Colorectal PolypsNCT06773832
AI in Predicting Polyp Pathology and Endoscopic Classification
An observational study of Colorectal Polyps, run by Peking Union Medical College Hospital at 1 site in China.
Trial summary
Background: Colonoscopy with optical diagnosis based on the appearance of polyps can guide the selection of endoscopic treatment methods, reduce unnecessary polypectomy procedures and the need for tissue pathological diagnosis, and formulate follow-up strategies in a timely manner [1]. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Colorectal Polyps
- What is being tested?
- Real-time Artificial Intelligence Model for Diagnosing Colorectal Polyp Pathology and Endoscopic Classification
- How many people
- 400 participants
- Where
- 1 siteChina
- Study dates
- Jan 2025to Dec 2026
- How long will I take part?
- 3 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients aged 18 years or older undergoing routine colonoscopy screening
Who may be able to join
- Outpatients or inpatients undergoing routine colonoscopy screening at the endoscopy centers of multicenter hospitals;
- Aged 18 years or older;
- Have understanding of the study content and have signed the informed consent form.
Who cannot join
- Gastroparesis or gastric outlet obstruction;
- Known or suspected intestinal obstruction or perforation;
- Severe chronic renal failure (creatinine clearance less than 30 mL/minute);
- Severe congestive heart failure (New York Heart Association Class III or IV);
- Currently pregnant or breastfeeding;
- Toxic colitis or megacolon;
Show all 11Show fewer
- Poorly controlled hypertension (systolic blood pressure greater than 180 mmHg and/or diastolic blood pressure greater than 100 mmHg);
- Moderate or massive active gastrointestinal bleeding (>100 mL/day);
- Significant psychiatric or psychological illness;
- Allergy to medications used for bowel preparation;
- Patients who have undergone colorectal surgery.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Peking Union Medical College Hospital
Beijing, 100730
Wenmo Hu, MD, 86+15101581963, huwenmo1995@126.com
Wenmo Hu, MD
About this study
Official title: Artificial Intelligence Predicts the Pathology and Endoscopic Classification of Colorectal Polyps During Colonoscopy
Background: Colonoscopy with optical diagnosis based on the appearance of polyps can guide the selection of endoscopic treatment methods, reduce unnecessary polypectomy procedures and the need for tissue pathological diagnosis, and formulate follow-up strategies in a timely manner [1]. This approach significantly alleviates the economic burden on patients and the healthcare system and can effectively ease the tension on clinical resources [2]. Various endoscopic polyp classification methods, including Pit Pattern [3], NICE [4], WASP [5], and MS [6], are used to determine pathological types. However, mastering these classification methods requires endoscopists to undergo extensive training, and due to the inherent flaws in each method, no single endoscopic classification method can accurately diagnose all types of polyps to meet the requirements of optical diagnosis. This limitation has hindered the widespread application of optical diagnosis in clinical practice [7]. The application of artificial intelligence technology in this field, known as computer-aided diagnosis (CADx), has seen rapid development in recent years. Numerous large-scale, prospective studies have demonstrated that the accuracy of CADx technology for optical diagnosis of minute lesions (<5mm) has essentially met the threshold set by European and American endoscopy societies for optical diagnosis [8,9]. However, the diagnostic efficacy of CADx for polyps ≥5mm remains unclear. Moreover, current research is mostly limited to distinguishing between common adenomas and hyperplastic polyps, with little attention given to serrated lesions, which are also precancerous lesions and progress even more rapidly, and are more challenging for endoscopists to assess. These reasons prevent CADx from being widely applied in clinical practice for real-time accurate judgment of polyp pathological types.
