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Not yet recruitingNCT06792292Delta-AIUpdated Jan 24, 2025

Artificial Intelligence-Assisted Colonoscopy in Colorectal Cancer Screening in a General Hospital

An interventional study of conventional colonoscopy procedure and artificial intelligence-assisted colonoscopy procedure in Artificial Intelligence, Colonic Adenoma and Colonic Neoplasms, sponsored by Chirec. Not yet recruiting at 1 site in Belgium. Open to participants aged 45 Years to 74 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-01-24.

Sponsored by Chirec · Not applicable, Interventional, and Diagnostic

Phase
Not applicable
Study type
Interventional
Enrollment
765
Allocation
Randomized
Ages
45 Years to 74 Years
Sex
All
01

Study summary

Cancer can develop in the colon, or large bowel. Examination of the colon with a tube fitted with a camera is called a colonoscopy.

Colonoscopy allows detection of small growths in the colon, called "polyps". Polyps can often be removed during colonoscopy. Some of these polyps are called adenomas and can become cancer after several years.

A good colonoscopy aims to find and take out as many of these polyps as possible.

A quality indication of colonoscopy is the "adenoma detection rate" (ADR). It should be high, meaning many polyps are detected and taken out.

New artificial intelligence devices to assist colonoscopy seem to increase the ADR, and maybe help prevent cancer even better than normal colonoscopy.

The goal of this clinical trial is to compare the ADR when using standard colonoscopy to the ADR with artificial intelligence (AI)-assisted colonoscopy.

Read the detailed description

The colon is a part of the bowel where colon cancer can develop.

It is possible to prevent colon cancer by doing a screening test called a colonoscopy.

The colonoscopy procedure allows detection of "polyps" which can often be removed during the procedure. Some of these polyps are called adenomas and can become cancer after several years.

A good colonoscopy aims to find and take out as many of these polyps as possible.

A quality indication of colonoscopy is the "adenoma detection rate" (ADR). It should be high, meaning many polyps are detected and taken out.

New artificial intelligence devices to assist colonoscopy seem to increase the ADR, and maybe help prevent cancer even better than normal colonoscopy.

The goal of this clinical trial is to compare the ADR when using standard colonoscopy to the ADR with artificial intelligence (AI)-assisted colonoscopy.

Patients who are scheduled to have screening colonoscopy and who agree to participate, and are aged 45 years or more, will be randomly assigned to receive either standard colonoscopy or AI-assisted colonoscopy.

The main objective of this study is the difference in the ADR between a conventional colonoscopy procedure and an AI-assisted colonoscopy procedure.

Secondary objectives will compare the 2 groups (conventional colonoscopy and AI-assisted colonoscopy) regarding polyp size, polyp quantity, polyp histology (laboratory analysis of the polyp that was taken out), polyp dysplasia grade (how close the polyp is to cancer), polyp location in the colon, endoscopist experience (older or younger doctor), time of day and colonoscopy preparation quality (how clean the bowel is).

With these results we can show that AI-assisted colonoscopy is useful or not to help better prevent colon cancer.

02

Conditions studied

  • Artificial Intelligence
  • Colonic Adenoma
  • Colonic Neoplasms
  • Colonic Polyp
  • Colonoscopy

Keywords

  • colo-rectal cancer screening
  • artificial intelligence assisted colonoscopy
03

Who can participate

Ages eligible
45 Years to 74 Years
Sexes eligible
All
Accepts healthy volunteers
Yes

Inclusion criteria

  • Patient (woman or man) candidate for a screening colonoscopy - Age: 45 to 74 years included
  • Absence of inflammatory bowel disease
  • Absence of significant digestive symptoms indicating colonoscopy (i.e. screening is the only indication for the examination)
  • Patient able to understand the concept of the study and agreeing to participate

Exclusion criteria

Exclusion criteria:

  • Patient outside the inclusion age
  • All exclusion criteria for a colonoscopy.
  • The indication for colonoscopy is not simple screening; for example, assessment of anemia, rectal bleeding, weight loss or abdominal pain.
  • Patient's refusal to participate, or patient's inability to understand the study concept
  • Any patient with major psychological or psychiatric disorders.
04

Study design

Phase
Not applicable
Primary purpose
Diagnostic
Allocation
Randomized
Intervention model
Parallel assignment
Masking
None (open label)
Enrollment
765 participants (estimated)

Study arms

  • Active comparator
    CCP: conventional colonoscopy procedure

    The conventional colonoscopy arm subjects will undergo a screening colonoscopy without assistance from artificial intelligence.

    Other: conventional colonoscopy procedure

  • Active comparator
    ACP: artificial intelligence-assisted colonoscopy procedure

    The artificial intelligence-assisted colonoscopy arm subjects will undergo a screening colonoscopy with assistance from an artificial intelligence module.

    Other: artificial intelligence-assisted colonoscopy procedure

Interventions

  • Otherconventional colonoscopy procedure

    Study subjects in this interventional arm will undergo conventional colonoscopy.

