Comparison of Polyp Detection and False Alarm Rates in Water Exchange and Air Insufflation Colonoscopy

NCT ID: NCT04727814

Last Updated: 2021-04-05

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

250 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-08-01

Study Completion Date

2021-04-10

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

Water exchange (WE) improves adenoma detection rate (ADR) but missed polyps occur due to human limitations. Computer-aided detection (CADe) improves polyp detection and can overcome human omissions, but a limiting factor is feces and air bubbles related false alarms (FA). WE provides salvage cleansing and can potentially reduce FA. The investigators compared the additional polyp detection rate (APDR) and false alarm rate (FAR) by CADe between WE and air insufflation.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Worldwide colorectal cancer (CRC) is the second most common cancer in women and the third in men. Early detection and removal of the colon polyps (cancer precursors) reduce the incidence of CRC. However, interval colon cancers still occur within 3-5 years after colonoscopy among patients of colonoscopists with low adenoma detection rate (ADR), defined as the proportion of patients with at least one adenoma. ADR was widely variable, suggesting that some adenomas were missed. Twenty six percent of adenomas were missed during tandem examination reported in a recent meta-analysis. Missed adenomas accounted for about 58% of interval cancers. Adenomas are more likely to be missed in the right colon than in other segments because of their flat morphology and hiding behind the accentuated folds and curvatures. Innovations in colonoscopy to increase ADR and decrease adenoma miss rate (AMR) hold the potential to reduce interval cancers.

The consensus statements in a recent modified Delphi review confirmed water exchange (WE) as a standardized insertion method produced less insertion pain, better bowel cleanliness and higher ADR than gas insufflation. It is characterized by infusing water to guide insertion in an airless lumen and almost simultaneous suctioning of the infused water during insertion, aiming at near-complete removal of the infused water and debris upon cecal intubation. Although an RCT with tandem examination showed WE significantly decreased right colon adenoma miss rate (rAMR) compared with CO2 insufflation (18.0% \[33/183\] vs. 34.6% \[62/179\], P = 0.0025), a considerable percentage of polyps in the right colon were still overlooked.

In recent years, the field of machine learning and artificial intelligence has made remarkable progress, and an increasing number of publications showed improved polyp detection rate (PDR) and ADR using computer-aided detection (CADe). CADe can detect polyps overlooked by the colonoscopist due to human limitations of inattention or inexperience. However, one major drawback of current CADe systems is false alarms (FAs), or false positives (FPs). Usually triggered by bubbles and fecal debris, FAs might distract the endoscopists with potential unfavorable effect on ADR. One study reported a FP rate of up to 60%.

In an overview on applying deep learning algorithms and WE in colonoscopy to improve adenoma detection, the authors noted that WE could enhance the performance of artificial intelligence (CADe) by improving bowel cleanliness and thus the exposure of polyps. In a follow-up review, the authors reported that artificial intelligence might mitigate operator-dependent factors that limited the potential of WE, while WE might provide the platform to optimize the performance of artificial intelligence by increasing bowel cleanliness and improving visualization, Therefore, the strengths of WE and artificial intelligence may complement the weaknesses of each other to maximize adenoma detection.

One of our recently completed studies compared right colon ADR evaluated by a blinded endoscopist using either air insufflation or WE for insertion, with all the colonoscopies video recorded (NCT02737514). We developed and applied a CADe system to detect the polyps in the videos. The current report is a proof of principle study to test the hypothesis that WE could yield a significantly higher additional PDR (APDR) and reduce false alarms rate (FAR) as compared to air insufflation in the right colon.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Colon Polyp

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Water exchange with computer-aided detection system

Computer-aided detection system overlaid videos with water exchange colonoscopy method

Computer-aided detection system overlaid colonoscopy videos analysis

Intervention Type OTHER

Analysis of computer-aided detection system overlaid videos from colonoscopies performed with water exchange or air insufflation method.

Air insufflation with computer-aided detection system

Computer-aided detection system overlaid videos with air insufflation colonoscopy method

Computer-aided detection system overlaid colonoscopy videos analysis

Intervention Type OTHER

Analysis of computer-aided detection system overlaid videos from colonoscopies performed with water exchange or air insufflation method.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Computer-aided detection system overlaid colonoscopy videos analysis

Analysis of computer-aided detection system overlaid videos from colonoscopies performed with water exchange or air insufflation method.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Patients aged 40 to 80 years old, undergoing screen, diagnostic or surveillance colonoscopy were enrolled.

