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

Colorectal Neoplasms Clinical Trial

Official title:

Real-time Artificial Intelligence-based Endocytoscopic Diagnosis of Colorectal Neoplasms: a Single Center, Prospective Clinical Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06335654
• Other study ID #: 24K056-001
• Status: Recruiting
• Start date: April 1, 2024
• Est. completion date: December 31, 2024

Study information

• Verified date: April 2024
• Source: The First Hospital of Jilin University
• Contact: Mingqing Liu, Doctor
• Phone: +8613204300453
• Email: liumq23[@]mails.jlu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 350
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Colorectal Neoplasms
• Neoplasms

Intervention

Diagnostic Test:
• 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.

Locations

Country	Name	City	State
China	First hospital of Jilin University	Changchun	Jilin

Sponsors (1)

Lead Sponsor	Collaborator
The First Hospital of Jilin University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	sensitivity		December 2024
Primary	specificity		December 2024
Primary	accuracy		December 2024
Primary	positive predictive value		December 2024
Primary	negative predictive value		December 2024
Primary	high confidence diagnosis rate		December 2024