View clinical trials related to Colorectal Adenoma.
Filter by:The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.
The investigators aim to evaluate and compare the diagnostic accuracy of FIT and the novel panel of bacterial gene markers (Fn, m3, Ch and Bc) collectively named as M3, in detecting colorectal advanced neoplasia.
The investigators hypothesize that gut microbiome composition and the four bacterial gene markers (M3) show dynamic changes after endoscopic resection of advanced neoplasia, some key bacteria are associated with restoration of gut microbiome after endoscopic resection.
Colorectal adenoma is a common disease and frequently-occurring disease in gastroenterology. With the continuous progress of colonoscopy equipment and the gradual improvement of endoscopic accessories, especially the development of chromo-endoscopy and magnifying endoscopy. The observation of the surface structure and capillary morphology of colorectal adenomas can realize optical biopsy. Currently, most clinical endoscopic diagnosis of colorectal diseases is biopsy under colonoscopy, and further treatment options are determined based on the pathological results of the biopsy. The problem is that the pathological diagnosis of some preoperative biopsy is not completely consistent with the pathological diagnosis of postoperative large specimens. Previous studies have found that the pathological diagnosis accuracy rate of preoperative biopsy is only 66-75%, so there is a certain degree of subjectivity in relying solely on colonoscopy white light biopsy. Based on the previous work, the research team has initially established an intelligent recognition model for colorectal adenoma classification (low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia), and formed a colorectal adenoma of a certain size with annotated endoscopic image data set. Using the YOLO-V4 algorithm, under the Darknet framework, to train an artificial intelligence (AI) system which specifically for adenoma recognition and diagnosis, its accuracy rate has reached more than 90%. This study intends to increase the sample size based on the previous work, and further improve the accuracy of the classification and diagnosis of the AI system, so as to guide the endoscopist to perform targeted biopsy and improve the accuracy of preoperative biopsy.
The investigators hypothesize that detection of field cancerization in the GI tract could be performed during endoscopy by performing Raman and scattering measurements. Together with the Technical University of Munich (TUM) and the Universidad Carlos III de Madrid (UC3M), the investigators have developed an investigational medical device that integrates probe-based Raman and scattering measurements for endoscopic purposes: the SENSITIVE system. During preclinical ex vivo studies, the investigators have established that measurements of the SENSITIVE system were able to discriminate between non-field cancerized tissue and field cancerized tissue. Considering these results, the investigators aim to assess the safety of in vivo Raman/scattering during endoscopy. Secondly, the investigators to assess the feasibility of this approach measurements to determine field cancerization in the alimentary tract during endoscopy through the SENSITIVE system.
Early detecting and removing of colorectal advanced adenomas can reduce incidence of colorectal cancer. In order to reduce the incidence of colorectal cancer, improve the early diagnosis of colorectal cancer, the investigators conducted this study to explore diagnostic accuracy of fecal immunochemical test in colorectal cancer screening population.
The study aimed to analyze the risk factors of colorectal advanced adenoma and constructe a model to predict the high-risk individuals of harbouring colorectal advanced adenomas, so as to better identify screening participants and provide an important theoretical basis for the prevention of colorectal cancer.
The investigators aim to evaluate the diagnostic accuracy of FIT and the novel panel of four bacterial gene markers collectively named as M3, to detect recurrent advanced adenomas in patients with history of colonic adenomas.
Most of the sporadic colorectal cancer (CRC )develop from colorectal adenoma (CRA), patients with CRA have a high risk of recurrence and development of metachronous CRA or CRC after removal, therefore, the investigators conducted this clinical trial to explore the chemoprevetion effect of metformin for CRA recurrence after removal.
This is an observational, prospective study using fecal DNA methylation test to define the risk of suffering from advanced adenoma or colorectal cancer (CRC) compared to colonoscopy and fecal immunochemical test (FIT). This study recruits at least 80 participants, including 40 people of healthy controls, 20 people with adenoma, and 20 people with CRC, which were confirmed by colonoscopy. All fecal specimens from participants will be examined by FIT and multi-methylated target gene detection through real-time quantitative methylation-specific PCR (qMSP). The objective of this study is to evaluate the sensitivity and specificity of multi-methylated target PCR compared with the FIT and confirm the examination results through colonoscopy.