  • Otherartificial intelligence-assisted colonoscopy procedure

    Study subjects in this interventional arm will undergo colonoscopy done with a commercially-available module that uses artificial intelligence to highlight suspected polyps on the screen during colonoscopy. This module also attempts to characterize the detected polyp as adenomatous or not. The detection and characterization of polyps is in real time, during the procedure.

05

What researchers measure

Primary outcomes

  1. Adenoma Detection Rate in Conventional versus Artificial Intelligence-Assisted Colonoscopy

    The main objective of this study is the difference in the detection rate of colorectal adenomas in individuals over 45 years old during colonoscopies for colon cancer screening between a conventional colonoscopy procedure (CCP) and a colonoscopy procedure with AI (ACP).

    Time frame: 1 day

Secondary outcomes

  1. The difference in the detection rate of colorectal adenomas according to size by group (5 mm/6-9 mm/>10 mm) between a conventional colonoscopic procedure (CCP) and a colonoscopy procedure with AI (ACP).

    Detection rate of diminutive, small, and large polyps will be compared between a conventional colonoscopic procedure (CCP) and a colonoscopy procedure with AI (ACP).

    Time frame: 1 day

  2. The difference in the detection rate of colorectal adenomas according to the number per group (n=1-2/n= 3-10/n >10) between a conventional colonoscopic procedure (CCP) and a colonoscopy procedure with AI (ACP).

    The number of adenomas detected during the procedure will be compared between a conventional colonoscopic procedure (CCP) and a colonoscopy procedure with AI (ACP).

    Time frame: 1 day

  3. The difference in the detection rate of colorectal adenomas based on histology by group (hyperplastic/conventional adenomas/serrated adenomas/adenocarcinoma) between a conventional colonoscopic procedure (PCC) and a colonoscopy procedure with AI.

    Adenomas have different histologic types. After obtaining histology, the detection rate of the various histologic types will be compared between a conventional colonoscopic procedure (CCP) and a colonoscopy procedure with AI (ACP).

    Time frame: 1 month

  4. The difference in detection rate of colorectal adenomas based on dysplastic grade by group

    The difference in detection rate of colorectal adenomas based on dysplastic grade by group (conventional or serrated adenomas/low-grade dysplasia/high-grade dysplasia/adenocarcinoma) or Narrow Band Imaging International Colorectal Endoscopic (NICE) (stages 1, 2 or 3) classification or Japan Narrow Band Imaging Expert Team (JNET) classification (stages 1, 2A and 2B, or 3) between a conventional colonoscopy procedure (CCP) and a colonoscopy procedure with AI (ACP).

    Time frame: 1 day

  5. The difference in the detection rate of colorectal adenomas depending on the location by group (rectum/left colon/transverse colon/right colon) between a conventional colonoscopic procedure (CCP) and a colonoscopy procedure with AI (ACP).

    Location of adenomas will be compared between a conventional colonoscopic procedure (CCP) and a colonoscopy procedure with AI (ACP).

    Time frame: 1 day

  6. The difference in the detection rate of colorectal adenomas according to experience by group of colonoscopists

    The difference in the detection rate of colorectal adenomas according to experience by group (Colonoscopists aged 45-55 years/56-65 years/\>66 years) between a conventional colonoscopic procedure (CCP) and a conventional colonoscopic procedure (CCP) colonoscopy with AI (ACP).

    Time frame: 1 day

  7. The difference in the detection rate of colorectal adenomas depending on the time of day .

    The difference in the detection rate of colorectal adenomas depending on the time of day (before 11:00/between 11:00 and 15:00/after 15:00) and the number of colonoscopies (Colonoscopy from 1 to 6 per half-day) per group (hyperplastic/conventional adenomas/scalloped adenomas/adenocarcinoma) between a conventional colonoscopic procedure (PCC) and a colonoscopy procedure with AI (ACP).

    Time frame: 1 day

  8. The difference in the detection rate of colorectal adenomas according to colonic preparation by group (Boston Score 9/6-8/<6) between a conventional colonoscopic procedure (PCC) and a colonoscopy procedure with AI (ACP).

    The difference in the adenoma detection rate according to the quality of colonic preparation will be compared between a conventional colonoscopic procedure (CCP) and a colonoscopy procedure with AI (ACP).

