Real-time Artificial Intelligence-based Endocytoscopic Diagnosis of Colorectal Neoplasms: a Single Center, Prospective Clinical Study
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Colorectal Neoplasms
- Sponsor
- The First Hospital of Jilin University
- Enrollment
- 680
- Locations
- 1
- Primary Endpoint
- 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.
- Status
- Completed
- Last Updated
- 4 months ago
Overview
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
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.
Investigators
Hong Xu
Director, Head of Gastroenterology and Endoscopy Center, Principal Investigator, Clinical Professor
The First Hospital of Jilin University
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
Primary 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.
Time Frame: 8 months
The sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and high confidence diagnosis rate will be calculated for comparison with final histology as the gold standard for diagnosis
Secondary Outcomes
- To evaluate the performance and high-confidence diagnostic rate of EndoBRAIN in diagnosing adenomas of rectosigmoid colon ≤5 mm;(8 months)
- To evaluate the performance and high-confidence diagnostic rate of Endobrain under EC-stained mode in the diagnosis of invasive cancer;(8 months)
- To evaluate the Influencing factors on the diagnosis of colorectal lesions by EndoBRAIN.(8 months)
- To compare the diagnostic performance of diagnosing the histology of colorectal lesions by EndoBRAIN, by endoscopists, and by endoscopists combined with EndoBRAIN;(8 months)