- Conditions
- Colorectal Polyps
- Being tested
- Real-time Artificial Intelligence Model for Diagnosing Colorectal Polyp Pathology and Endoscopic Classification
- Keywords
- Colonoscopy, Artificial Intelligence, Optical Diagnosis, Sessile Serrated Adenoma, Adenoma
- Medical subject headings
- Adenoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Real-time Artificial Intelligence Model for Diagnosing Colorectal Polyp Pathology and Endoscopic ClassificationDiagnostic test
During the AI model development phase, the aim is to include as many samples as possible. Given the focus on the diagnostic accuracy of serrated lesions, we retrospectively collected approximately 400 cases serrated lesions with pathological diagnosis by the department of pathology at Peking Union Medical College Hospital to date. Additionally, we matched with 400 cases each of hyperplastic polyps, conventional adenomas, and early-stage colorectal cancer, totaling approximately 1600 cases.
The model employs mainstream AI classification algorithms to construct the model and compare the predictive performance of different models. Utilizing the dataset established in the first phase, which contains static images of polyp lesions along with their corresponding pathological diagnosis and endoscopic classifications, we developed and optimized the AI model. Then the model will be be compared with endoscopists in a prospective cohort to investigate the efficacy.
| Study group | Role | What they receive |
|---|---|---|
| Patients aged 18 years or older undergoing routine colonoscopy screening | Not described Receives: Diagnostic Test: Real-time Artificial Intelligence Model for Diagnosing Colorectal Polyp Pathology and Endoscopic Classification |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of Optical Diagnosis for Colorectal Polyps
The accuracy of the AI model's optical diagnosis is compared with that of endoscopists, with pathological diagnosis serving as the gold standard.
Measured: 2 years
Secondary outcomes (3)
Other Assessment Parameters of Optical Diagnosis
Including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of Optical Diagnosis
Measured: 2 years
Accuracy in Determining Endoscopic Classification of Colorectal Polyps
Using the endoscopic classification judgment of experienced endoscopists as the gold standard, the study investigates the accuracy of the AI model in determining the endoscopic classification of lesions. The endoscopic classifications include Pit Pattern, CP, NICE, JNET, WASP, and MS.
Measured: 2 years
Other Assessment Parameters in Determining Endoscopic Classification
The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the AI Model in determining endoscopic classification of colorectal polyps
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Jan 2025
- Study startJan 2025 (estimated)
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Colorectal PolypsStudies of the same condition, open and under way.
- More trials run by Peking Union Medical College HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 400 participants (estimated)
- Target duration
- 3 Years
Sponsor and investigators
- Lead sponsor
- Peking Union Medical College Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Dong Wu, MD, Principal investigator, Peking Union Medical College Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06773832
- Sponsor's protocol ID
- K7281
- Other IDs
- BRWEP2024W034010100
- Status last verified
- Dec 2024
- First submitted
- 3 Jan 2025
- Last updated
- 14 Jan 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: All IPD collected throughout the trial
Publications
van der Zander QEW, Schreuder RM, Fonolla R, Scheeve T, van der Sommen F, Winkens B, Aepli P, Hayee B, Pischel AB, Stefanovic M, Subramaniam S, Bhandari P, de With PHN, Masclee AAM, Schoon EJ. Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared with intuitive optical diagnosis. Endoscopy. 2021 Dec;53(12):1219-1226. doi: 10.1055/a-1343-1597. Epub 2021 Mar 10.
PubMed 33368056Zachariah R, Samarasena J, Luba D, Duh E, Dao T, Requa J, Ninh A, Karnes W. Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves "Resect and Discard" Thresholds. Am J Gastroenterol. 2020 Jan;115(1):138-144. doi: 10.14309/ajg.0000000000000429.
PubMed 31651444Rees CJ, Rajasekhar PT, Wilson A, Close H, Rutter MD, Saunders BP, East JE, Maier R, Moorghen M, Muhammad U, Hancock H, Jayaprakash A, MacDonald C, Ramadas A, Dhar A, Mason JM. Narrow band imaging optical diagnosis of small colorectal polyps in routine clinical practice: the Detect Inspect Characterise Resect and Discard 2 (DISCARD 2) study. Gut. 2017 May;66(5):887-895. doi: 10.1136/gutjnl-2015-310584. Epub 2016 Apr 19.
