Gastroenterology

Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics.

OBJECTIVES: This study aims to develop and validate a novel radiomics model utilizing magnetic resonance imaging (MRI) to predict progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) who are receiving a combination of immune checkpoint inhibitors (ICIs) and antiangiogenic agents. This is an area that has not been previously explored using MRI-based radiomic...

Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer.

OBJECTIVES: To establish and validate deep learning (DL) models based on pre-treatment multiparametric magnetic resonance imaging (MRI) images of primary rectal cancer and basic clinical data for the prediction of synchronous liver metastases (SLM) in patients with Rectal cancer (RC).

BoostDILI: Extreme Gradient Boost-Powered Drug-Induced Liver Injury Prediction and Structural Alerts Generation.

Over the past 60 years, drug-induced liver injury (DILI) has played a key role in the withdrawal of marketed drugs due to safety concerns. Early predi...

Classification of disease subtypes in medical spectroscopy for multi-category sample imbalances based on grouping and hierarchical learning.

In the medical domain, the challenges in sample acquisition and collection often result in imbalanced training sets for multi-class models, especially...

Hidden threats beneath: uncovering the bio-accessible hazards of chromite-asbestos mine waste and their impacts on rice components via multi-machine learning algorithm.

The chromite-asbestos mining leaves behind tonnes of toxic waste, contaminating nearby agricultural fields with potentially toxic elements (PTEs). Ove...

Advances in gastrointestinal endoscopy: A comprehensive review of innovations in cancer diagnosis and management.

The field of gastroenterology has experienced revolutionary advances over the past years, as flexible endoscopes have become widely accessible. In add...

Deep learning progressive distill for predicting clinical response to conversion therapy from preoperative CT images of advanced gastric cancer patients.

Identifying patients suitable for conversion therapy through early non-invasive screening is crucial for tailoring treatment in advanced gastric cance...

Artificial intelligence generated 3D body composition predicts dose modifications in patients undergoing neoadjuvant chemotherapy for rectal cancer.

PURPOSE: Chemotherapy administration is a balancing act between giving enough to achieve the desired tumour response while limiting adverse effects. C...

Multi-contrast laser endoscopy for in vivo gastrointestinal imaging

White light endoscopy is the clinical gold standard for detecting diseases in the gastrointestinal tract. Most applications involve identifying visu...

HWA-UNETR: Hierarchical Window Aggregate UNETR for 3D Multimodal Gastric Lesion Segmentation

Multimodal medical image segmentation faces significant challenges in the context of gastric cancer lesion analysis. This clinical context is define...

Clinical Implications of The Cancer Genome Atlas Molecular Classification System in Esophagogastric Cancer.

PURPOSE: The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (M...

Fine-Tuning an Existing Large Language Model with Knowledge from the Medical Expert System Hepaxpert.

The analysis and individual interpretation of hepatitis serology test results is a complex task in laboratory medicine, requiring either experienced p...

Sociodemographic Profile of People with Diagnosed Pancreatic Cancer in the UK: Retrospective Sentinel Network Cohort Study.

Pancreatic cancer is a devasting disease which is an increasing cause of cancer mortality. The aim of this study was to characterise, using descriptiv...

Machine Learning-Based Multimodal Radiomics and Transcriptomics Models for Predicting Radiotherapy Sensitivity and Prognosis in Esophageal Cancer.

Radiotherapy plays a critical role in treating esophageal cancer, but individual responses vary significantly, impacting patient outcomes. This study ...

A Multitask Network for the Diagnosis of Autoimmune Gastritis.

Autoimmune gastritis (AIG) has a strong correlation with gastric neuroendocrine tumors (NETs) and gastric cancer, making its timely and accurate diagn...

Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation.

Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imaging by enabling precise liver segmentation, a cruc...

Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma.

The transcription factor SOX9 plays a critical role in various diseases, including hepatocellular carcinoma (HCC), and has been implicated in resista...

Artificial intelligence as a predictive tool for gastric cancer: Bridging innovation, clinical translation, and ethical considerations.

With gastric cancer ranking among the most prevalent and deadly malignancies worldwide, early detection and individualized prognosis remain essential ...

Integrating ultrasound and serum indicators for evaluating outcomes of targeted immunotherapy in advanced liver cancer.

BACKGROUND: Hepatocellular carcinoma (HCC) is a major global contributor to cancer-related mortality, with advanced stages presenting substantial ther...

Development and validation of machine learning nomograms for predicting survival in stage IV pancreatic cancer: A retrospective study.

BACKGROUND: Stage IV pancreatic cancer (PC) has a poor prognosis and lacks individualized prognostic tools. Current survival prediction models are lim...