Gastroenterology

An automated machine-learning model for prognostic risk stratification of intermediate-stage hepatocellular carcinoma after transarterial chemoembolization.

BACKGROUND: Currently, there is still a lack of noninvasive, automated, and accurate machine-learnin...

DCAF13 Regulates Cell Proliferation and Immune Escape of Hepatocellular Carcinoma Through Activating the NF-κB Pathway.

Hepatocellular carcinoma (HCC), the third leading cause of global cancer deaths, has a high unmet cl...

Development of a tongue image-based machine learning tool for the diagnosis of colorectal cancer: a prospective multicentre clinical cohort study.

Colorectal cancer (CRC) remains a persistent major global health burden, with traditional diagnostic...

Heterogeneity Habitats -Derived Radiomics of Gd-EOB-DTPA Enhanced MRI for Predicting Proliferation of Hepatocellular Carcinoma.

OBJECTIVE: To construct and validate the optimal model for preoperative prediction of proliferative ...

Diagnostic performance of artificial intelligence based on contrast-enhanced computed tomography in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis.

PURPOSE: This meta-analysis systematically evaluated the diagnostic performance of artificial intell...

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, us...

A ubiquitous and interoperable deep learning model for automatic detection of pleomorphic gastroesophageal lesions.

In recent years, artificial intelligence (AI) has been widely explored to enhance capsule endoscopy ...

Artificial Intelligence-Powered Insights into Polyclonality and Tumor Evolution.

Recent studies have revealed that polyclonality-where multiple distinct subclones cooperate during e...

Benford's Law in histology.

Digital pathology is an emerging field that is gaining popularity due to its numerous advantages ove...

AI-Driven Integration of Transcriptomics, Quantum Mechanics, and Physiology for Predicting Drug-Induced Liver Injury in Data-Limited Scenarios.

Drug-induced liver injury (DILI) is a significant concern with prescription medications and suppleme...

An MOF-Enhanced Anti-Fouling Immunoprobe Platform for Efficient Direct Screening of Pancreatic Cancer.

Monitoring biomarkers offers insights for early disease (e.g., cancer, chronic diseases) screening, ...

Impact of Introducing Artificial Intelligence on Colonoscopy: A Retrospective Study on Potential Benefits and Drawbacks.

BACKGROUND AND AIM: Computer-aided detection (CAD) can improve adenoma detection rates (ADRs); howev...

Machine learning models integrating dietary data predict all-cause mortality in U.S. NAFLD patients: an NHANES-based study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease, c...

An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery.

BACKGROUND: Liver metastasis is the most frequent site of distant metastasis in pancreatic ductal ad...

Risk factors and prediction of distant metastasis (DM) of colon adenocarcinoma: a logistic regression and machine learning study based on surveillance, epidemiology, and end results (SEER) database.

Development and validation of a small-sample machine learning model to predict 5-year overall survival in patients with hepatocellular carcinoma.

BACKGROUND: Early-onset hepatocellular carcinoma (HCC) is insidious, with characteristics of easy me...

MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients.

Traditional methods for measuring body composition in CT scans rely on labor-intensive manual deline...

MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma.

BACKGROUND: To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellu...

Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.

BACKGROUND: Detecting liver tumors via computed tomography (CT) scans is a critical but labor-intens...

Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.

BACKGROUND: Immunohistochemistry (IHC) is a critical tool for tumor diagnosis and treatment, but it ...

Preoperative MRI-based deep learning reconstruction and classification model for assessing rectal cancer.

BACKGROUND: To determine whether deep learning reconstruction (DLR) could improve the image quality ...