PubMed 27196576Singh R, Jayanna M, Navadgi S, Ruszkiewicz A, Saito Y, Uedo N. Narrow-band imaging with dual focus magnification in differentiating colorectal neoplasia. Dig Endosc. 2013 May;25 Suppl 2:16-20. doi: 10.1111/den.12075.
PubMed 23617643IJspeert JE, Bastiaansen BA, van Leerdam ME, Meijer GA, van Eeden S, Sanduleanu S, Schoon EJ, Bisseling TM, Spaander MC, van Lelyveld N, Bargeman M, Wang J, Dekker E; Dutch Workgroup serrAted polypS & Polyposis (WASP). Development and validation of the WASP classification system for optical diagnosis of adenomas, hyperplastic polyps and sessile serrated adenomas/polyps. Gut. 2016 Jun;65(6):963-70. doi: 10.1136/gutjnl-2014-308411. Epub 2015 Mar 9.
PubMed 25753029Tanaka S, Sano Y. Aim to unify the narrow band imaging (NBI) magnifying classification for colorectal tumors: current status in Japan from a summary of the consensus symposium in the 79th Annual Meeting of the Japan Gastroenterological Endoscopy Society. Dig Endosc. 2011 May;23 Suppl 1:131-9. doi: 10.1111/j.1443-1661.2011.01106.x.
PubMed 21535219Axelrad AM, Fleischer DE, Geller AJ, Nguyen CC, Lewis JH, Al-Kawas FH, Avigan MI, Montgomery EA, Benjamin SB. High-resolution chromoendoscopy for the diagnosis of diminutive colon polyps: implications for colon cancer screening. Gastroenterology. 1996 Apr;110(4):1253-8. doi: 10.1053/gast.1996.v110.pm8613016.
PubMed 8613016Mori Y, Kudo SE, East JE, Rastogi A, Bretthauer M, Misawa M, Sekiguchi M, Matsuda T, Saito Y, Ikematsu H, Hotta K, Ohtsuka K, Kudo T, Mori K. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest Endosc. 2020 Oct;92(4):905-911.e1. doi: 10.1016/j.gie.2020.03.3759. Epub 2020 Mar 30.
PubMed 32240683ASGE Technology Committee; Abu Dayyeh BK, Thosani N, Konda V, Wallace MB, Rex DK, Chauhan SS, Hwang JH, Komanduri S, Manfredi M, Maple JT, Murad FM, Siddiqui UD, Banerjee S. ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2015 Mar;81(3):502.e1-502.e16. doi: 10.1016/j.gie.2014.12.022. Epub 2015 Jan 16.
