View clinical trials related to Colorectal Polyp.
Filter by:Bowel cancer is one of the most common cancers and the best method of diagnosing it is through endoscopic examination of the bowel (colonoscopy). Pre-cursors of bowel cancer are called polyps which can be detected and removed at the time of the colonoscopy. This reduces the chance of developing bowel cancer. There are different types of polyp ranging from completely harmless to those that may develop into cancer over time. Advances in technology mean more polyps are being detected and it is possible to predict the type of polyp. Therefore there is a new strategy in endoscopy whereby when a small polyp is detected, a prediction of polyp type is made, the polyp removed and then discarded rather than sending to the laboratory, thereby reducing costs to health services. In the hands of experts, accuracies in predicting polyp type is similar to when the polyp is removed and sent to the lab for analysis. Whilst experts can do this, non-experts cannot reach these standards and there is a need for effective training. The aim of the study is to compare the effectiveness of two training methods: Didactic face-to-face training and computer-based self-learning on the ability of trainees at predicting polyp type.
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist. If an endoscopist would be able to correctly predict the histology of these diminutive polyps during colonoscopy, histopathological examination could be omitted and practise could become more time- and cost-effective. Studies have shown that prediction of histology by the endoscopist remains dependent on training and experience and varies greatly between endoscopists, even after systematic training. Computer aided diagnosis (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to a group of endoscopists performing optical diagnosis during real-time colonoscopy are lacking. Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during colonoscopy with high accuracy and to compare the performance of this system to a group of endoscopist performing optical diagnosis, with the histopathology as the gold standard. Study design: Multicentre, prospective, observational trial. Study population: Consecutive patients who undergo screening colonoscopy (phase 2) Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists. Histopathology is used as the gold standard.
Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images. Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
Colorectal polyps are risk factors for cancer. Early detection of polyps is critical for colorectal cancer management. However, the diagnosis rate of patients with colorectal polyps is still low. Therefore, we design this study to access whether pre-endoscopic screening risk assessment of genetic and environmental risk factors could improve diagnosis rate of colorectal polyps.
The main aim of this study is to determine whether the assessment of the invasive pattern based on NBI with dual focus/magnification or BLI with magnification ± chromoendoscopy (NBI+CE) for predicting deep invasion is significantly more accurate than the assessment based on white light endoscopy (WLE), carried out by trained endoscopists.
This study has three main purposes：screening: the first purpose is to evaluate the diagnostic value of combintion of the life risk factors and immunochemical fecal occult blood test (FIT) on detection of colorectal neoplasia in Chinese population; resection: the second objective is to investigate the complete resection rate of colorectal adenoma and risk factors of incomplete resection in China; identification and classification: the third objective is to initially establish an artificial intelegence-assissted recognition and classification system of polyp based on deep learning.
In European countries, colorectal cancer (CRC) represents an important public health problem. It is widely held view that most carcinomas develop from an adenoma-carcinoma progression. Adenoma detection rate (ADR) is a marker of high quality colonoscopy and it was inversely associated with the risk of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer after colonoscopy. Although colonoscopy is considered the gold standard for adenoma detection, it has shown some limits, so industry has aimed at increasing detection rate of adenomas providing new technologies, most of witch to detect lesions located in blind spots. ARC Endocuff Vision (AEV), the second generation of Endocuff, represents a new generation of these devices, thus assessing the diagnostic sensibility of ARC Endocuff Vision assisted colonoscopy (EAC) is an interesting challenge. Aim of the study is to compare ADR of EAC versus standard colonoscopy among FIT positive subjects in the context of CRC screening programs.
Colorectal cancer screening showed an increased incidence of malignant colorectal polyps pT1 after endoscopic excision. Their management is not yet standardized, for the presence of histological features increasing early lymph node involvement. The literature has proposed several histopathological criteria, for which the risk of lymph node metastasis can vary (6-20%), but final data are not yet available. Aim 1.To collect data about patients undergoing an endoscopic polypectomy with histologic finding of pT1, retrospectively and prospectively, dividing both databases into two groups, endoscopic group (EG) and surgical group (SG) Aim 2. To analyze retrospectively which pathological criteria can increase the risk of lymph node metastasis and to elaborate a prognostic score for lymph node metastatic risk Aim 3. To verify prospectively the prognostic score capacity on predicting lymph node metastasis Aim 4. To calculate the disease free survival, overall survival, local recurrence rate and distal recurrence rate and verify if there is a difference between EG and SG According to literature, the most important histopathological criteria to establish the high risk of lymph node metastasis are: 1. Lateral margin of healthy tissue (high risk: <1mm and piecemeal polypectomy) 2. Depth of submucosa invasion (high risk: >1000 μM or sm2-sm3 for sessile polyps; Haggitt level 4 for pedunculated polyps) 3. Vascular invasion (high risk: presence) 4. Lymphatic invasion (high risk: presence) 5. Tumor budding (high risk: presence) 6. Tumor differentiation (high risk: grade G3-G4 or mucinous) A database will be used by all participating centres for collecting clinical and pathological data. All the analyses will be centralized by the PI. Uni-multivariate analyses will be conducted at the end of data collection for retrospective arm and at 2 years of follow-up for prospective arm. Impact: This study aimed to investigate pathological risk factors for lymph node metastasis in pT1 colorectal polyps after endoscopic polypectomy; their accurate identification could lead to improve their management, avoiding useless complementary surgery. Results could change clinical practice and reduce health-related costs.
The study aims to compare the results between colonoscopies with two different attachments on the distal end of the colonoscope.
The study aims to compare the results between a standard colonoscopy to a colonoscopy with an attachment on the distal end of the colonoscope.