Real-time Artificial Intelligence-based Endocytoscopic Diagnosis of Colorectal Neoplasms

NCT ID: NCT06335654

Last Updated: 2025-12-09

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 680 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-04-01
• Study Completion Date: 2024-12-19

Brief Summary

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide. Colonoscopy is considered the preferred method of screening for colorectal cancer, and resection of colorectal lesions can significantly reduce the incidence and mortality of colorectal cancer. In order to improve the qualitative and quantitative diagnosis of colorectal lesions, many endoscopic techniques, such as image-enhanced endoscopy (IEE), including narrowband imaging (NBI), magnifying endoscopy, pigment endoscopy, confocal laser endoscopy, and endocytoscopy (EC) are applied clinically. However, with the increasing number of endoscopic resection, the costs associated with the pathological diagnosis of endoscopic resection and resection specimens increase year by year. In clinical practice, some non-neoplastic colorectal lesions may not require resection, so it is important to distinguish neoplastic from non-neoplastic during colonoscopy. The application of EC is intended to achieve the purpose of real-time histopathological endoscopic diagnosis without biopsy. Several studies have shown that EC is effective in identifying the nature of colorectal lesions and judging the depth of invasion in CRC. Based on the endoscopic diagnosis, the endoscopist can determine the treatment plan for the colorectal lesions. The latest EC is an integrated endoscope with a contact light microscopy system with a maximum magnification of 520 x. EC can demonstrate the atypical of gland structure and cells after staining and display the super-amplified surface microvessels of the lesion under the EC-NBI mode. However, the judgment of endocytoscopic images needs a lot of experience to improve the diagnostic accuracy. Moreover, endoscopists have certain subjective judgments and errors in endocytoscopic diagnosis. There is an artificial intelligence system which has been developed to identify colorectal neoplasms. However, there is still a lack of prospective clinical verification based on Chinese population. In the study, the investigators performed a prospective clinical study to determine the diagnostic accuracy of artificial intelligence system.

Detailed Description

Colonoscopy is currently the gold standard of screening for CRC. The endocytoscopy, due to its high magnification function, can achieve the purpose of optical biopsy. However, endoscopic doctors have certain difficulties in diagnosing with the endocytoscopy, especially for novice endoscopic doctors, whose diagnostic accuracy is often low.

Therefore, EndoBRAIN, as an artificial intelligence system for assisting in the diagnosis of the endocytoscopy, has the advantage of rapid diagnosis. In the EC-NBI mode, it predicts as "Non-neoplastic" or "Neoplastic", and in the EC-stained mode, its prediction result is "Non-neoplastic", "Adenoma" or "Invasive cancer".

However, currently this artificial intelligence-assisted diagnostic system has not been applied in the Chinese population. The investigators plan to conduct a prospective clinical trial to validate the accuracy of EndoBRAIN for prediction of colorectal lesions histology in real-time endocytoscopy. This study will prospectively collect the lesions that meet the inclusion and exclusion criteria. After the endoscopic doctors make the diagnosis through endoscopic optics and EndoBRAIN, and then undergo endoscopic resection or surgical resection followed by pathological diagnosis, they will compare the doctor's diagnosis, the artificial intelligence diagnosis results with the gold standard pathological results, and summarize the diagnostic accuracy of this artificial intelligence-assisted diagnostic system for the colorectal lesions.

Conditions

Colorectal Neoplasms

Study Design

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

Study Groups

Patients with one or more colorectal lesions detected

During endocytoscopy, the Clinician inspect for the presence of colorectal lesions as per routine clinical practice with the EndoBRAIN turned off. When a colorectal lesion is encountered, the Clinician will make a prediction on the histology based on routine clinical practice. Following this, the EndoBRAIN function will be switched on and the Clinician will take note of the EndoBRAIN prediction for the every image of colorectal lesion.

In addition, other colorectal lesion features such as the size, location and shape will be recorded, which is similar to what is performed in routine clinical practice. The colorectal lesion will be resected and sent for pathological examination, which will form the "gold standard" for the diagnosis of colorectal lesion histology.

artificial intelligence system

Intervention Type: DIAGNOSTIC_TEST

The colorectal lesions had been observed with EC-NBI and EC-stained by endoscopists before treatment that were ultimately performed histopathologic examination. The endocytoscopies (CF-H290ECI, Olympus, Tokyo, Japan) have a maximum magnification of ×520, focusing depth, 35 μm; field of view, 570 × 500μm. During EC-NBI , the endoscopist pushed the button of the endoscope to switch from white-light imaging to NBI and observed the lesion with full magnification. After endocytoscopic observation, the artificial intelligence system will be open and display the predictive result. Finally, the endoscopist performed EC-stained mode diagnosis after staining the lesion surface with 1.0% methylene blue. After endocytoscopic observation, the artificial intelligence system will be open again and display the predictive result.

Interventions

artificial intelligence system

The colorectal lesions had been observed with EC-NBI and EC-stained by endoscopists before treatment that were ultimately performed histopathologic examination. The endocytoscopies (CF-H290ECI, Olympus, Tokyo, Japan) have a maximum magnification of ×520, focusing depth, 35 μm; field of view, 570 × 500μm. During EC-NBI , the endoscopist pushed the button of the endoscope to switch from white-light imaging to NBI and observed the lesion with full magnification. After endocytoscopic observation, the artificial intelligence system will be open and display the predictive result. Finally, the endoscopist performed EC-stained mode diagnosis after staining the lesion surface with 1.0% methylene blue. After endocytoscopic observation, the artificial intelligence system will be open again and display the predictive result.

Intervention Type: DIAGNOSTIC_TEST

Other Intervention Names

endocytoscopy

Eligibility Criteria

Inclusion Criteria

• Those patients who, during the endoscopic examination, discovered at least one colorectal lesion and received treatment and obtained a pathological diagnosis
• consent obtained for the study

Exclusion Criteria

• non-epithelial tumors
• a history of inflammatory bowel disease
• chemotherapy or radiation therapy for colorectal cancer
• lesions without clear EC images
• specific pathological types
• familial adenomatous polyposis

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

The First Hospital of Jilin University

OTHER

Sponsor Role: lead

Responsible Party

Hong Xu

Director, Head of Gastroenterology and Endoscopy Center, Principal Investigator, Clinical Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Hong Xu, PHD

Role: PRINCIPAL_INVESTIGATOR

The First Hospital of Jilin University

Locations

The First hospital of Jilin University

Changchun, Jilin, China

Countries

China

Other Identifiers

24K056-001

Identifier Type: -

Identifier Source: org_study_id