Active clinical trials for "Adenoma"

The investigators hypothesize that the combined use of CADe system (ENDOAID) and mucosal exposure device (Endocuff Vision®) would improve the adenoma detection rate when compared to CADe system alone.

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 EAGLE study is a prospective randomized controlled multicenter parallel design trial, for the assessment of clinical performance of the CADDIE device and to confirm that the device performs as expected.

The study was proposed to include 20 patients with clinical suspicion of primary aldosteronism for [18F]AlF-NOTA-pentixather PET/CT imaging and to analyze the specificity and sensitivity of [18F]AlF-NOTA-pentixather PET/CT for the diagnosis of APA by comparison with the final pathological findings.

The purpose of this study is to assess if artificial intelligence aid colonoscopy colonoscopy is superior to conventional colonoscopy for the detection of adenomas during surveillance colonoscopy in patients with Lynch syndrome.

The goal of this study is to assess whether using PTeye (AiBiomed, Santa Barbara, CA) - a NIRAF detection modality - can improve patient outcomes and reduce healthcare associated costs after parathyroid surgeries. By being able to quickly and definitively locate parathyroid glands while in the operating room, the duration of surgical procedure could be further reduced. In addition, the number of frozen section biopsy and associated costs can be minimized. Furthermore, repeat surgeries as a result of missing a diseased parathyroid gland at the time of the initial parathyroidectomy for hyperparathyroidism could potentially be avoided.

Colonoscopy is considered the gold standard for diagnosis of colonic polyps. However, it was reported that colonoscopy could still miss colonic polyps. Many attempts have been made to improve the detection rate of colonoscopy. Artificial intelligence (AI) is a promising new technique to improve detection rate of colonic adenoma. However, it remains uncertain whether whether the combined use of Endocuff and AI assisted examination could help to further improve the adenoma detection rate. This is a prospective randomized trial comparing the use of endocuff with AI, AI alone or conventional colonoscopy examination on adenoma detection rate.

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.

The goal of this multicenter prospective longitudinal study is to study the long-term impact of multimodal treatment (chemotherapy, radiotherapy and surgery) in adult brain and base of skull tumors on neurocognitive functioning. All included patients will complete a self-report inventory (subjective cognitive functioning, QoL, confounders), a cognitive test battery, an advanced MR at multiple timepoints. Moreover, toxicity will be scored according to the CTCAEv5.0 in these patients over time.

As the clinical manifestations of pituitary neuroendocrine tumors vary greatly, 2.7-15% of them are resistant to conventional treatments such as surgery, drug therapy and radiotherapy, and often relapse or regrow in the early postoperative period, which is invasive and has a poor prognosis. Therefore, it is important to find imaging, histological or serum molecular markers for early prediction of the invasiveness and clinical prognosis of pituitary neuroendocrine tumors. The aim of this study is to observe the changes of biomarkers and imaging features in serum or tissues of pituitary neuroendocrine tumors during the course of disease and treatment, and to explore the biomarkers and imaging features that can predict the proliferation, progression and recurrence risk of pituitary neuroendocrine tumors after medical or surgical treatment.