Gastroenterology

A High-resolution T2WI-based Deep Learning Model for Preoperative Discrimination Between T2 and T3 Rectal Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES: To construct a deep learning model (DL) based on high-resolution T2-weight...

Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge wi...

The efficacy and toxicity equilibrium of emodin for liver injury: A bidirectional meta-analysis and machine learning.

BACKGROUND: Emodin, a hepatoprotective agent derived from various herbs, exhibits dual effects on li...

Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).

Esophageal cancer (EC) is one of the leading causes of cancer-related mortality worldwide, still fac...

Novel CT radiomics models for the postoperative prediction of early recurrence of resectable pancreatic adenocarcinoma: A single-center retrospective study in China.

PURPOSE: To assess the predictive capability of CT radiomics features for early recurrence (ER) of p...

Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases.

Reports indicate a growing role for artificial intelligence (AI) in the evaluation of pancreaticobil...

Assessment of POPs in foods from western China: Machine learning insights into risk and contamination drivers.

Persistent organic pollutants (POPs), including PCDD/Fs, PCBs, and PBDEs, are major environmental an...

Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis.

Non-alcoholic fatty liver disease (NAFLD) affects about 25% of adults worldwide. Its advanced form, ...

Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.

Early and accurate identification of patients at high risk of metabolic dysfunction-associated steat...

Characterization of fibrotic liver tissue microstructure for predicting shear wave speed variability: a machine-learning-based computational study.

This study aimed to establish a link between the microstructure of simulated fibrotic liver tissues ...

Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.

An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation....

A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.

BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to c...

Preclinical evidence construction for epigallocatechin-3-gallate against non-alcoholic fatty liver disease: a meta-analysis and machine learning study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) has emerged as a significant health concern wo...

Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis.

BACKGROUND: Portal vein system thrombosis (PVST) is a common and potentially life-threatening compli...

Habitat Radiomics Based on MRI for Predicting Metachronous Liver Metastasis in Locally Advanced Rectal Cancer: a Two‑center Study.

RATIONALE AND OBJECTIVES: This study aimed to explore the feasibility of using habitat radiomics bas...

Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.

BACKGROUND: Malignant digestive tract tumors are highly prevalent and fatal tumor types globally, of...

Preoperative assessment in lymph node metastasis of pancreatic ductal adenocarcinoma: a transformer model based on dual-energy CT.

BACKGROUND: Deep learning(DL) models can improve significantly discrimination of lymph node metastas...

An artificial intelligence model utilizing endoscopic ultrasonography for differentiating small and micro gastric stromal tumors from gastric leiomyomas.

BACKGROUND: Gastric stromal tumors (GSTs) and gastric leiomyomas (GLs) represent the primary subtype...

Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis.

BACKGROUND: Gastric cancer (GC) is a highly aggressive and heterogeneous cancer with extremely compl...

Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.

To explore the value of applying the MRI-based radiomic nomogram for predicting lymph node metastasi...

Letter to the editor: "Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study".