Research on Endoscopic Precision Biopsy.

NCT ID: NCT05261932

Last Updated: 2022-03-02

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 40 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-11-26
• Study Completion Date: 2023-11-30

Brief Summary

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.

Conditions

Colorectal Adenoma

Study Design

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

Study Groups

The accuracy of expert with or with-out AI

AI-assisted guided biopsy

Intervention Type: PROCEDURE

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

AI-assisted guided biopsy

Intervention Type: PROCEDURE

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.

Interventions

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.

Intervention Type: PROCEDURE

Eligibility Criteria

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.

• Minimum Eligible Age: 30 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Beijing Tsinghua Chang Gung Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Ruigang Wang

Role: STUDY_CHAIR

Beijing Tsinghua Changgeng Hospital

Locations

Beijing Tsinghua Changgung Hospital

Beijing, Beijing Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Ruigang Wang

Role: CONTACT

wrga02147@btch.edu.cn

Xuan Jiang

Role: CONTACT

jxa01998@btch.edu.cn

+86 (010)56119096

Facility Contacts

Ruigang Wang

Role: primary

wrga02147@btch.edu.cn

Other Identifiers

12021C1011

Identifier Type: -

Identifier Source: org_study_id