AI in Predicting Polyp Pathology and Endoscopic Classification

NCT06773832 | Recruiting DMC

• 2 other identifiers: K7281BRWEP2024W034010100
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 1

Brief 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 \[1\]. 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

Keywords

Colonoscopy; Artificial Intelligence; Optical Diagnosis; Sessile Serrated Adenoma; Adenoma

Outcome Measures

Primary Outcomes (1)

• 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.

  2 years

Secondary Outcomes (3)

• Other Assessment Parameters of Optical Diagnosis

  2 years

• Accuracy in Determining Endoscopic Classification of Colorectal Polyps

  2 years

• Other Assessment Parameters in Determining Endoscopic Classification

  2 years

Study Arms (1)

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

Interventions

Real-time Artificial Intelligence Model for Diagnosing Colorectal Polyp Pathology and Endoscopic Classification DIAGNOSTIC_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.

Patients aged 18 years or older undergoing routine colonoscopy screening

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients aged 18 years or older undergoing routine colonoscopy screening

You may qualify if:

• 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.

You may not qualify if:

• 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;
• 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.

Sponsors & Collaborators

• Peking Union Medical College Hospital: lead

Study Sites (1)

Peking Union Medical College Hospital

Beijing, 100730, China

RECRUITING

Related Publications (9)

• 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.

  PMID: 33368056 BACKGROUND

• Zachariah 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.

  PMID: 31651444 BACKGROUND

• Rees 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.

  PMID: 27196576 BACKGROUND

• Singh 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.

  PMID: 23617643 BACKGROUND

• IJspeert 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.

  PMID: 25753029 BACKGROUND

• Tanaka 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.

  PMID: 21535219 BACKGROUND

• Axelrad 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.

  PMID: 8613016 BACKGROUND

• Mori 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.

  PMID: 32240683 BACKGROUND

• ASGE 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.

  PMID: 25597420 BACKGROUND

MeSH Terms

Conditions

Adenoma

Condition Hierarchy (Ancestors)

Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms

Study Officials

• Dong Wu, MD

  Peking Union Medical College Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Wenmo Hu, MD

CONTACT

86+15101581963; huwenmo1995@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 3 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 3, 2025
• First Posted: January 14, 2025
• Study Start: January 1, 2025
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: January 14, 2025

Record last verified: 2024-12

Data Sharing

• IPD Sharing: Will share
• Time Frame: Beginning 3 months after publication with no end date
• Access Criteria: Any investigators who wish to utilize the data for pertinent research with an appropriate request

Locations

CN (1)