Gastroenterology

Diffusion-weighted imaging in rectal cancer MRI from theory to practice.

Diffusion-weighted imaging (DWI) has become a cornerstone of high-resolution rectal MRI, providing c...

Liver Retransplantation: Identifying Factors and Developing a Risk Prediction Model to Predict Futility in the Modern Era.

BACKGROUND: Liver retransplantation (rLT) results have traditionally been inferior compared with tho...

Machine-Learning-Driven Discovery of -Phenylbenzenesulfonamides as a Novel Chemotype for Lactate Dehydrogenase A Inhibition with Anti-Pancreatic Cancer Activity.

Lactate dehydrogenase A (LDHA) is a promising target for cancer therapy due to its crucial role in a...

SPP1 promotes malignant characteristics and drug resistance in hepatocellular carcinoma by activating fatty acid metabolic pathway.

Hepatocellular carcinoma (HCC) progression and prognosis are influenced by various molecular markers...

Approach to upper gastrointestinal subepithelial lesions.

INTRODUCTION: Subepithelial lesions (SELs) of the upper gastrointestinal tract are commonly encounte...

Data Extraction and Curation from Radiology Reports for Pancreatic Cyst Surveillance Using Large Language Models.

INTRODUCTION: Manual curation of radiographic features in pancreatic cyst registries for data abstra...

Mapping the colon through the colonoscope's coordinates - The Copenhagen Colonoscopy Coordinate Database.

Colonoscopy is the leading endoscopic technique when it comes to implementing artificial intelligenc...

Autoimmune gastritis detection from preprocessed endoscopy images using deep transfer learning and moth flame optimization.

Gastric Tract Disease (GTD) constitutes a medical emergency, emphasizing the critical importance of ...

How machine learning on real world clinical data improves adverse event recording for endoscopy.

Endoscopic interventions are essential for diagnosing and treating gastrointestinal conditions. Accu...

FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer ...

Recent advancement in endoscopic diagnosis for risk stratification of gastric cancer.

Approximately 90% of cases of gastric cancer (GC) are caused by Helicobacter pylori infection, and s...

ColoViT: a synergistic integration of EfficientNet and vision transformers for advanced colon cancer detection.

BACKGROUND: Colon cancer remains a leading cause of cancer-related mortality globally, highlighting ...

Radiomics in Early Detection of Pancreatic Ductal Adenocarcinoma: A Close Look at Its Current Status and Challenges to Clinical Implementation.

Radiomics is a mathematical approach to medical images to extract quantitative features generating a...

Predicting time-to-first cancer diagnosis across multiple cancer types.

Cancer causes over 10 million deaths annually worldwide, with 40.5% of Americans expected to be diag...

Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.

BACKGROUND: Neoadjuvant chemotherapy (NAC) can improve the prognosis of patients with locally advanc...

Validation of the Toronto recurrence inference using machine-learning for post-transplant hepatocellular carcinoma model.

BACKGROUND: Organ shortages require prioritizing hepatocellular carcinoma (HCC) patients with the hi...

The unwell patient with advanced chronic liver disease: when to use each score?

BACKGROUND: Prognostication in chronic liver disease and the implementation of appropriate scoring s...

Hierarchical Prediction and Perturbation of Chromatin Organization Reveal How Loop Domains Mediate Higher-Order Architectures.

The genome is folded within the dense cell nucleus in a hierarchical manner, resulting in complex in...

Response to Letter to the Editor 'Comments on the potential of an artificial intelligence for diagnosing MRI images in rectal cancer: multicenter collaborative trial' by Mao and Li.

Prediction of Internal Exposures after Virtual Oral Doses of Disparate Chemicals in Rats and Humans Using Simplified Physiologically Based Pharmacokinetic Models with Generated Input Parameters.

Toxicological evaluation of industrial chemicals with a broad range of chemical structures, for exam...

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts l...