View clinical trials related to Adenoma.
Filter by:The study will evaluate the efficacy of bilateral infraorbital nerve block versus preoperative nasal packing with long-acting local anesthetic bupivacaine in term of maintaining hemodynamics intraoperative within 20% below baseline to achieve adequate hypotensive anesthesia and longer duration of postoperative analgesia up to 24 hours in patients undergoing transsphenoidal pituitary adenoma resection.
Parathyroid glands are in the neck and produce a substance called parathormone which maintains the calcium level in the blood. Sometimes one or more of the parathyroid glands become hyperactive and produce too much parathormone which causes increased calcium in the blood which can cause ill effects on multiple parts of the body. Hyperactive glands are identified by Tc-99m Sestamibi (MIBI) scan which helps the surgeons to remove them with minimal risk to the patient. But about 30% of the time MIBI scan does not localize the hyperactive gland. There is some evidence that a new agent called F-18 PSMA (prostate-specific membrane antigen) can localize hyperactive parathyroid. This study is being done to collect preliminary data to answer the question: Can imaging with the PET tracer, F-18 PSMA (Pylarify), prior to parathyroid surgery, provide better information to a surgeon than the standard of care imaging with MIBI scan? Patients who are scheduled for parathyroidectomy and are scheduled for imaging with MIBI scan prior to surgery will be asked to take part in this study. This is a single institutional study to collect preliminary data to help do a larger study. Participants will get MIBI scan first, and the same day will get an F-18 PSMA scan which involves an injection in the vein, waiting an hour, and imaging of the neck and chest area for 10 minutes. The findings of F-18 PSMA will not interfere with the participant's management. Patients who participate will not directly benefit by participating in this study. If the scanning method using F-18 PSMA shows better results than MIBI scan (standard of care) then the investigators will conduct a larger multi-institutional study. If the results prove that F-18 PSMA is better than the standard of care in the larger study, then patients with hyperactive parathyroid patients in the future will benefit.
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 primary objective of this clinical trial is to determine the sensitivity and specificity of the EarlyTect® CRC test for detecting CRC, using colonoscopy as the reference method. The secondary objective is to compare the clinical performance of EarlyTect® CRC test with a commercially available Fecal Immunochemical Test (FIT), with respect to CRC. By histopathological examination, lesions identified during colonoscopy will be confirmed as malignant or precancerous by histological examination.
The registry of this study was subjected to patients who were radiologically diagnosed with a non-malignant brain tumor at Seoul National University Hospital since 2001, and who have had magnetic resonance (MR) re-examination after first MR exam or will be re-examined because it was determined that immediate treatment would not be needed at the first visit to the hospital. In all MRs taken by patients, the date of imaging and the volume of the tumor are measured, and we aim to establish a natural growth history for non-malignant brain tumors.
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.
This is a pragmatic, double-blind, randomized, controlled trial, to evaluate the effect of implementing a CADs system within the routine clinical practice of Canadian healthcare institutions. The main hypothesis of this study is that the ADR in the operating room equipped with the GI genius CADe system will be significantly higher than the ADR in the ordinary operating room.
Colonoscopy is clinically used as the gold standard for detection of colorectal cancer (CRC) and removal of adenomatous polyps of the colon and rectum. Evidence has shown that CRC could be prevented by colonoscopic removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. In recent years, emerging artificial intelligence (AI) and computer-aided detection (CADe) technology has been shown to improve ADR. Based on a meta-analysis, ADR was demonstrated to be significantly higher in the CADe groups than in the standard colonoscopy groups, representing a relative risk of 25.2%. In this study, performance of colonoscopy with or without aid of CADe will be compared in terms of quality indicators. The adenoma detection rate (ADR), which is the proportion of average-risk patients undergoing screening colonoscopy in whom an adenoma is found, is regarded as a robust measure of colonoscopy performance quality that correlates with subsequent cancer risk. Thus, ADR is taken as the primary outcome of this study. The target population includes individuals who are undergoing screening, diagnostic, or surveillance colonoscopy.
The purpose of this study is to compare the additional diagnostic yield over Standard Colonoscopy (i.e., the adenoma miss-rate reduction) obtained by performing CADEYE and G-EYE® aided colonoscopy, vs. the additional diagnostic yield over Standard Colonoscopy (i.e., the adenoma miss-rate reduction) obtained by performing CAD-EYE aided colonoscopy.
Colonoscopy is the gold standard for colorectal screening. The diagnostic accuracy of colonoscopy highly depends on the quality of inspection of the colon during the procedure. To increase detection new polyp detection systems based on artificial intelligence (AI) have been developed. However, these systems still depend on the ability of the endoscopist to adequately visualize the complete colonic mucosa, especially to detect smaller and more subtle lesions, or lesions hidden behind folds in the colon. With this study we want to combine a device to flatten the folds in the colon combined with an artificial intelligence system to further improve the detection rate of lesions during colonoscopy.