Gastroenterology

Small-bowel capsule endoscopy - from clinic to couch: time to go green?

PURPOSE OF REVIEW: Small-bowel capsule endoscopy (SBCE) has transformed small-intestine diagnostics by enabling direct, noninvasive mucosal visualization. As healthcare systems increasingly prioritize value-based and sustainable care, this review explores recent advances in SBCE, focusing on home-delivery models, telemedicine, artificial intelligence (AI), and environmental impact. RECENT FINDINGS...

Utilisation of intramuscular and intermuscular fat to develop a new skeletal muscle grading score which can predict treatment outcomes for locally advanced rectal cancer.

BACKGROUND: Sarcopenia has been widely studied in rectal cancer with increasing evidence to suggest that other body composition parameters, in particular adipose tissue, have an important role. Advances in artificial intelligence (AI) now allow 3D body composition analysis of intermuscular/intramuscular adipose tissue (IMAT) from CT scans. This study aimed to develop and evaluate a skeletal muscle...

The Anti-proliferative and Pro-apoptotic Effects of Iranian Viper Venom (Vipera raddei kurdistanica) on Human Gastric Cancer Cells (C615) are Mediated by PI3K/AKT/mTOR Signalling Pathway Inhibition.

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies worldwide. Since the PI3K/AKT/mTOR signalling pathway plays a crucial role in tu...

Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence.

BACKGROUND: Endoscopic submucosal dissection (ESD) serves as a critical treatment modality for superficial esophageal cancer. However, non-curative re...

Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics.

Gastrointestinal (GI) cancers, particularly colorectal cancer, continue to be a major contributor to global cancer-related morbidity and mortality. De...

Risk prediction for chronic atrophic gastritis using a random forest model: A multicenter study.

BACKGROUND: Chronic atrophic gastritis (CAG) is a significant precancerous condition of gastric cancer (GC). CAG often lacks typical symptoms in its e...

Radiomics-based model for predicting neoadjuvant therapy response in esophageal cancer: Limitations and suggestions.

A core challenge in the diagnosis and treatment of esophageal cancer (EC) lies in accurately identifying patients who will benefit from neoadjuvant th...

Application value of multiphase contrast-enhanced computed tomography radiomics in preoperative evaluation of peritoneal metastasis in gastric cancer.

BACKGROUND: Peritoneal metastasis occurs in 10%-45% of gastric cancer patients and significantly impacts prognosis and treatment decisions. Traditiona...

Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.

BACKGROUND: Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide and is often diagnosed at advanced stages, reducing ...

Toward Timely Diagnosis of Pancreatic Cancer: Revolutionizing Early Detection Through Genomics, Artificial Intelligence, and Noninvasive Biomarkers.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive cancers, typically diagnosed at an advanced stage due to its su...

Association between monocyte-to-lymphocyte ratio and mortality in patients with acute pancreatitis requiring intensive care unit admission: a retrospective cohort study and predictive model establishment based on machine learning.

The purpose of this study was to evaluate the predictive value of monocyte-to-lymphocyte ratio (MLR) on the short-term (28 days) and long-term (365 da...

GAST-NET: A multi-modal and multi-task deep learning framework for preoperative prediction of perineural invasion and prognostic risk in gastric cancer.

BACKGROUND: Preoperative imaging prediction of perineural invasion in gastric cancer (GC-PNI) mainly relies on tumour characteristics and clinical var...

H-DCA Net: Hierarchical dual-branch coordinate attention framework for multi-scale gastric histopathology diagnosis.

BACKGROUND AND OBJECTIVE: Early and accurate diagnosis of gastric cancer is crucial for improving patient prognosis. However, conventional histopathol...

Peripheral immune landscape in pancreatic ductal adenocarcinoma reveals expansion of effector states with disease progression.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy often diagnosed late due to the limited sensitivity and specificity of current m...

Real-time intraoperative depth estimation in transsphenoidal surgery using deep learning: A feasibility study.

PURPOSE: Endoscopic endonasal and transcranial approaches are used for the resection of various pathological lesions in neurosurgery, especially pitui...

Deep learning for histopathological diagnosis of esophageal squamous cell carcinoma in biopsies: A multicenter analysis.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer mortality in Asia, where histopathological diagnosis of endoscopic bi...

Development and external validation of a machine learning model for predicting in-hospital mortality in acute liver failure.

BACKGROUND: Acute liver failure (ALF) is a rapidly progressive and life-threatening condition that requires accurate risk stratification. Existing pro...

A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators.

OBJECTIVE: Sepsis-associated liver injury (SALI) occurs in approximately 40% of sepsis cases and is linked to high mortality, a challenge that may ste...

Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients.

Hepatocellular carcinoma (HCC) remains a major global health challenge due to its molecular heterogeneity, late diagnosis, and limited therapeutic opt...

Association of cardiovascular-kidney-metabolic syndrome stages with MASLD prevalence and liver fibrosis severity: evidence from traditional and machine learning approaches.

INTRODUCTION: Cardiovascular-Kidney-Metabolic (CKM) syndrome reflects the convergence of cardiovascular, renal, and metabolic disorders. Metabolic dys...