Radiology

MRI radiomics combined with clinical features for predicting initial dose in focused ultrasound ablation surgery for uterine fibroids.

OBJECTIVE: To develop and validate a radiomics-clinical model for individualized prediction of the initial treatment dose in focused ultrasound ablation surgery (FUAS) for uterine fibroids. METHODS: This retrospective study included 210 patients with solitary uterine fibroids who underwent FUAS and were randomly divided into training and testing cohorts (7:3). The outcome variable was defined as t...

Detection of Pancreatic Cancer via Specific Metabolite Markers: A Metabolomics Approach.

BACKGROUND AND AIMS: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies worldwide because most patients are diagnosed at advanced stages when curative treatment is no longer feasible. Metabolomics has emerged as a promising strategy for identifying biochemical alterations associated with early tumor development and may improve the early detection of PDAC. This revi...

Accurate differentiation of metastatic and ultrasound-atypical reactive hyperplastic lymph nodes using a fusion model integrating ultrasound radiomics and habitatomics: A multicenter study.

BACKGROUND: The significant sonographic overlap between metastatic and ultrasound-atypical reactive hyperplastic lymph nodes remains a challenge in su...

Hierarchical two-stage ensemble of dilated U-net models for brain tumor segmentation.

Ensemble methods for image segmentation improve performance by combining predictions from multiple models, yielding more accurate and reliable results...

AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective study.

OBJECTIVES: To evaluate an artificial intelligence (AI) algorithm on three different mammography devices and assess screening performance using genera...

Quality and reliability of YouTube videos on PET/CT radiation safety: a comparative analysis between human experts and large language models.

PURPOSE: To evaluate the quality and accuracy of YouTube videos regarding PET/CT radiation safety and to assess the feasibility of using a Large Langu...

Recent advances in MR neuroimaging: toward quantitative and AI-driven brain and spinal cord imaging.

Magnetic resonance neuroimaging is undergoing a major paradigm shift from traditional qualitative anatomical mapping toward integrated, quantitative m...

Incremental Diagnostic Value of Clinical Information for Large Language Models Across Multiple Organs: Retrospective Study.

BACKGROUND: Although large language models (LLMs) have demonstrated the ability to generate the impression section from radiology findings automatical...

Large Language Models for Patient Education in Cardiovascular Imaging: Prospective Observational Comparative Study.

BACKGROUND: Large language models (LLMs) are increasingly used to support digital health communication, yet their reliability in patient-facing cardio...

A novel approach for liver steatosis assessment in ultrasound images using an adapted Vision-Language Foundation Model.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is among the most prevalent chronic liver diseases worldwide. B-mode ultrasound remai...

Clinical phenotyping of bloodstream infections: a review of current evidence.

BACKGROUND: Bloodstream infections (BSIs) are a leading cause of morbidity and mortality, yet their clinical heterogeneity continues to challenge effe...

Deep learning-derived magnetic resonance imaging masseter muscle volume is associated with oral impairment and cognitive frailty in older Japanese adults.

BACKGROUND AND OBJECTIVES: The masseter muscle is associated with oral impairment and age-related functional decline, yet scalable and objective indic...

Improved Deep Learning Segmentation of Pediatric Diffuse Midline Gliomas After Treatment.

PURPOSE: To develop and validate a pediatric diffuse midline glioma (DMG) auto-segmentation tool optimized for longitudinal treatment response assessm...

Multiparametric MRI-Based Interpretable Machine Learning Radiomics Model for Predicting Neoadjuvant Chemotherapy Sensitivity and Recurrence-Free Survival in Breast Cancer.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate an interpretable model using pretreatment multiparametric magnetic resonance imagin...

Intelligent medical sensors for smart healthcare and precision medicine.

Conventional medical sensors primarily function as passive transducers and often struggle to maintain accuracy and stability under complex physiologic...

An Imaging Informatics Workflow for Angiography-Derived Coronary Physiology Profiling and Virtual PCI Simulation.

Coronary angiography is routinely acquired during invasive assessment of coronary artery disease, but its interpretation remains fragmented across vis...

Attention-Guided TransMorph for Real-Time Tumor Tracking in Cine-MRI.

Magnetic Resonance Imaging (MRI) is a key modality in cancer treatment, providing high soft tissue contrast for the visualization of tumors and intern...

Feasibility of AI-Based Wheelchair Classification for MRI Anteroom Safety Management: A Single-Centre Deep Learning Object-Detection Study.

In MRI examinations, MR-unsafe metallic wheelchairs inadvertently brought into the scanner room represent a serious safety hazard. This study was aime...

Enhancing Robustness of Deep Learning to Batch Effects from Multi-site Data for Segmentation of Clinically Significant Prostate Cancer on MRI.

Deep learning (DL) has shown promise in segmenting clinically significant prostate cancer (csPCa) on MRI. However, batch effects arising from multi-si...

Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images.

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulated follicular variant of papillary thyroi...