Radiology

Advances in CT and MRI for Yttrium-90 Radioembolization of Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: Transarterial radioembolization (TARE) is increasingly used for patients with hepatocellular carcinoma (HCC) across Barcelona Clinic Liver Cancer stages. It provides local disease control and downstaging or bridging to definitive therapy in selected cases. Imaging plays a central role in patient selection, dosimetry planning, and post-treatment assessment. This review aim...

A Pulmonary Ultrasound Dataset for Tumor Segmentation and Classification.

Pulmonary tumor diagnosis with ultrasound is clinically valuable yet technically challenging. Although deep learning has shown promise in supporting sonographers, progress is hindered by the lack of publicly available, high-quality annotated datasets. Given the limited clinical adoption of pulmonary ultrasound for differentiating benign and malignant tumors, publicly available datasets for AI rese...

AI integration in pediatric radiology: perspectives from international academic leaders.

OBJECTIVE: Artificial intelligence (AI) is increasingly integrated into radiology, but pediatric imaging remains underrepresented in implementation st...

End-to-end 2.5D multisequence-multichannel fusion model for preoperative survival prediction in glioma: a retrospective study.

Gliomas are the most common primary malignant tumors of the central nervous system and show marked imaging heterogeneity, making accurate preoperative...

Deep learning-based super-resolution ultrashort echo time MRI for pulmonary nodule detection and longitudinal assessment in emphysema.

PURPOSE: This study aimed to evaluate the performance of deep learning-based super-resolution ultrashort echo time magnetic resonance imaging (SR-UTE ...

Preoperative MRI and clinical indicators for predicting meniscal repairability: a machine learning-based study.

OBJECTIVES: To develop and internally validate a radiology-centered machine-learning model using preoperative MRI and clinical characteristics to pred...

Deep Learning-Based Standard Section Recognition and Multi-Organ Segmentation in Upper Abdominal Ultrasound.

OBJECTIVE: To develop and evaluate deep learning models for upper abdominal ultrasound standard section recognition and downstream multi-organ segment...

A study to measure the utility of an AI-enhanced reporting tool in assisting busy CCTA readers with REPORT generation (SMART-REPORT).

PURPOSE: This study aimed to determine if an integrated structured reporting (SR) tool incorporating results from artificial intelligence (AI) enabled...

Evaluating and Mitigating Carbon Dioxide Equivalent Emissions in Stroke Management: A Modeling Study.

BACKGROUND: Artificial intelligence (AI) can improve stroke imaging workflows, but its computational carbon footprint remains poorly quantified. We es...

The application of artificial intelligence in adrenal imaging: current state of knowledge, challenges, and future directions.

Adrenal incidentalomas are frequently detected on abdominal imaging and require evaluation for malignancy and hormonal activity. Although most are ben...

Novel Imaging Approaches for Visualising Root-Mycorrhizal Fungal Interactions.

Mycorrhizal fungi form essential symbiotic relationships with plant roots, facilitating nutrient exchange and promoting plant health. Understanding th...

Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.

OBJECTIVE: To evaluate a machine learning (ML) model that integrates clinical data and 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG)-PET radiomic features...

Reconstruction of complete cerebral arterial anatomy from non-contrast CT using deep learning for pre-thrombectomy guidance.

BACKGROUND AND PURPOSE: Traditional imaging methods, like CT angiography (CTA), are unable to visualize the occluded vessels in acute ischemic stroke ...

ISSLS Prize in Bioengineering Science 2026: Hidden in Plain Sight: Machine Learning-Assisted MRI Reveals Novel Vertebral Body Biomarkers of Chronic Low Back Pain in Humans.

PURPOSE: To identify signal and spatial biomarkers in the lumbar vertebrae of individuals with and without non-specific chronic low back pain (NSCLBP)...

Carotid inflammation on FDG-PET is associated with lower cognitive performance in people with treated HIV infection.

OBJECTIVES: Carotid atherosclerosis is an established risk factor for cognitive impairment. FDG-PET detects subtle inflammatory changes in the arteria...

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

Photoacoustic computed tomography (PACT) with linear transducer array is a widely adopted imaging modality due to its cost-effectiveness and portabili...

A multimodal deepsurv approach: integrating radiomics and clinical factors for brain metastasis survival prediction.

PURPOSE: Predicting survival outcomes for brain metastasis (BM) patients is crucial for tailoring treatment strategies and improving patient managemen...

Artificial Intelligence in Echocardiography for Valvular Heart Disease.

The global burden of valvular heart disease (VHD) is increasingly burdensome, and precise early diagnosis combined with accurate risk stratification c...

Homogeneity of Liver Fat Distribution Serves as a Diagnostic Marker for Metabolic Dysfunction-Associated Steatohepatitis.

RATIONALE AND OBJECTIVES: To characterize liver fat distribution in metabolic dysfunction-associated steatohepatitis (MASH) and propose a magnetic res...

Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys.

Efficient algorithms are needed to segment vasculature in new 3D medical imaging datasets at scale for research and clinical applications. Manual segm...