Radiology

Improving the Identification of Muscular Dystrophies From Their Mimickers Based on MRI Muscle Texture Features Combined With Clinical Biomarkers.

MRI can detect the most significant pathological changes of muscle-fat replacement and muscle edema in muscular dystrophies. MRI-derived texture analysis is superior to conventional MRI for identifying pathological changes in muscular dystrophies. Furthermore, the combination of selected radiomics features and clinical biomarkers can enhance the diagnostic accuracy for muscular dystrophies. Muscul...

From Theory to Practice: Which Biomarkers Are Ready for Predicting Response in Advanced HCC?

Systemic therapies for advanced hepatocellular carcinoma (HCC) have expanded considerably with the advent of tyrosine kinase inhibitors, immune checkpoint inhibitors and immunotherapy-anti-angiogenic combinations. However, despite this therapeutic diversification, first-line treatment selection remains largely empirical, as few biomarkers are available at diagnosis to inform therapeutic choice. Th...

Literature Commentary June, 2026.

In this issue, the following 4 articles are reviewed by Drs. Mark L. Moster and Deborah I. Friedman, most of which include authors who are members of ...

Enhancing urothelial carcinoma diagnosis with artificial intelligence-integrated urine cytology: Biopsy-validated accuracy and efficiency gain.

BACKGROUND: Urine cytology is a noninvasive tool for detecting urothelial carcinoma, yet its performance depends heavily on expert cytologists and tim...

From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis.

Evaluation of body composition (BC) is a set of biomarkers, including fat, muscle and bone, that allows the quantification of an individual's composit...

A Z-Score Template Method for Person-Specific Augmentation of Clinical Brain MRI: An Investigation in Multiple Sclerosis.

Clinical MRI is essential for managing neurological disorders such as multiple sclerosis (MS) but often inconsistent, limiting secondary analyses for ...

Artificial Intelligence in Medicine: Basic Knowledge, Applications, Ethics and Perspectives.

BACKGROUND: Artificial intelligence (AI) is rapidly transforming medicine through advanced diagnostic, prognostic, and decision-support systems. OBJEC...

Mechanistic Insights into PET-MPs-Aggravated Ischemic Stroke via Integrated Computational-Experimental Approaches.

Microplastics, especially the environmentally pervasive polyethylene terephthalate microplastics (PET-MPs), are important environmental pollutants, an...

MedNet-FS: a few-shot learning framework for 3D MRI-based knee injury classification.

The development of deep learning models for 3D knee MRI analysis is critically constrained by the scarcity of large, annotated datasets. Few-shot lear...

Deep learning algorithm for automatic detection of acute ischemic stroke on noncontrast brain CT.

This study aimed to evaluate the diagnostic performance of a deep learning-based algorithm for detecting acute ischemic stroke (AIS), including small ...

An AI-Driven Pipeline for Localization, Segmentation, and Classification of Carpal Tunnel Syndrome Using Ultrasound Images of the Median Nerve.

BACKGROUND: Carpal tunnel syndrome (CTS), the most common peripheral neuropathy, is currently diagnosed by clinical suspicion supported by tools such ...

Frequency-Specific Alterations of Spontaneous Brain Activity in Stroke Patients with Upper Limb Motor Dysfunction: A Multi-Metric rs-fMRI and Machine Learning Study.

BACKGROUND: Post-stroke upper limb motor dysfunction is associated with complex alterations in brain function, but the frequency-specific characterist...

Quantitative MRI of pediatric chronic kidney disease and renal fibrosis: potential non-invasive clinical biomarkers.

Chronic kidney disease (CKD) in children is a rare but serious condition characterized by progressive renal dysfunction and a range of systemic compli...

Predicting De Novo Stress Urinary Incontinence After Colpocleisis Using Clinical and Pelvic Floor Ultrasound Parameters: Development and Internal Validation of a Multimodal Model.

INTRODUCTION AND HYPOTHESIS: De novo stress urinary incontinence (SUI) remains a clinically relevant complication after colpocleisis for advanced pelv...

Machine learning-based MRI radiomics identifies patients with degenerative cervical myelopathy and predicts baseline function.

PURPOSE: The diagnosis of Degenerative cervical myelopathy (DCM) relies on clinical evaluation and conventional MRI, yet early symptoms are subtle and...

Technical challenges in autonomous robotic ultrasound examinations: perception, planning, and control.

Ultrasound is one of the most widely used medical imaging modalities in clinical practice. Robot-assisted ultrasound systems (RAUS) offer significant ...

AI-driven text mining of the female reproductive system: enabling multiscale biomedical modeling and personalized medicine.

The female reproductive system, including the endometrium, placenta, ovary, cervix, and fallopian tube, plays a critical role in conception, implantat...

Beyond Broca and Wernicke: Epilepsy surgery in the language areas.

Epilepsy surgery in language areas is challenged by the intricacies of presurgical workup and surgical planning. In recent decades, the view of langua...

Imaging Primary Chronic Pain Syndromes: Current Evidence, Clinical Relevance, and Advances in MRI and PET/CT.

Primary chronic pain syndromes are increasingly understood through advanced neuroimaging techniques, which reveal consistent structural, functional, a...

Assessing Image Quality and Diagnostic Performance of Quadruple-Low Coronary CT Angiography with Deep Learning in High-BMI Patients.

RATIONALE AND OBJECTIVES: To examine the feasibility of a quadruple-low protocol in coronary computed tomography angiography (CCTA) assisted by the de...