Radiology

Machine Learning to Predict Extranodal Extension in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the clinical utility of machine learning algorithms (MLAs) in diagnosing extra-nodal extension (ENE) using CT imaging in HNSCC. DATA SOURCES: A comprehensive literature search was conducted on MEDLINE (Ovid), EMBASE, Cochrane, Scopus, and Web of Science, from January 1, 2000, to February 12, 2025. REVIEW METHODS: Two independent reviewers selected studies reporting the diagn...

Real-Time Artificial Intelligence-Assisted Middle Meningeal Artery Embolization Using Liquid Embolic Agents for Chronic Subdural Hematoma: A Preliminary Experience.

BACKGROUND AND OBJECTIVES: Middle meningeal artery (MMA) embolization is an emerging treatment option for chronic subdural hematoma. Surgeons must pay close attention to multiple vessels when using liquid embolic agents to avoid complications. Unintended embolization through dangerous anastomotic connections can result in serious complications, such as visual loss or cranial nerve dysfunction. In ...

Japanese Radiology 2025 Updates.

This review provides a comprehensive overview of recent transformative advancements in diagnostic imaging that position Japan at the forefront of radi...

VIBESegmentator: full body MRI segmentation for the NAKO and UK Biobank.

OBJECTIVES: To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including de...

Effectiveness of automated segmentation of maxillofacial structures in cone-beam computed tomography images using artificial intelligence: A systematic review.

BACKGROUND: The automated segmentation of maxillary and mandibular bones in cone-beam computed tomography (CBCT) using artificial intelligence (AI) is...

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 ...

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 gen...

Connectome-based markers predict the sub-types of frontotemporal dementia.

Frontotemporal dementia (FTD) presents a complex spectrum of neurodegenerative disorders, encompassing distinct subtypes with varied clinical manifest...

Artificial Intelligence-Based Algorithms Improve Care of Patients with AAA.

BACKGROUND: Timely detection and monitoring of abdominal aortic aneurysms (AAAs) are necessary to prevent ruptures and decrease mortality. Artificial ...

The current research status of non-destructive testing technologies for egg quality: internal freshness - a review.

1. Traditional methods of assessing egg freshness, such as sensory evaluation and specific gravity testing, are labour-intensive and destructive. Howe...

Radiation Risk in 2D Mammography Screening: A Scoping Review of Modelling Strategies and Emerging AI Applications.

Breast cancer is the most commonly diagnosed cancer among women worldwide, and concerns regarding radiation exposure from mammography screening remain...

Deep neural network-based detection of lead contamination via Förster resonance energy transfer in live cells.

Lead (Pb), a heavy metal with extensive industrial applications, poses significant risks to human health and the environment. These detrimental effect...

Brain metabolic imaging with 18 F-PET-CT and machine-learning clustering analysis reveal divergent metabolic phenotypes in patients with amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by significant clinicopathologic heterogeneity. Th...

A Datasheet for Age-Related Eye Disease Study 2 on the Database of Genotypes and Phenotypes.

OBJECTIVE: To provide a comprehensive summary of the controlled-access Age-Related Eye Disease Study 2 (AREDS2) data elements, encompassing phenotypic...

Dual-feature cross-fusion network for precise brain tumor classification: a neurocomputational approach.

Brain tumors represent a significant neurological challenge, affecting individuals across all age groups. Accurate and timely diagnosis of tumor types...

Deep learning with multimodal Raman spectral fusion framework: An analytical approach for microalgal lipid quantification.

As a key feedstock for sustainable bioenergy, microalgae require precise regulation of lipid synthesis and accurate detection methods. To efficiently ...

Validation of novel low-dose CT methods for quantifying bone marrow in the appendicular skeleton of patients with multiple myeloma: initial results from the [18F]FDG PET/CT sub-study of the Phase 3 GMMG-HD7 Trial.

PURPOSE: The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multi...

Deep Learning-Based CAD System for Enhanced Breast Lesion Classification and Grading Using RFTSDP Approach.

OBJECTIVES: Accurate detection of breast lesion type is crucial for optimizing treatment; however, due to the limited precision of current diagnostic ...

Deep Learning-Based Cardiac CT Coronary Motion Correction Method with Temporal Weight Adjustment: Clinical Data Evaluation.

Cardiac motion artifacts frequently degrade the quality and interpretability of coronary computed tomography angiography (CCTA) images, making it diff...

3D Convolutional Neural Network for Predicting Clinical Outcome from Coronary Computed Tomography Angiography in Patients with Suspected Coronary Artery Disease.

This study aims to develop and assess an optimized three-dimensional convolutional neural network model (3D CNN) for predicting major cardiac events f...