View clinical trials related to Colorectal Adenoma.
Filter by:To evaluate the accuracy and effectiveness of a novel screening method based on plasma multi-omics combining with artificial intelligence in a large prospective cohort for the detection of colorectal cancer and advanced adenomas.
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.
This study examines whether the use of Hybrid-ESD+ with LiftUp (Injection solution) results in a higher en bloc and/or R0 rate for non pedunculated colorectal adenomas between 2 and 3 cm than described in the literature for conventional EMR.
Chronic lower gastrointestinal (GI) symptoms, including lower abdominal pain, bowel habit change, bleeding per rectum, and abdominal bloating, are caused by functional gastrointestinal disorders (FGID) and organic intestinal disorders, including colorectal cancer and chronic colitis. The presence of alarming features, such as the age of onset older than 50 years, rectal bleeding, anemia, significant weight loss, and family history of colorectal cancer, indicates organic diseases, and colonoscopy should be required. However, using only alarming features may not be sufficiently accurate. For example, anemia or significant weight loss, which are highly specific for organic disorders, usually occur in late-stage diseases. Conversely, the parameters with high sensitivity, such as the age of onset after 50 years, have a low specificity; colonoscopy in these patients may not be urgent. Therefore, tests that can help discriminate organic from functional diseases are warranted. Immunochemical fecal occult blood tests (iFOBT) and fecal calprotectin (FC) are biomarkers that indicate organic lesions in the gastrointestinal tract and could help diagnose patients with lower GI symptoms more accurately.
To evaluate the effectiveness and accuracy of the ctDNA dual-target test kit in a large case-control cohort for the detection of colorectal cancer and advanced adenomas.
The study involves the planned use of a new microwave-based device during colonoscopy procedures in a small group of patients to assess the preliminary safety of its use and lack of normal clinical practice modification. The device is a final design version, which has been previously tested in several preclinical studies, including: phantom studies, an ex vivo study with human tissues, and an in vivo study with animal model (pig).
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 overall goal of this study is to develop a platform for both large-scale chemoprevention trials and real-world chemoprevention studies for colorectal cancer (CRC) prevention. The specific objectives of this proof of concept study are to: 1. Evaluate the feasibility of a real-world chemoprevention agent (CPA) intervention (3-months of daily low-dose acetylsalicylic (ASA)) in participants at increased risk for CRC (one or more high-risk adenomas removed during colonoscopy) based on participant uptake, adherence (days taking CPA), and adverse events; 2. Evaluate factors related to uptake and adherence of ASA using validated surveys and interviews.
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.