No abstract

Gastrointestinal (GI) cancers, including esophageal, gastric, colorectal, liver, and pancreatic cancers, remain as one of the leading causes of death worldwide, with a large proportion accounting for fatalities related to metastatic disease. Invasion of primary cancer occurs by the actin cytoskeleton remodeling, including the formation of the filopodia, stereocilia, and other finger-like membrane protrusions. The crucial step of actin remodeling in the malignant cells is mediated by the fascin protein family, with fascin-1 being the most active. Fascin-1 is an actin-binding protein that cross-links filamentous actin into tightly packed parallel bundles, giving rise to finger-like cell protrusions, thus equipping the cell with the machinery necessary for adhesion, motility, and invasion. Thus, fascin-1 has been noted to be a key component for determining patient diagnosis and treatment plan. Indeed, the overexpression of fascin-1 in GI tract cancers has been associated with a poor clinical prognosis and metastatic progression. Moreover, fascin-1 has received attention as a potential therapeutic target for metastatic GI tract cancers. In this review, we provide an up-to-date literature review of the role of fascin-1 in the initiation of GI tract cancers, metastatic progression, and patients’ clinical outcomes.

Background: Pancreatic cancer (PC) is a highly fatal malignancy with a global overall 5-year survival of under 10%. Screening of PC is not recommended outside of clinical trials. Endoscopic ultrasonography (EUS) is a very sensitive test to identify PC but lacks specificity and is operator-dependent, especially in the presence of chronic pancreatitis (CP). Artificial Intelligence (AI) is a growing field with a wide range of applications to augment the currently available modalities. This study was undertaken to study the effectiveness of AI with EUS in the diagnosis of PC. Methods: Studies from MEDLINE and EMBASE databases reporting the AI performance applied to EUS imaging for recognizing PC. Data were analyzed using descriptive statistics. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess the quality of the included studies. Results: A total of 11 articles reported the role of EUS in the diagnosis of PC. The overall accuracy, sensitivity, and specificity of AI in recognizing PC were 80–97.5%, 83–100%, and 50–99%, respectively, with corresponding positive predictive value (PPV) and negative predictive value (NPV) of 75–99% and 57–100%, respectively. Types of AI studied were artificial neural networks (ANNs), convolutional neural networks (CNN), and support vector machine (SVM). Seven studies using other than basic ANN reported a sensitivity and specificity of 88–96% and 83–94% to differentiate PC from CP. Two studies using SVM reported a 94–96% sensitivity, 93%–99% specificity, and 94–98% accuracy to diagnose PC from CP. The reported sensitivity and specificity of detection of malignant from benign Intraductal Papillary Mucinous Neoplasms (IPMNs) was 96% and 92%, respectively. Conclusion: AI reported a high sensitivity with high specificity and accuracy to diagnose PC, differentiate PC from CP, and differentiate benign from malignant IPMN when used with EUS.

Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.

Background and study aims Post-ERCP pancreatitis (PEP) is the most common complication attributed to the procedure, its incidence being approximately 9.7 %. Numerous studies have evaluated the predictive efficacy of post-procedure serum amylase and lipase levels but with varied procedure-to-test time intervals and cut-off values. The aim of this meta-analysis was to present pooled data from available studies to compare the predictive accuracies of serum amylase and lipase for PEP. Patients and methods A total of 18 studies were identified after a comprehensive search of various databases until June 2021 that reported the use of pancreatic enzymes for PEP. Results The sample size consisted of 11,790 ERCPs, of which PEP occurred in 764 (6.48 %). Subgroups for serum lipase and amylase were created based on the cut-off used for diagnosing PEP, and meta-analysis was done for each subgroup. Results showed that serum lipase more than three to four times the upper limit of normal (ULN) performed within 2 to 4 hours of ERCP had the highest pooled sensitivity (92 %) for PEP. Amylase level more than five to six times the ULN was the most specific serum marker with a pooled specificity of 93 %. Conclusions Our analysis indicates that a lipase level less than three times the ULN within 2 to 4 hours of ERCP can be used as a good predictor to rule out PEP when used as an adjunct to patient clinical presentation. Multicenter randomized controlled trials using lipase and amylase are warranted to further evaluate their PEP predictive accuracy, especially in high-risk patients.