Gastroenterology

From diagnosis and treatment to prognosis: Clinical prospects of artificial intelligence in multimodal research of hepatocellular carcinoma.

PURPOSE: This review aims to critically evaluate the evolving role and clinical readiness of multimodal Artificial Intelligence (AI) in Hepatocellular Carcinoma (HCC), addressing the fundamental limitations of traditional single-modality approaches in characterizing tumor heterogeneity. PRINCIPAL RESULTS: Integrative analysis of heterogeneous data sources-specifically radiomics, pathomics, genomic...

LI-RADS-aligned artificial intelligence for liver cancer diagnosis: methods, evidence, and clinical readiness.

Liver tumor diagnosis relies heavily on imaging, and the liver imaging reporting and data system (LI-RADS) provides a structured framework for evaluating hepatocellular carcinoma (HCC) and related entities in at-risk populations. Artificial intelligence (AI) has rapidly expanded across CT, MRI, and ultrasound/CEUS, yet its clinical credibility depends on adherence to modality-appropriate tasks, ro...

A bibliometric analysis of the 100 most cited radiology papers on pancreatic diseases (1990-Present).

PURPOSE: To map the intellectual evolution of pancreatic radiology through a comprehensive bibliometric analysis of the 100 most-cited articles, ident...

A nomogram model integrating ultrasound-based multimodal radiomics features and clinical indexes for diagnosing significant hepatic fibrosis in AILD patients.

OBJECTIVE: To develop a prediction model combining radiomics features from 2D ultrasound (2D-US) and shear wave elastography (SWE) with clinical indic...

Deep learning-based simulated contrast-enhanced MRI for rectal cancer evaluation: a multicenter study.

OBJECTIVES: To assess the feasibility and accuracy of using deep learning to generate simulated contrast-enhanced T1-weighted rectal MRI scans from pr...

Blood metabolic panels for identifying significant fibrosis and inflammation in patients with MASLD.

Metabolic dysfunction-associated steatotic liver disease (MASLD) remains a prevalent condition with limited diagnostic and therapeutic options. This s...

Defining weight trajectory after liver transplantation using generative artificial intelligence.

A better understanding of weight trajectories in liver transplant (LT) recipients is essential to improving clinical outcomes. We utilized Generative ...

Exploring Patients' Profiles Associated With the Resolution of Acute Calcium Pyrophosphate Arthritis Treated With Colchicine and Prednisone: Post Hoc Analysis of a Randomized Controlled Trial.

OBJECTIVE: The objective was to identify factors determining acute arthritis resolution and safety with colchicine and prednisone in acute calcium pyr...

Multi-modal sensing in colonoscopy: a data-driven approach.

Soft optical sensors hold potential for enhancing minimally invasive procedures like colonoscopy, yet their complex, multi-modal responses pose signif...

Bayesian selection of lipidome dynamics features for a K-nearest neighbors (KNN) classifier model of tumor response in patients with advanced gastrointestinal adenocarcinoma undergoing systemic treatment.

BACKGROUND: Dysregulated lipid metabolism is common in patients with gastrointestinal (GI) cancer. This study investigated the ability of plasma lipid...

TEWS: Transformer-empowered weakly supervised prediction of immune score and genetic mutations in liver cancer from whole slide image.

Whole Slide Imaging (WSI) plays a crucial role in predicting immune scores by providing detailed cellular and tissue-level insights, thereby enhancing...

The basis for future personalized therapy approaches - Machine learning-generated 1-year survival rate, metastatic status and therapy-dependent survival in pancreatic cancer patients.

BACKGROUND: Pancreatic adenocarcinoma (PDAC) remains one of the most lethal types of cancer, characterized by its unspecific symptoms, aggressive natu...

Revealing the anti-tumor mechanisms of aromatic oil from Amomum villosum through integrated network pharmacology, bioinformatics, machine learning, single-cell sequencing, and cell experiments.

The dry fruits of Amomum villosum (Av) are a traditional Chinese medicine used for gastrointestinal disease. Aromatic oil has been reported to have an...

Integrative analysis of transcriptomic data reveals a predictive gene signature for chemoradiotherapy response in rectal cancer.

Locally advanced rectal cancer (LARC) is treated with neoadjuvant chemoradiotherapy (nCRT), but only a minority of patients achieve a pathological com...

A filter-level explainability framework for CNNs in histopathology image analysis.

Convolutional neural networks (CNNs) have achieved remarkable accuracy in histopathology image classification, yet their decision logic remains largel...

AI-Based Exhaled VOC Panels as Gut Microbiota-Derived Biomarkers for IBD Screening.

Inflammatory bowel diseases (IBD) present significant diagnostic and therapeutic challenges due to their heterogeneity. Here, we analyze exhaled volat...

Artificial intelligence takes on the multidisciplinary committee: A single-center study for rectal cancer management.

BACKGROUND: As rectal cancer management evolves, the multidisciplinary committee becomes increasingly important in integrating expertise to optimize p...

Decipher the molecular network of PFOA in inflammatory bowel disease through integrating machine learning, molecular docking strategies, and validation in vitro.

OBJECTIVE: Perfluorooctanoic Acid (PFOA), widely recognized as an enduring environmental pollutant, is associated with immune system disruption and po...

Modulating tumor mechanics and vascular barriers to enhance chemotherapy efficacy in pancreatic tumors and melanoma.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense stroma and dysfunctional vasculature, limiting drug delivery and immune infiltrati...

Interpretable fusion deep learning on super-resolution MRI for perineural invasion prediction in pancreatic ductal adenocarcinoma: a multicenter study.

PURPOSE: To develop an interpretable fusion deep learning model based on super-resolution (SR) MRI for predicting preoperative perineural invasion (PN...