Radiology

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 study aims to identify metabolic signatures of disease progression and develop non-invasive diagnostic models through three independent cohorts (including two cohorts confirmed by biopsy and one cohort confirmed by ultrasound) involving 293 participan...

Targeting High-Risk Populations for Abdominal Aortic Aneurysm Screening: A Sex-Stratified Analysis in an Ethnicity Diverse Cohort Incorporating Machine Learning Tools.

BACKGROUND: Abdominal aortic aneurysm (AAA) is usually asymptomatic, but rupture carries up to 90% mortality. Ultrasound screening reduces rupture-related mortality in ever-smoking men older than 65. Women have 4-fold lower prevalence but worse outcomes and were markedly underrepresented in major AAA trials. We analyzed a nationwide database to identify high-risk subgroups. METHODS: We retrospecti...

Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography.

PURPOSE: To conduct a comprehensive systematic evaluation of federated learning (FL) strategies for multi-disease retinal classification using OCT ang...

BRAIN-META: A reproducible CNN-vision transformer meta-ensemble pipeline for explainable brain tumor classification.

This study presents BRAIN-META, a reproducible deep learning methodology designed for multi-class brain tumor classification using structural MRI. The...

A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI.

OBJECTIVE: The Nottingham Histologic Grade (NHG) informs prognosis and treatment decisions in breast cancer, but NHG2 tumors are biologically heteroge...

Global trends and collaboration networks in radiology: A bibliometric analysis of the 500 most-cited articles in web of science.

OBJECTIVE: This study examined global research trends in Radiology, Nuclear Medicine, and Medical Imaging by analyzing the 500 most-cited articles in ...

Regional-aware and sequence-informed multi-decoder network for robust brain glioma segmentation in multi-parametric MRI.

Accurate segmentation of glioblastoma subregions from multi-parametric MRI is essential for diagnosis, surgical planning, and treatment monitoring in ...

Reduced left atrial strain from 4-dimensional computed tomography predicts atrial fibrillation recurrence after ablation.

BACKGROUND: Existing models that use clinical history and cardiac imaging data remain inadequate for accurate prediction of the success of catheter ab...

Hardware-based brain tumor classification using graph Laplacian spectral features.

Early and accurate identification of Brain Tumors (BT) is one of the most challenging problems due to the complex, non-Euclidean, and irregular charac...

Accuracy of fully automated iliac artery tortuosity measurements in patients with abdominal aortic aneurysm using deep learning.

OBJECTIVE: To externally validate the accuracy of deep learning-based iliac artery tortuosity assessment (PRAEVAorta 2, Bordeaux, France) in computed ...

Venture Capital Investments in Radiology From 2000 to 2023.

OBJECTIVE: Venture capital (VC) is playing a growing role in driving innovation in health care. Although previous studies have examined VC trends in v...

Biparametric MRI-Based Habitat Analysis Integrated With Deep Learning for Predicting Clinically Significant Prostate Cancer in PI-RADS Category 3 Lesions.

BACKGROUND: Detection of clinically significant prostate cancer (csPCa) within PI-RADS category 3 lesions remains a major diagnostic challenge. PURPOS...

Pericoronary adipose tissue attenuation on CCTA as a marker of cardiovascular risk: A systematic review and meta-analysis.

BACKGROUND: Pericoronary adipose tissue (PCAT) attenuation on coronary computed tomography angiography (CCTA) reflects local vascular inflammation, bu...

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...

Neuroanatomical-based machine learning prediction of Alzheimer's Disease across sex and age.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. In 2024 it affected approxima...

Diagnostic Performance of AI-Assisted Coronary CT Angiography: A Systematic Review and Meta-Analysis.

BACKGROUND: Coronary computed tomography angiography (CCTA) is vital for diagnosing ischemic heart disease, yet its accuracy is unreliable due to vary...

A review of deep learning techniques in Alzheimer's disease with emphasis on data tools and transfer learning.

Alzheimer's disease (AD) is a significant neurological condition that is marked by the gradual decline of memory and cognitive function, with a higher...

Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.

PURPOSE: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming because of the...

Use of gadolinium-based contrast agents in head and neck cancer diagnosis, staging, and monitoring: current applications and future perspectives.

Gadolinium-based contrast agents (GBCAs) have been fundamental to head and neck cancer (HNC) imaging, enabling effective detection, characterization, ...

Causality-driven dual-domain network enhanced by Gaussian splatting for magnetic resonance image reconstruction.

Magnetic resonance imaging (MRI) is a crucial tool in modern clinical diagnostics due to its non-invasive nature and high-resolution imaging capabilit...