospital of Traditional Chinese Medicine: collaborator
• Jiangsu Taizhou People's Hospital: collaborator

Study Sites (1)

Combined Endoscopy Unit, ALice Ho Miu Ling Nethersole Hospital

Hong Kong, Hong Kong

Location

MeSH Terms

Conditions

Colonic Polyps

Condition Hierarchy (Ancestors)

Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Masking Details: Recruited asymptomatic subjects will be randomized in a 1:1 ratio to undergo either AI or CC group. Randomization will be done upon caecal intubation. The randomization sequence will be generated in a concealed allocation fashion in block sizes of 10 for the participating centres. Patient recruitment and group assignment will be done independently by the study team members of each participating centre. Randomization will be stratified by endoscopist's colonoscopy experience (non-expert vs expert). Group assignments will be contained in sealed, opaque envelopes. This is a single-blinded randomization with enrolled patients being blinded to the result of their randomization while endoscopists are not blinded to the group assignment. Colonic polyp specimens will be evaluated by pathologists who are also blinded to the study group allocation and they are not aware of the diagnosis by AI.
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: September 6, 2023
• First Posted: October 2, 2023
• Study Start: September 29, 2023
• Primary Completion: November 30, 2025
• Study Completion (Estimated): December 31, 2026
• Last Updated: April 24, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

HK (1)