Gastroenterology

Interpretable machine learning model for predicting anastomotic leak after esophageal cancer surgery via LightGBM.

BACKGROUND: Postoperative anastomotic leakage (AL) is a severe complication following esophageal cancer surgery, that often leads to a poor prognosis. This study aims to develop an interpretable machine learning (ML) model to predict AL occurrence and identify associated risk factors.

Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by high heterogeneity, and its molecular features and microenvironment complexity contribute to varied patient prognoses. Anoikis, a form of programmed cell death, is considered closely linked to the development and progression of liver cancer. This study aimed to analyze the cellular heterogeneity of HCC using single-cell sequencing and ...

Exploration of the potential therapeutic effects and targets of Coriandrum sativum on non-erosive esophagitis based on bioinformatics and molecular dynamics simulation.

Gastroesophageal reflux disease (GERD) is one of the most frequently diagnosed gastrointestinal disorders, adversely affecting quality of life. Corian...

Identification of multiomics and immune infiltration-associated biomarkers for early gastric cancer: a machine learning-based diagnostic model development study.

BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with early diagnosis remaining a significant challenge. Availab...

Machine Learning for Predicting the Low Risk of Postoperative Pancreatic Fistula After Pancreaticoduodenectomy: Toward a Dynamic and Personalized Postoperative Management Strategy.

BACKGROUND: Postoperative pancreatic fistula (POPF) remains one of the most relevant complications following pancreaticoduodenectomy (PD), significant...

Identification and Validation of Key Genes Involved in the Coupling of Mitochondria-Associated Endoplasmic Reticulum Membrane in Hemorrhoidal Disease.

BACKGROUND: Hemorrhoidal disease (HD) is the most prevalent rectal disorder, with various cellular processes influenced by the mitochondria-associated...

Deep learning reconstruction for T2-weighted and contrast-enhanced T1-weighted magnetic resonance enterography imaging in patients with Crohn's disease: Assessment of image quality and clinical utility.

PURPOSE: To investigate the image quality of deep learning-reconstructed T2-weighted half-Fourier single-shot turbo spin echo (DL T2 HASTE) and contra...

One-Drop Serum Screening Test to Monitor Tissue Iron Accumulation.

Although iron is an essential element for vital body functions, iron overload (IO) is accompanied by significant cellular damage due to its accumulati...

Comparative analysis of natural language processing methodologies for classifying computed tomography enterography reports in Crohn's disease patients.

Imaging is crucial to assess disease extent, activity, and outcomes in inflammatory bowel disease (IBD). Artificial intelligence (AI) image interpreta...

Comparison of an Attention-Based Multiple Instance Learning (MIL) With a Visual Transformer Model: Two Weakly Supervised Deep Learning (DL) Algorithms for the Detection of Histopathologic Lesions in the Rat Liver to Distinguish Normal From Abnormal.

The histopathologic evaluation of regulatory toxicity studies using artificial intelligence (AI) has the potential to increase study efficiency. For e...

Advantages of deep learning reconstruction algorithm in ultra-high-resolution CT for the diagnosis of pancreatic cystic neoplasm.

PURPOSE: This study aimed to evaluate the image quality and clinical utility of a deep learning reconstruction (DLR) algorithm in ultra-high-resolutio...

Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses.

The tumor microenvironment (TME) plays a pivotal role in tumor progression, immune evasion, and therapeutic responses. Among its key components, endot...

Systematic Exploration of Potential Toxicity Targets and Molecular Mechanisms of Emerging Short-Chain PFAS Substitutes: PFBA- and PFBS-Induced Hepatocellular Carcinoma Based on Toxicity Network Analysis, Machine Learning, and Biomimetic Calculations.

Perfluorobutanoic acid (PFBA) and perfluorobutanesulfonic acid (PFBS) are short-chain alternatives to traditional perfluoroalkyl and polyfluoroalkyl s...

Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs.

Distinguishing high-risk intraductal papillary mucinous neoplasms (IPMNs), pancreatic cysts requiring surgery, from low-risk lesions remains a clinica...

Two decades of progress in gastric cancer peritoneal metastasis: a bibliometric perspective on molecular mechanisms and therapeutic innovations.

BACKGROUND: Gastric cancer (GC) is the fifth most common malignant tumor worldwide. The peritoneum is a common site of metastasis in advanced GC, and ...

Artificial intelligence in endoscopy and colonoscopy: a comprehensive bibliometric analysis of global research trends.

BACKGROUND: Artificial intelligence (AI) has revolutionized the field of gastroenterology, particularly in endoscopic and colonoscopic procedures. The...

Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration.

OBJECTIVE: Diagnosis of hepatocellular carcinoma (HCC) remains challenging for clinicians. Machine learning approaches and big data analyses are viabl...

Machine Learning for Predicting Colorectal Cancer and Advanced Colorectal Polyps: A Systematic Review and Meta-Analysis.

INTRODUCTION: Machine learning (ML) has become increasingly pivotal in health care, particularly in colorectal cancer (CRC) detection and diagnosis wi...

Ultrasound image-based contrastive fusion non-invasive liver fibrosis staging algorithm.

OBJECTIVE: The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is a...

Hepatitis B In Silico Trials Capture Functional Cure, Indicate Mechanistic Pathways, and Suggest Prognostic Biomarker Signatures.

In silico trials, utilizing mathematical models calibrated with clinical data, present a transformative approach to expedite drug development. We prop...