Computer Aided Detection of Polyps in the Colon
1 other identifier
interventional
234
1 country
4
Brief Summary
The purpose of this study is to examine the role of an automatic polyp detection software (henceforth referred to as the research software) as a support system during colonoscopy; a procedure during which a physician uses a colonoscope or scope, to look inside a patient's rectum and colon. The scope is a flexible tube with a camera-to see the lining of the colon. The research software is used to aid in the detection of polyps (abnormal tissue growths in the wall of the colon and adenomas (pre-cancerous growths) during colonoscopy. The research software used in this study was programmed by a company in Shanghai, which develops artificial intelligence software for computer aided diagnostics. The research software was developed using a large repository (database or databases) of polyp images where expert colonoscopists outlined polyps and suspicious lesions. The software was subsequently developed and validated using several databases of images and video to operate in near real-time or within minutes of photographing the tissue. It is intended to point out polyps and suspicious lesions on a separate screen that stands behind the primary monitor during colonoscopy. It is not expected to change the colonoscopy procedure in any way, and the physician will make the final determination on whether or not to biopsy or remove any lesion in the colon wall. The research software will not record any video data during the colonoscopy procedure. In the future, this software may help gastroenterologists detect precancerous areas and decrease the incidence of colon cancer in the United States.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started May 2019
Typical duration for not_applicable
4 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
April 20, 2019
CompletedFirst Posted
Study publicly available on registry
April 24, 2019
CompletedStudy Start
First participant enrolled
May 7, 2019
CompletedPrimary Completion
Last participant's last visit for primary outcome
November 24, 2020
CompletedStudy Completion
Last participant's last visit for all outcomes
May 12, 2021
CompletedJuly 20, 2021
July 1, 2021
1.6 years
April 20, 2019
July 19, 2021
Conditions
Outcome Measures
Primary Outcomes (1)
Adenoma Miss Rate (AMR)
Adenoma Miss Rate (AMR), to determine if the combination technique identifies more adenomas compared to the standard technique. AMR will be calculated as the number of adenomas detected on the second pass or portion in either group divided by the total number of adenomas detected during both passes
One Hour
Secondary Outcomes (6)
Polyp Miss Rate (PMR)
One Hour
Amplified adenoma detection rate
6 months
Advanced adenoma miss rate determination
6 months
Colonoscope segmental withdrawal time determination
6-10 minutes
Total procedure time determination
During length of procedure
- +1 more secondary outcomes
Study Arms (2)
Arm-1 Standard Colonoscopy/AI-Assisted Combined Colonoscopy
EXPERIMENTALNormal scope insertion and withdrawal first, followed by a second withdrawal with the research software running on a separate screen to catch any additional polyps missed during the first withdrawal.
Arm-2 AI-Assisted Combined Colonoscopy/Standard Colonoscopy
EXPERIMENTALNormal scope insertion but first withdrawal with the research software running on a separate screen, followed by a second withdrawal without the research software running.
Interventions
The research software is deep learning algorithm used to aid in the detection of polyps (abnormal tissue growths in the wall of the colon and adenomas (pre-cancerous growths) during colonoscopy. In its current form, the automatic polyp detection system is installed on a computer system unit that utilizes an an operating system.
Eligibility Criteria
You may qualify if:
- Patients age: ≥ 22 years
- Patients presenting for routine colonoscopy for screening and/or surveillance purposes.
- Willingness to undergo two withdrawals with and without the use of computer-aided software while undergoing conventional colonoscopy with sedation
- Ability to provide written, informed consent and understand the responsibilities of trial participation
You may not qualify if:
- Minors aged \< 22 years.
- People with diminished cognitive capacity
- Patients undergoing diagnostic colonoscopy (e.g. as an evaluation for active gastrointestinal bleed, referring collectively to the stomach and the small and large intestine).
- Patients with incomplete colonoscopies (those where endoscopists did not successfully intubate the cecum due to technical difficulties or poor bowel preparation)
- Patients that have standard contraindications to colonoscopy in general (e.g. documented acute diverticulitis, fulminant colitis and known or suspected perforation).