    Time frame: 1 day

06

Study locations

1 site
  • Hopital Delta Chirec
    Auderghem, Brussels 1160, Belgium
07

References and documents

Publications

  • Spadaccini M, Marco A, Franchellucci G, Sharma P, Hassan C, Repici A. Discovering the first US FDA-approved computer-aided polyp detection system. Future Oncol. 2022 Apr;18(11):1405-1412. doi: 10.2217/fon-2021-1135. Epub 2022 Jan 27. PubMed 35081745 ↗
  • Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, Ferrara E, Spadaccini M, Alkandari A, Fugazza A, Anderloni A, Galtieri PA, Pellegatta G, Carrara S, Di Leo M, Craviotto V, Lamonaca L, Lorenzetti R, Andrealli A, Antonelli G, Wallace M, Sharma P, Rosch T, Hassan C. Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial. Gastroenterology. 2020 Aug;159(2):512-520.e7. doi: 10.1053/j.gastro.2020.04.062. Epub 2020 May 1. PubMed 32371116 ↗
  • Gupta S, Lieberman D, Anderson JC, Burke CA, Dominitz JA, Kaltenbach T, Robertson DJ, Shaukat A, Syngal S, Rex DK. Recommendations for Follow-Up After Colonoscopy and Polypectomy: A Consensus Update by the US Multi-Society Task Force on Colorectal Cancer. Gastrointest Endosc. 2020 Mar;91(3):463-485.e5. doi: 10.1016/j.gie.2020.01.014. Epub 2020 Feb 7. No abstract available. PubMed 32044106 ↗
  • Kaminski MF, Thomas-Gibson S, Bugajski M, Bretthauer M, Rees CJ, Dekker E, Hoff G, Jover R, Suchanek S, Ferlitsch M, Anderson J, Roesch T, Hultcranz R, Racz I, Kuipers EJ, Garborg K, East JE, Rupinski M, Seip B, Bennett C, Senore C, Minozzi S, Bisschops R, Domagk D, Valori R, Spada C, Hassan C, Dinis-Ribeiro M, Rutter MD. Performance measures for lower gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) quality improvement initiative. United European Gastroenterol J. 2017 Apr;5(3):309-334. doi: 10.1177/2050640617700014. Epub 2017 Mar 16. PubMed 28507745 ↗
  • Hassan C, Antonelli G, Dumonceau JM, Regula J, Bretthauer M, Chaussade S, Dekker E, Ferlitsch M, Gimeno-Garcia A, Jover R, Kalager M, Pellise M, Pox C, Ricciardiello L, Rutter M, Helsingen LM, Bleijenberg A, Senore C, van Hooft JE, Dinis-Ribeiro M, Quintero E. Post-polypectomy colonoscopy surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Guideline - Update 2020. Endoscopy. 2020 Aug;52(8):687-700. doi: 10.1055/a-1185-3109. Epub 2020 Jun 22. PubMed 32572858 ↗
  • Brenner H, Hoffmeister M, Stegmaier C, Brenner G, Altenhofen L, Haug U. Risk of progression of advanced adenomas to colorectal cancer by age and sex: estimates based on 840,149 screening colonoscopies. Gut. 2007 Nov;56(11):1585-9. doi: 10.1136/gut.2007.122739. Epub 2007 Jun 25. PubMed 17591622 ↗
  • Saftoiu A, Hassan C, Areia M, Bhutani MS, Bisschops R, Bories E, Cazacu IM, Dekker E, Deprez PH, Pereira SP, Senore C, Capocaccia R, Antonelli G, van Hooft J, Messmann H, Siersema PD, Dinis-Ribeiro M, Ponchon T. Role of gastrointestinal endoscopy in the screening of digestive tract cancers in Europe: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy. 2020 Apr;52(4):293-304. doi: 10.1055/a-1104-5245. Epub 2020 Feb 12. PubMed 32052404 ↗
  • Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, Antonelli G, Yu H, Areia M, Dinis-Ribeiro M, Bhandari P, Sharma P, Rex DK, Rosch T, Wallace M, Repici A. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021 Jan;93(1):77-85.e6. doi: 10.1016/j.gie.2020.06.059. Epub 2020 Jun 26. PubMed 32598963 ↗

Individual participant data

Plan to share: Yes — Individual Participant Data will include anonymized demographic information, clinical outcomes, laboratory results, and recorded adverse events. Access to the data will be available to qualified researchers, with requests submitted by contacting one of the investigators. The investigators will employ strict data protection measures, ensuring all shared data is de-identified and in compliance with applicable data protection laws. Access to the individual participant data will be contingent on the signing of a data sharing agreement that outlines the intended use of the data and adherence to ethical guidelines.

Supporting information: Study protocol, Sap, Icf, Csr

08

Registry details

Key details

Study ID
NCT06792292
Lead sponsor
Chirec
Responsible party
Erik Francois (Principal Investigator, Chirec) — Principal investigator
First posted
Jan 24, 2025
Start date
Feb 1, 2025 (estimated)
Primary completion
Dec 31, 2026 (estimated)
Completion
Jun 1, 2027 (estimated)
Last update
Jan 24, 2025

Study contacts

Erik Francois, M.D.
Contact
drfrancois.chirec@gmail.com
32 495 77 00 60
Philippe Langlet, M.D.
Contact
philippe.langlet@chirec.be
32 2 434 8105
Denis Franchimont, M.D., PhD
study chair · Chirec

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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