PubMed 25597420
Study at a glance
From the registry record, last updated by the sponsor on 14 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Predicts the Pathology and Endoscopic Classification of Colorectal Polyps During Colonoscopy
- ClinicalTrials.gov ID
- NCT06773832
- Acronym / sponsor ID
- K7281
- Conditions
- Colorectal Polyps
- Keywords
- Colonoscopy, Artificial Intelligence, Optical Diagnosis, Sessile Serrated Adenoma, Adenoma
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Peking Union Medical College HospitalUniversity, hospital or other
- Study start
- Jan 2025 (estimated)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 14 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Background: Colonoscopy with optical diagnosis based on the appearance of polyps can guide the selection of endoscopic treatment methods, reduce unnecessary polypectomy procedures and the need for tissue pathological diagnosis, and formulate follow-up strategies in a timely manner [1]. This approach significantly alleviates the economic burden on patients and the healthcare system and can effectively ease the tension on clinical resources [2]. Various endoscopic polyp classification methods, including Pit Pattern [3], NICE [4], WASP [5], and MS [6], are used to determine pathological types. However, mastering these classification methods requires endoscopists to undergo extensive training, and due to the inherent flaws in each method, no single endoscopic classification method can accurately diagnose all types of polyps to meet the requirements of optical diagnosis. This limitation has hindered the widespread application of optical diagnosis in clinical practice [7]. The application of artificial intelligence technology in this field, known as computer-aided diagnosis (CADx), has seen rapid development in recent years. Numerous large-scale, prospective studies have demonstrated that the accuracy of CADx technology for optical diagnosis of minute lesions (<5mm) has essentially met the threshold set by European and American endoscopy societies for optical diagnosis [8,9]. However, the diagnostic efficacy of CADx for polyps ≥5mm remains unclear. Moreover, current research is mostly limited to distinguishing between common adenomas and hyperplastic polyps, with little attention given to serrated lesions, which are also precancerous lesions and progress even more rapidly, and are more challenging for endoscopists to assess. These reasons prevent CADx from being widely applied in clinical practice for real-time accurate judgment of polyp pathological types.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 3 Years
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients aged 18 years or older undergoing routine colonoscopy screening | – | Diagnostic Test: Real-time Artificial Intelligence Model for Diagnosing Colorectal Polyp Pathology and Endoscopic Classification |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients aged 18 years or older undergoing routine colonoscopy screening
Key inclusion criteria (3 listed)
- Outpatients or inpatients undergoing routine colonoscopy screening at the endoscopy centers of multicenter hospitals;
- Aged 18 years or older;
- Have understanding of the study content and have signed the informed consent form.
Key exclusion criteria (11 listed)
- Gastroparesis or gastric outlet obstruction;
- Known or suspected intestinal obstruction or perforation;
- Severe chronic renal failure (creatinine clearance less than 30 mL/minute);
- Severe congestive heart failure (New York Heart Association Class III or IV);
- Currently pregnant or breastfeeding;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Real-time Artificial Intelligence Model for Diagnosing Colorectal Polyp Pathology and Endoscopic ClassificationDuring the AI model development phase, the aim is to include as many samples as possible. Given the focus on the diagnostic accuracy of serrated lesions, we retrospectively collected approximately 400 cases serrated lesions with pathological diagnosis by the department of pathology at Peking Union Medical College Hospital to date. Additionally, we matched with 400 cases each of hyperplastic polyps, conventional adenomas, and early-stage colorectal cancer, totaling approximately 1600 cases. The model employs mainstream AI classification algorithms to construct the model and compare the predictive performance of different models. Utilizing the dataset established in the first phase, which contains static images of polyp lesions along with their corresponding pathological diagnosis and endoscopic classifications, we developed and optimized the AI model. Then the model will be be compared with endoscopists in a prospective cohort to investigate the efficacy. | Diagnostic test | Patients aged 18 years or older undergoing routine colonoscopy screening |
Primary outcome
Accuracy of Optical Diagnosis for Colorectal Polyps
Time frame: 2 years
The accuracy of the AI model's optical diagnosis is compared with that of endoscopists, with pathological diagnosis serving as the gold standard.
3 secondary outcomes
Other Assessment Parameters of Optical Diagnosis
Time frame: 2 years
Accuracy in Determining Endoscopic Classification of Colorectal Polyps
Time frame: 2 years
Other Assessment Parameters in Determining Endoscopic Classification
Time frame: 2 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Jan 2025.
- Background: PubMed 33368056van der Zander QEW, Schreuder RM, Fonolla R, Scheeve T, van der Sommen F, Winkens B, Aepli P, Hayee B, Pischel AB, Stefanovic M, Subramaniam S, Bhandari P, de With PHN, Masclee AAM, Schoon EJ. Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared with intuitive optical diagnosis. Endoscopy. 2021 Dec;53(12):1219-1226. doi: 10.1055/a-1343-1597. Epub 2021 Mar 10.