- Patients with inflammatory bowel disease
- Patients with any polypoid/ulcerated lesion \> 2 cm concerning for invasive cancer on endoscopy
- Patients referred for endoscopic mucosal resection (EMR), which is a procedure to remove early-stage cancer and precancerous growths from the lining of the digestive tract.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (4)
University of Chicago
Chicago, Illinois, 60637, United States
Beth Israel Deaconess Medical Center
Boston, Massachusetts, 02130, United States
NYU Langone
New York, New York, 10016, United States
Baylor College of Medicine
Houston, Texas, 77030, United States
Related Publications (7)
Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin. 2017 Jan;67(1):7-30. doi: 10.3322/caac.21387. Epub 2017 Jan 5.
PMID: 28055103BACKGROUNDWinawer SJ, Fletcher RH, Miller L, Godlee F, Stolar MH, Mulrow CD, Woolf SH, Glick SN, Ganiats TG, Bond JH, Rosen L, Zapka JG, Olsen SJ, Giardiello FM, Sisk JE, Van Antwerp R, Brown-Davis C, Marciniak DA, Mayer RJ. Colorectal cancer screening: clinical guidelines and rationale. Gastroenterology. 1997 Feb;112(2):594-642. doi: 10.1053/gast.1997.v112.agast970594. No abstract available.
PMID: 9024315BACKGROUNDFerlitsch M, Reinhart K, Pramhas S, Wiener C, Gal O, Bannert C, Hassler M, Kozbial K, Dunkler D, Trauner M, Weiss W. Sex-specific prevalence of adenomas, advanced adenomas, and colorectal cancer in individuals undergoing screening colonoscopy. JAMA. 2011 Sep 28;306(12):1352-8. doi: 10.1001/jama.2011.1362.
PMID: 21954479BACKGROUNDWinawer SJ, Zauber AG, Ho MN, O'Brien MJ, Gottlieb LS, Sternberg SS, Waye JD, Schapiro M, Bond JH, Panish JF, et al. Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med. 1993 Dec 30;329(27):1977-81. doi: 10.1056/NEJM199312303292701.
PMID: 8247072BACKGROUNDRex DK, Schoenfeld PS, Cohen J, Pike IM, Adler DG, Fennerty MB, Lieb JG 2nd, Park WG, Rizk MK, Sawhney MS, Shaheen NJ, Wani S, Weinberg DS. Quality indicators for colonoscopy. Am J Gastroenterol. 2015 Jan;110(1):72-90. doi: 10.1038/ajg.2014.385. Epub 2014 Dec 2. No abstract available.
PMID: 25448873BACKGROUNDCorley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, Zauber AG, de Boer J, Fireman BH, Schottinger JE, Quinn VP, Ghai NR, Levin TR, Quesenberry CP. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014 Apr 3;370(14):1298-306. doi: 10.1056/NEJMoa1309086.
PMID: 24693890BACKGROUNDGlissen Brown JR, Mansour NM, Wang P, Chuchuca MA, Minchenberg SB, Chandnani M, Liu L, Gross SA, Sengupta N, Berzin TM. Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial). Clin Gastroenterol Hepatol. 2022 Jul;20(7):1499-1507.e4. doi: 10.1016/j.cgh.2021.09.009. Epub 2021 Sep 14.
PMID: 34530161DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Tyler M Berzin, MD
Beth Israel Deaconess Medical Center
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Purpose
- DIAGNOSTIC
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Assistant Professor of Medicine
Study Record Dates
First Submitted
April 20, 2019
First Posted
April 24, 2019
Study Start
May 7, 2019
Primary Completion
November 24, 2020
Study Completion
May 12, 2021
Last Updated
July 20, 2021
Record last verified: 2021-07
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, SAP, CSR
- Time Frame
- After study completion
- Access Criteria
- All collected data is to be analyzed in support to the study's hypothesis and endpoints. This data includes other variables, which will be obtained shortly after the procedure via chart review, including intra-procedural data points such as time needed to reach the cecum and scope withdrawal time. Data will be collected and stored in an encrypted and anonymized database such as REDCap or in an excel spreadsheet with de-identified information and encryption. All collected de-identified data (data which is stripped off all personal information) will be shared with other sites via REDCap.
There is a plan to make IPD and related data available to researchers involved in the study at other centers. Data sharing is subject to data sharing agreement signed by participating institutions with BIDMC. The polyp detection software does not save or store any study data.