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