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

Recruiting

• Conditions: Colorectal Neoplasms
• Interventions: Diagnostic Test: artificial intelligence system

• Registration Number: NCT06335654
• Lead Sponsor: The First Hospital of Jilin University

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

Not available

Study Timeline

• Study First Submitted: 2024-03-22
• Study First Submitted QC: 2024-03-22
• Study First Posted: 2024-03-28
• Status Verified: 2024-04
• Study Start: 2024-04-01
• Last Update Submitted: 2024-04-16
• Last Update Posted: 2024-04-18
• Primary Completion: 2024-12-31
• Study Completion: 2024-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 350

Inclusion Criteria

• colorectal lesions

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
colorectal lesion	artificial intelligence system	-

Primary Outcome Measures

Name	Time	Method
sensitivity	December 2024
positive predictive value	December 2024
accuracy	December 2024
negative predictive value	December 2024
specificity	December 2024
high confidence diagnosis rate	December 2024

Trial Locations

Locations (1)

First hospital of Jilin University; 🇨🇳 Changchun, Jilin, China