Research on Endoscopic Precision Biopsy. (REPB)

Research on Endoscopic Precision Biopsy Guided by AI System

Colorectal adenoma is a common disease and frequently-occurring disease in gastroenterology. With the continuous progress of colonoscopy equipment and the gradual improvement of endoscopic accessories, especially the development of chromo-endoscopy and magnifying endoscopy. The observation of the surface structure and capillary morphology of colorectal adenomas can realize optical biopsy. Currently, most clinical endoscopic diagnosis of colorectal diseases is biopsy under colonoscopy, and further treatment options are determined based on the pathological results of the biopsy. The problem is that the pathological diagnosis of some preoperative biopsy is not completely consistent with the pathological diagnosis of postoperative large specimens. Previous studies have found that the pathological diagnosis accuracy rate of preoperative biopsy is only 66-75%, so there is a certain degree of subjectivity in relying solely on colonoscopy white light biopsy. Based on the previous work, the research team has initially established an intelligent recognition model for colorectal adenoma classification (low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia), and formed a colorectal adenoma of a certain size with annotated endoscopic image data set. Using the YOLO-V4 algorithm, under the Darknet framework, to train an artificial intelligence (AI) system which specifically for adenoma recognition and diagnosis, its accuracy rate has reached more than 90%. This study intends to increase the sample size based on the previous work, and further improve the accuracy of the classification and diagnosis of the AI system, so as to guide the endoscopist to perform targeted biopsy and improve the accuracy of preoperative biopsy.

Study Overview

• Status: Recruiting
• Conditions: Colorectal Adenoma
• Intervention / Treatment: Procedure: AI-assisted guided biopsy

• Study Type: Observational
• Enrollment (Anticipated): 40

Contacts and Locations

• Study Contact: Name: Ruigang Wang; Email: wrga02147@btch.edu.cn
• Study Contact Backup: Name: Xuan Jiang; Phone Number: +86 (010)56119096; Email: jxa01998@btch.edu.cn

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 102218
      • Recruiting
      • Beijing Tsinghua Changgung Hospital
      • Contact: Ruigang Wang; Email: wrga02147@btch.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 30 years to 75 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

This study has been reviewed by the hospital ethics committee. The enrolled subjects were found to have advanced colorectal adenomas during colonoscopy, and already had the pathological results of AI-assisted guided biopsy. The patients were hospitalized for complete resection with EMR and ESD and were willing to participate in this study.

Description

Inclusion Criteria:

• Age between 30-75;
• Those who have no mental abnormality and can conduct questionnaire surveys;
• BBPS ≥ 6;
• Colorectal advanced adenoma, and admitted for complete resection with EMR and ESD;
• Provide the relevant information required by this study and sign the informed consent.

Exclusion Criteria:

• Those who cannot provide the relevant information required by this research;
• Patients with inflammatory bowel disease;
• Those with a history of liver cirrhosis, uncontrolled hypertension, history of myocardial infarction, cardiac insufficiency, renal insufficiency, respiratory failure, diabetic ketosis and electrolyte imbalance and other serious diseases;
• Those who cannot stop antiplatelet drugs or anticoagulant drugs;
• Those who have not completed full colonoscopy;
• Pregnant women.

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Control
• Time Perspectives: Prospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
The accuracy of expert with or with-out AI	Procedure: AI-assisted guided biopsy; The surface of the adenoma was classified and identified by the AI system, and different areas of the adenoma were marked by distribution to guide the endoscopist for biopsy to obtain the poorly differentiated portion of the lesion.
The accuracy non-expert with or with-out AI	Procedure: AI-assisted guided biopsy; The surface of the adenoma was classified and identified by the AI system, and different areas of the adenoma were marked by distribution to guide the endoscopist for biopsy to obtain the poorly differentiated portion of the lesion.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The accuracy of AI	Concordance rate between biopsy and postoperative pathology	June 2023
The accuracy of expert with or without AI	Concordance rate between expert experience and postoperative pathology	June 2023
The accuracy of non-expert with or without AI	Concordance rate between non-expert experience and postoperative pathology	June 2023

Collaborators and Investigators

• Sponsor: Beijing Tsinghua Chang Gung Hospital
• Investigators: Study Chair: Ruigang Wang, Beijing Tsinghua Changgeng Hospital

Study record dates

Study Major Dates

• Study Start (Actual): November 26, 2021
• Primary Completion (Anticipated): June 1, 2023
• Study Completion (Anticipated): November 30, 2023

Study Registration Dates

• First Submitted: February 17, 2022
• First Submitted That Met QC Criteria: February 28, 2022
• First Posted (Actual): March 2, 2022

Study Record Updates

• Last Update Posted (Actual): March 2, 2022
• Last Update Submitted That Met QC Criteria: February 28, 2022
• Last Verified: February 1, 2022

More Information

Terms related to this study

• Keywords: AI system; Colonoscope; Guided Biopsy
• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Adenoma
• Other Study ID Numbers: 12021C1011

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: Undecided
• IPD Plan Description: After completing the pre-experiment.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No