Radiology

Leveraging generative AI to transform statistical analysis plan authoring in clinical trials.

BackgroundStatistical analysis plans are critical regulatory documents that define the statistical methodology, objectives, and data-handling procedures for clinical trials. The traditional process of statistical analysis plan development is resource-intensive, typically spanning 4-6 weeks, and is increasingly complicated by evolving regulatory requirements and complex trial designs such as adapti...

Proteomic profile of CSF obtained at the time of diagnosis determines amyotrophic lateral sclerosis progression and survival: CXCL7 levels in disease prognosis and survival.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease primarily affecting motor neurons. Neurofilament light chain (NfL) is the most established prognostic biomarker; however, its diagnostic resolution is limited, particularly within intermediate concentration ranges, and it does not capture the molecular heterogeneity of ALS. This study aimed to identify complementary cerebrosp...

What's New: Sub-5-minute Knee Magnetic Resonance Imaging- Spectrum of Sports Injuries and Overuse Conditions.

Knee injuries are one of the most common complaints in sports medicine. Magnetic resonance imaging is an essential adjunct to clinical evaluation for ...

A scoping review of radiation dose and image quality in paediatric CT: Towards safe imaging protocols and diagnostic reference levels in Jordan.

BACKGROUND: Computed tomography (CT) is an essential diagnostic tool, but its associated radiation exposure raises significant concerns, especially fo...

Visual assessment of AI-reconstructed knee MRI: A pilot study.

INTRODUCTION: Artificial intelligence is increasingly influencing medical imaging workflows by enhancing image quality and reducing acquisition time. ...

Minimum Clinically Achievable Dose for Detecting Liver Lesions Using Deep Learning Image Reconstruction: A Phantom and Patient Study.

RATIONALE AND OBJECTIVES: To investigate the performance of deep learning image reconstruction (DLIR) at an ultra-low dose of approximately 4.5 mGy fo...

Tumor metrics imaging core labs: primer for radiologists.

Imaging biomarkers have emerged as increasingly important endpoints in cancer clinical trials. Incorporating tumor metric reads as part of routine cli...

Early recurrence prediction and risk stratification of hepatocellular carcinoma after transarterial chemoembolization achieving imaging complete response based on contrast-enhanced CT machine learning.

OBJECTIVES: To develop and validate machine learning (ML) models using clinical and contrast-enhanced CT (CECT) parameters to assess recurrence risk i...

Deep learning ensemble models for CT-based differentiation of malignant and benign sacral bone tumors: development and evaluation.

OBJECTIVE: Radiologists often face challenges in differentiating benign from malignant sacral bone lesions due to their similar imaging characteristic...

Current Trends in Ultrasound Wearables: Spotlight on System Architecture.

Wearable ultrasound sensing systems are rapidly emerging for precise, continuous, and intuitive biomedical monitoring and human-in-the-loop interactio...

AI-Assisted Lung Sliding Detection in Point-of-Care Ultrasound by Marine Corps Corpsmen: A Multi-Reader Study.

BACKGROUND: Artificial intelligence (AI) has the potential to address training limitations and inter-operator variability that constrain the use of lu...

A domain-adaptive deep contrastive network for magnetic resonance imaging-driven bladder cancer classification.

Bladder cancer is one of the most prevalent malignancies of the urinary system and is associated with high morbidity and mortality. With advances in m...

Exploring AI in radiology: a bibliometric study tailored to a research path.

Radiology has been profoundly transformed by artificial intelligence (AI) over the past decade, enabling automated detection, enhanced diagnostic accu...

Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI.

Non-contrast MRI, routinely used for the preoperative diagnosis of glioma tumors and establishing treatment strategies, provides the potential for ass...

18F-FDG PET/CT and receptor-positive circulating tumor cells-based machine learning model for predicting poorly differentiated lung adenocarcinoma.

INTRODUCTION: This study aimed to develop and validate a machine learning model that integrates radiomic features from 2-[18F]fluoro-2-deoxy-D-glucose...

Vessel-specific perivascular fat attenuation index derived from AI-CCTA and its association with impaired coronary flow reserve in patients with INOCA.

OBJECTIVES: To investigate the association between artificial intelligence (AI)-derived coronary computed tomography angiography (CCTA) features and i...

Advances and challenges of PET imaging in bladder cancer-An update and future trends.

Molecular imaging with positron emission tomography (PET) is a powerful tool in the clinical management of bladder cancer, providing functional inform...

Prediction of Neoadjuvant Chemotherapy Efficacy for Locally Advanced Nasopharyngeal Carcinoma Using MRI-Based Deep Learning Features Combined with Vision Transformer.

RATIONALE AND OBJECTIVES: Predicting neoadjuvant chemotherapy (NACT) efficacy is vital for advanced nasopharyngeal carcinoma (LA-NPC) management. Exis...

Nuclear Medicine AI in Action: The Bethesda Report (AI Summit 2024).

The second Society of Nuclear Medicine and Molecular Imaging (SNMMI) AI Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on F...

AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.

This article reports the results of the second iteration of the autoPET challenge on automated lesion segmentation in whole-body PET/CT, held in conju...