Exclusion Criteria

* Patients were excluded in case of having colonoscopy in the past 3 years, renal failure, previous colonic resection, scheduled for polypectomy, partial intake of bowel preparation, American Society of Anesthesiology (ASA) Risk Class 3 or higher, and lack of written informed consent.
Minimum Eligible Age

40 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

University of California

OTHER

Sponsor Role collaborator

National Chiayi University

UNKNOWN

Sponsor Role collaborator

Dalin Tzu Chi General Hospital

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Chia Pei Tang

Gastroeneterologist

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Chia Pei Tang

Role: PRINCIPAL_INVESTIGATOR

Dalin Tzu Chi General Hospital

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Chia Pei Tang

Chiayi City, Chiayi, Taiwan

Site Status

Countries

Review the countries where the study has at least one active or historical site.

Taiwan

References

Explore related publications, articles, or registry entries linked to this study.

Cheng CL, Kuo YL, Hsieh YH, Tang JH, Leung FW. Comparison of Right Colon Adenoma Miss Rates Between Water Exchange and Carbon Dioxide Insufflation: A Prospective Randomized Controlled Trial. J Clin Gastroenterol. 2021 Nov-Dec 01;55(10):869-875. doi: 10.1097/MCG.0000000000001454.

Reference Type BACKGROUND
PMID: 33074950 (View on PubMed)

Hsieh YH, Tseng CW, Hu CT, Koo M, Leung FW. Prospective multicenter randomized controlled trial comparing adenoma detection rate in colonoscopy using water exchange, water immersion, and air insufflation. Gastrointest Endosc. 2017 Jul;86(1):192-201. doi: 10.1016/j.gie.2016.12.005. Epub 2016 Dec 15.

Reference Type BACKGROUND
PMID: 27988288 (View on PubMed)

Leung FW, Hsieh YH. Artificial intelligence (computer-assisted detection) is the most recent novel approach to increase adenoma detection. Gastrointest Endosc. 2021 Jan;93(1):86-88. doi: 10.1016/j.gie.2020.07.059. No abstract available.

Reference Type BACKGROUND
PMID: 33353642 (View on PubMed)

Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, Lei L, Li L, Guo Z, Lei S, Xiong F, Wang H, Song Y, Pan Y, Zhou G. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020 Apr;5(4):343-351. doi: 10.1016/S2468-1253(19)30411-X. Epub 2020 Jan 22.

Reference Type BACKGROUND
PMID: 31981517 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 32371116 (View on PubMed)

Barua I, Vinsard DG, Jodal HC, Loberg M, Kalager M, Holme O, Misawa M, Bretthauer M, Mori Y. Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis. Endoscopy. 2021 Mar;53(3):277-284. doi: 10.1055/a-1201-7165. Epub 2020 Sep 29.

Reference Type BACKGROUND
PMID: 32557490 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 32598963 (View on PubMed)

Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019 Oct;68(10):1813-1819. doi: 10.1136/gutjnl-2018-317500. Epub 2019 Feb 27.

Reference Type BACKGROUND
PMID: 30814121 (View on PubMed)

Hsieh YH, Leung FW. An overview of deep learning algorithms and water exchange in colonoscopy in improving adenoma detection. Expert Rev Gastroenterol Hepatol. 2019 Dec;13(12):1153-1160. doi: 10.1080/17474124.2019.1694903. Epub 2019 Nov 30.

Reference Type BACKGROUND
PMID: 31755802 (View on PubMed)

Cadoni S, Ishaq S, Hassan C, Falt P, Fuccio L, Siau K, Leung JW, Anderson J, Binmoeller KF, Radaelli F, Rutter MD, Sugimoto S, Muhammad H, Bhandari P, Draganov PV, de Groen P, Wang AY, Yen AW, Hamerski C, Thorlacius H, Neumann H, Ramirez F, Mulder CJJ, Albeniz E, Amato A, Arai M, Bak A, Barret M, Bayupurnama P, Cheung R, Ching HL, Cohen H, Dolwani S, Friedland S, Harada H, Hsieh YH, Hayee B, Kuwai T, Lorenzo-Zuniga V, Liggi M, Mizukami T, Mura D, Nylander D, Olafsson S, Paggi S, Pan Y, Parra-Blanco A, Ransford R, Rodriguez-Sanchez J, Senturk H, Suzuki N, Tseng CW, Uchima H, Uedo N, Leung FW. Water-assisted colonoscopy: an international modified Delphi review on definitions and practice recommendations. Gastrointest Endosc. 2021 Jun;93(6):1411-1420.e18. doi: 10.1016/j.gie.2020.10.011. Epub 2020 Oct 16.

Reference Type RESULT
PMID: 33069706 (View on PubMed)

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

B10903009

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.