Gastroenterology

Enhancing the prediction accuracy of pathological downstaging in locally advanced rectal cancer using deep learning models with preoperative MRI and clinicopathological data.

PURPOSE: Conventional magnetic resonance imaging (MRI) for locally advanced rectal cancer (LARC) involves challenges in evaluating and predicting the preoperative response to neoadjuvant chemoradiotherapy (CRT). Deep learning (DL) methods incorporating MRI are widely utilized for cancer diagnosis and outcome prediction. This study aims to develop and validate DL models based on preoperative T2-wei...

Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.

PURPOSE: Small cell lung cancer (SCLC) is an aggressive disease with diverse phenotypes that reflect the heterogeneous expression of tumor-related genes. Recent studies have shown that neuroendocrine (NE) transcription factors may be used to classify SCLC tumors with distinct therapeutic responses. The liver is a common site of metastatic disease in SCLC and can drive a poor prognosis. Here, we pr...

Accurate fatty liver disease diagnosis with a multi-source feature fusion model on the segmented tongue image dataset.

INTRODUCTION: More than 100 million individuals in rural areas of China are suffered from Fatty Liver Disease (FLD). However, health clinics in remote...

Machine learning demonstrates normal fetal Doppler velocimetry associated with reduced risk of necrotizing enterocolitis among preterm infants with growth restriction.

BackgroundNecrotizing enterocolitis (NEC) is an intestinal ischemic disease that affects preterm infants with fetal growth restriction (FGR). The role...

Microbiome-transcriptome-histology triad enhances survival risk stratification in multiple cancers.

Accurate prognostic stratification is essential for optimizing postoperative therapeutic strategies in oncology. While deep learning approaches have s...

Development of a clinical-CT-radiomics nomogram for predicting endoscopic red color sign in cirrhotic patients with esophageal varices.

PURPOSE: To evaluate the predictive performance of a clinical-CT-radiomics nomogram based on radiomics signature and independent clinical-CT predictor...

Impact of artificial intelligence-assisted colonoscopy on gastroenterology fellow performance: a pragmatic randomized controlled trial.

BACKGROUND AND AIMS: The substantial miss rate during screening and surveillance colonoscopy, particularly for the right side, underscores the need to...

AI for colon cancer: A focus on classification, detection, and predictive modeling.

PURPOSE: Artificial Intelligence (AI) is increasingly recognized for its potential in improving the detection, classification, prediction, and segment...

Transformer-Based Feature Extraction and Optimized Deep Neural Network for Gastric Cancer Detection.

Gastric cancer is among the most common diseases worldwide and can lead to fatal outcomes. Early diagnosis significantly increases the success of trea...

Refining the Classroom: The Self-Supervised Professor Model for Improved Segmentation of Locally Advanced Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related deaths, with accurate staging being critical for treatment planning. Auto...

Multitask radioclinical decision stratification in non-metastatic colon cancer: integrating MMR status, pT staging, and high-risk pathological factors.

OBJECTIVES: Constructing a multi-task global decision support system based on preoperative enhanced CT features to predict the mismatch repair (MMR) s...

Precise prevention of DEHP induced hepatic fibrosis: Early identifying high-risk populations, revealing key factors, and applying targeted intervention.

Metabolic dysfunction-associated fatty liver disease (MAFLD) poses a serious threat to human health. Hepatic fibrosis is a decisive factor in the deat...

Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.

PURPOSE: Current radiomic approaches inadequately resolve spatial intratumoral heterogeneity (ITH) in esophageal squamous cell carcinoma (ESCC), limit...

Nivolumab plus ipilimumab for potentially resectable hepatocellular carcinoma: Long-term efficacy and biomarker exploration.

BACKGROUND & AIMS: Immune checkpoint inhibitor-based combination therapy has demonstrated high objective response rates in patients with hepatocellula...

Concurrent AI assistance with LI-RADS classification for contrast enhanced MRI of focal hepatic nodules: a multi-reader, multi-case study.

PURPOSE: The Liver Imaging Reporting and Data System (LI-RADS) assessment is subject to inter-reader variability. The present study aimed to evaluate ...

ToBaFu: Topology-based fusion model for classification of two-dimensional cancer images.

Medical images play a pivotal role in disease diagnosis. Numerous studies on cancer image analysis focus on end-to-end deep neural networks, neglectin...

Prospective clinical validation of a novel artificial intelligence system for real-time detection of solid pancreatic masses during endoscopic ultrasonography.

Endoscopic ultrasonography (EUS) is the most sensitive modality for accurately establishing a tissue diagnosis in patients with solid pancreatic masse...

Improving the performance of the echinococcosis diagnosis model based on serum Raman spectroscopy via the integration of convolutional neural network and support vector machine.

Echinococcosis is a zoonotic parasitic disease characterized by its insidious nature and severe health impacts. Rapid and accurate screening is crucia...

Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.

BACKGROUND: Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of ...

Integration of machine learning and SERS technology for precise classification and diagnosis of colon cancer from plasma samples.

Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning offers a transformative label-free approach for colorectal cancer detection,...