Therapeutic Advances in Gastrointestinal Endoscopy最新文献

Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence-assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists' optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of "resect-and-discard" and "diagnose-and-leave" strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.

Colorectal cancer is common and can be devastating, with long-term survival rates vastly improved by early diagnosis. Colon capsule endoscopy (CCE) is increasingly recognised as a reliable option for colonic surveillance, but widespread adoption has been slow for several reasons, including the time-consuming reading process of the CCE recording. Automated image recognition and artificial intelligence (AI) are appealing solutions in CCE. Through a review of the currently available and developmental technologies, we discuss how AI is poised to deliver at the forefront of CCE in the coming years. Current practice for CCE reporting often involves a two-step approach, with a 'pre-reader' and 'validator'. This requires skilled and experienced readers with a significant time commitment. Therefore, CCE is well-positioned to reap the benefits of the ongoing digital innovation. This is likely to initially involve an automated AI check of finished CCE evaluations as a quality control measure. Once felt reliable, AI could be used in conjunction with a 'pre-reader', before adopting more of this role by sending provisional results and abnormal frames to the validator. With time, AI would be able to evaluate the findings more thoroughly and reduce the input required from human readers and ultimately autogenerate a highly accurate report and recommendation of therapy, if required, for any pathology identified. As with many medical fields reliant on image recognition, AI will be a welcome aid in CCE. Initially, this will be as an adjunct to 'double-check' that nothing has been missed, but with time will hopefully lead to a faster, more convenient diagnostic service for the screening population.

The global obesity pandemic is among the most significant public health crises today. Furthermore, obesity remains a major risk factor for many weight-related comorbid conditions including cardiovascular disease, type 2 diabetes mellitus, liver disease, and cancer. Endoscopic bariatric therapies are currently on the rise as a new tool in the fight against the obesity epidemic, offering patients an alternative to more invasive surgery and a more effective option than diet and lifestyle modifications. The aim of this review article is to summarize the current literature regarding endoscopic bariatric therapies and their impact on obesity and its associated metabolic complications.

Colorectal cancer is the third most common cancer worldwide, and its incidence continues to grow. Approximately one-third of patients with colorectal cancer develop liver metastases during the natural course of disease. Complete surgical resection is associated with very low mortality in colorectal liver metastasis patients, but only a small fraction of colorectal liver metastasis patients fulfill the selection criteria for surgical treatment. We herein describe a high-risk stage-IV rectal carcinoma patient who was initially unresectable according to the National Comprehensive Cancer Network guidelines with a clinical risk score of 4 but received conversion surgery combined with systemic chemotherapy and achieved a favorable long-term clinical outcome (pathologic complete response) of approximately 28 months. Furthermore, serial circulating tumor DNA monitoring using next-generation sequencing provided a comprehensive view of the patient's clinical and pathologic status for better clinical decision support over the course of the disease. The absence of circulating tumor DNA/cells after conversion surgery was correlated with pathologic complete response. This case study not only demonstrated that a curative oncosurgical approach could be considered for high-risk colorectal liver metastasis patients under specific circumstances but also highlighted the role of circulating tumor DNA monitoring to gain further insight into the evolution of a patient's response over time.

Achalasia is a primary esophageal motility disorder characterized by the loss of inhibitory neurons in the myenteric plexus, resulting in impaired relaxation of the esophagogastric junction. Achalasia is an incurable disease, and the treatment modalities are aimed at disruption of the esophagogastric junction and vary widely from pharmacological to endoscopic to surgical. Traditional endoscopic therapy includes pneumatic dilation, botulinum toxin injection, and peroral endoscopic myotomy. This review aims to provide an overview of the endoscopic management of achalasia, while focusing on the utilization of peroral endoscopic myotomy and other novel approaches.

In this article, we aim to provide general principles as well as personal views for colonic capsule endoscopy. To allow an in-depth understanding of the recommendations, we also present basic technological characteristics and specifications, with emphasis on the current as well as the previous version of colonic capsule endoscopy and relevant software. To date, there is no scientific proof to support the optimal way of reading a colonic capsule endoscopy video, or any standards or guidelines exist. Hence, any advice is a mixture of recommendations by the capsule manufacturer and experts' opinion. Furthermore, there is a paucity of data regarding the use of term(s) (pre-reader/reader-validator) in colonic capsule endoscopy. We also include a couple of handy tables in order to get info at a glance.

Introduction: The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading.

Methods: Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning-based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis.

Results and conclusion: Our evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation.

Plain language summary: Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.

Data on prognostic factors associated with outcome following resection of perihilar cholangiocarcinoma vary. We sought to define and characterize current available evidence on prognostic factors associated with perihilar cholangiocarcinoma after resection. The PubMed, Embase, and Cochrane library were systematically searched for relevant studies published before December 2019. Prognostic factors were identified from multivariate regression analyses in studies. Only high-quality studies were included (Newcastle-Ottawa Scale > 6 stars). A total of 45 studies involving 7338 patients were analyzed. The meta-analysis demonstrated that serum bilirubin levels (hazard ratio: 1.76, 95% confidence interval: 1.27-2.44), serum CA19-9 levels (hazard ratio: 1.32, 95% confidence interval: 1.05-1.65), tumor size (hazard ratio: 1.27, 95% confidence interval: 1.04-1.55), major vascular involvement (hazard ratio: 1.61, 95% confidence interval: 1.09-2.38), distance metastasis (hazard ratio: 17.60, 95% confidence interval: 2.01-154.09), perioperative blood transfusion (hazard ratio: 1.36, 95% confidence interval: 1.15-1.62), T-stage (hazard ratio: 1.96, 95% confidence interval: 1.47-2.61), lymph node metastasis (hazard ratio: 2.06, 1.83-2.31), resection margin status (hazard ratio: 2.34, 95% confidence interval: 1.89-2.89), not-well histology differentiation (hazard ratio: 2.03, 95% confidence interval: 1.69-2.44), perineural invasion (hazard ratio: 2.37, 95% confidence interval: 1.59-3.55), and lymphovascular invasion (hazard ratio: 1.41, 95% confidence interval: 1.15-1.73) were prognostic factors for poorer overall survival. Adjuvant chemotherapy (hazard ratio: 0.37, 95% confidence interval: 0.25-0.55) had a positive effect on prolonged overall survival. In addition, positive resection margin status (hazard ratio: 1.96, 95% confidence interval: 1.47-2.61) and lymph node metastasis (hazard ratio: 2.06, 95% confidence interval: 1.83-2.31) were associated with poorer disease-free survival. The prognostic factors identified in the present meta-analysis can be used to characterize patients in clinical practice and enrich prognostic tools, which could be included in future trial designs and generate hypotheses to be tested in future research to promote personalized treatment.