- Background: PubMed 31651444Zachariah R, Samarasena J, Luba D, Duh E, Dao T, Requa J, Ninh A, Karnes W. Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves "Resect and Discard" Thresholds. Am J Gastroenterol. 2020 Jan;115(1):138-144. doi: 10.14309/ajg.0000000000000429.
- Background: PubMed 27196576Rees CJ, Rajasekhar PT, Wilson A, Close H, Rutter MD, Saunders BP, East JE, Maier R, Moorghen M, Muhammad U, Hancock H, Jayaprakash A, MacDonald C, Ramadas A, Dhar A, Mason JM. Narrow band imaging optical diagnosis of small colorectal polyps in routine clinical practice: the Detect Inspect Characterise Resect and Discard 2 (DISCARD 2) study. Gut. 2017 May;66(5):887-895. doi: 10.1136/gutjnl-2015-310584. Epub 2016 Apr 19.
- Background: PubMed 23617643Singh R, Jayanna M, Navadgi S, Ruszkiewicz A, Saito Y, Uedo N. Narrow-band imaging with dual focus magnification in differentiating colorectal neoplasia. Dig Endosc. 2013 May;25 Suppl 2:16-20. doi: 10.1111/den.12075.
- Background: PubMed 25753029IJspeert JE, Bastiaansen BA, van Leerdam ME, Meijer GA, van Eeden S, Sanduleanu S, Schoon EJ, Bisseling TM, Spaander MC, van Lelyveld N, Bargeman M, Wang J, Dekker E; Dutch Workgroup serrAted polypS & Polyposis (WASP). Development and validation of the WASP classification system for optical diagnosis of adenomas, hyperplastic polyps and sessile serrated adenomas/polyps. Gut. 2016 Jun;65(6):963-70. doi: 10.1136/gutjnl-2014-308411. Epub 2015 Mar 9.
- Background: PubMed 21535219Tanaka S, Sano Y. Aim to unify the narrow band imaging (NBI) magnifying classification for colorectal tumors: current status in Japan from a summary of the consensus symposium in the 79th Annual Meeting of the Japan Gastroenterological Endoscopy Society. Dig Endosc. 2011 May;23 Suppl 1:131-9. doi: 10.1111/j.1443-1661.2011.01106.x.
- Background: PubMed 8613016Axelrad AM, Fleischer DE, Geller AJ, Nguyen CC, Lewis JH, Al-Kawas FH, Avigan MI, Montgomery EA, Benjamin SB. High-resolution chromoendoscopy for the diagnosis of diminutive colon polyps: implications for colon cancer screening. Gastroenterology. 1996 Apr;110(4):1253-8. doi: 10.1053/gast.1996.v110.pm8613016.
- Background: PubMed 32240683Mori Y, Kudo SE, East JE, Rastogi A, Bretthauer M, Misawa M, Sekiguchi M, Matsuda T, Saito Y, Ikematsu H, Hotta K, Ohtsuka K, Kudo T, Mori K. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest Endosc. 2020 Oct;92(4):905-911.e1. doi: 10.1016/j.gie.2020.03.3759. Epub 2020 Mar 30.
- Background: PubMed 25597420ASGE Technology Committee; Abu Dayyeh BK, Thosani N, Konda V, Wallace MB, Rex DK, Chauhan SS, Hwang JH, Komanduri S, Manfredi M, Maple JT, Murad FM, Siddiqui UD, Banerjee S. ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2015 Mar;81(3):502.e1-502.e16. doi: 10.1016/j.gie.2014.12.022. Epub 2015 Jan 16.
- Individual participant dataSharing planned: Yes. All IPD collected throughout the trial
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Dong Wu, MD principal investigator
Peking Union Medical College Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Polyps, run by Peking Union Medical College Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 11 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 14 Jan 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Wenmo Hu, MD (86+15101581963, huwenmo1995@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of Optical Diagnosis for Colorectal Polyps (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06773832, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Jan 2025.