Spots Global Cancer Trial Database for Real-time Artificial Intelligence-based Endocytoscopic Diagnosis of Colorectal Neoplasms

Study Description

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.

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Eligibility

Minimum Age:

Eligible Ages: CHILD, ADULT, OLDER_ADULT

Sex: ALL

Healthy Volunteers: No

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