AI in Predicting Polyp Pathology and Endoscopic Classification

NCT ID: NCT06773832

Last Updated: 2025-01-14

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 400 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-01-31
• Study Completion Date: 2026-12-31

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Patients aged 18 years or older undergoing routine colonoscopy screening

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

Intervention Type: 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.

Interventions

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

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

1. Outpatients or inpatients undergoing routine colonoscopy screening at the endoscopy centers of multicenter hospitals;

2. Aged 18 years or older;

3. Have understanding of the study content and have signed the informed consent form.

Exclusion Criteria

1. Gastroparesis or gastric outlet obstruction;

2. Known or suspected intestinal obstruction or perforation;

3. Severe chronic renal failure (creatinine clearance less than 30 mL/minute);

4. Severe congestive heart failure (New York Heart Association Class III or IV);

5. Currently pregnant or breastfeeding;

6. Toxic colitis or megacolon;

7. Poorly controlled hypertension (systolic blood pressure greater than 180 mmHg and/or diastolic blood pressure greater than 100 mmHg);

8. Moderate or massive active gastrointestinal bleeding (>100 mL/day);

9. Significant psychiatric or psychological illness;

10. Allergy to medications used for bowel preparation;

11. Patients who have undergone colorectal surgery.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Peking Union Medical College Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Dong Wu, MD

Role: PRINCIPAL_INVESTIGATOR

Peking Union Medical College Hospital

Locations

Peking Union Medical College Hospital

Beijing, , China

Site Status: RECRUITING

Countries

China

Central Contacts

Wenmo Hu, MD

Role: CONTACT

huwenmo1995@126.com

86+15101581963

Facility Contacts

Wenmo Hu, MD

Role: primary

huwenmo1995@126.com

86+15101581963

Wenmo Hu, MD

Role: backup

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Other Identifiers

BRWEP2024W034010100

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

K7281

Identifier Type: -

Identifier Source: org_study_id