Real-time Artificial Intelligence-based Endocytoscopic Diagnosis of Colorectal Neoplasms: a Single Center, Prospective Clinical Study
Trial ID: NCT06335654
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-04-01
Completion date: 2024-12-19
Sponsor: The First Hospital of Jilin University
Conditions: Colorectal Neoplasms
Interventions: artificial intelligence system
Locations: The First hospital of Jilin University
Countries: China
Enrollment: 680
Age range: None - None
Sex: ALL

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.

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

Outcomes:
To evaluate the diagnostic performance and high confidence diagnosis rate of EndoBRAIN in diagnosing neoplastic lesions in a clinical setting.EndoBRAIN in diagnosing neoplastic lesions in a clinical setting.