Radiology

Comparative evaluation of conventional radiomics and VGG-SAM fusion strategies for MRI-based preoperative prediction of perineural invasion in cervical cancer.

OBJECTIVE: Perineural invasion (PNI) is an adverse feature in cervical cancer and may influence nerve-sparing surgery. We compared conventional radiomics and VGG-SAM deep-learning strategies for MRI-based preoperative prediction of PNI. METHODS: A retrospective cohort of 103 patients with cervical cancer, including 82 PNI-negative and 21 PNI-positive patients, was analyzed. PyRadiomics features we...

A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.

OBJECTIVES: Accurate MRI-based identification of extramural vascular invasion (EVI) and mesorectal fascia invasion (MFI) is crucial for risk-stratified rectal cancer treatment. However, subjective visual assessment and inter-institutional variability limit diagnostic consistency. This study developed and evaluated a multi-center, foundation model-driven framework that automatically classifies EVI ...

A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.

BACKGROUND: Pediatric lymphoma patients undergo multiple 18F-FDG PET/CT examinations for staging and response assessment, raising concerns about cumul...

Simultaneous partial volume correction and denoising of brain PET images, using transformers and transfer learning.

BACKGROUND: Positron emission tomography (PET) is a key tool for quantitative brain imaging, but its image quality and quantitative reliability are st...

A machine learning approach for early Parkinson's disease diagnosis based on brain texture features from T1-weighted imaging.

OBJECTIVE: To investigate the diagnostic value of subcortical texture features from T1-weighted MRI combined with machine learning for early Parkinson...

Non-invasive predictive model for incidental gallbladder carcinoma based on multimodal features: Integrating clinical data, MRI radiomics, and deep transfer learning features.

Gallbladder carcinoma, among the most prevalent malignancies of the biliary system, often presents with insidious early symptoms. Delayed diagnosis of...

Compression of 2D+t cine-MRI data and its impact on AI-based target tracking in MRIgRT.

Magnetic resonance imaging-guided radiotherapy (MRIgRT) uses 2D+t cine-MRI to track intra-fractional motion with high spatial and temporal resolution....

Predicting intracranial angioplasty failure using vessel wall MRI habitat radiomics and deep learning: a multicenter study.

BACKGROUND: Primary balloon angioplasty (PBA) is a promising strategy for symptomatic intracranial atherosclerotic stenosis but carries the risk of pr...

AI assisted detection of large vessel occlusion on non-contrast CT: multinational validation and reader study.

OBJECTIVE: Early detection of large vessel occlusion (LVO) on non-contrast CT (NCCT) could accelerate stroke triage, but NCCT based artificial intelli...

Preoperative Risk Assessment of Adrenal Metastases in a Multicenter Study: Development of a Robust Federated Learning Model.

RATIONALE AND OBJECTIVES: In clinical practice, the preoperative risk assessment of adrenal metastases versus benign adrenal lesions carries a substan...

Deep learning predicts stent implantation in borderline coronary lesions from angiography.

Accurate evaluation of coronary intermediate lesions (50-70% stenosis) is essential for stent decision-making, yet conventional angiography remains su...

An Automated Framework and Medical Intelligent System for MRI-Based Alzheimer's Diagnosis Classification.

In the era of artificial intelligence (AI), Alzheimer's disease (AD) can be diagnosed through magnetic resonance imaging (MRI) at accurate times and w...

Machine learning in automatic detection of chordoma signature, postoperative residuals, and prognosis of skull base chordomas.

Skull base chordomas are rare, locally invasive tumors that remain a diagnostic and therapeutic challenge. We developed a machine-learning (ML) radiom...

Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.

PURPOSE: magnetic resonance imaging (MRI)-based radiomics has emerged as a promising approach for non-invasive prediction of treatment response in rec...

Fully Automated Colon Delineation and Volume Estimation in T2-Weighted MRI with a 2D U-Net.

Artificial intelligence has the potential to provide an objective, accurate and fast evaluation of the colon for clinical assessment of gastrointestin...

Subgroup-Based Meta-Learning with Domain-Specific Self-Supervised Learning for Sarcopenia Detection from Musculoskeletal Ultrasound.

Sarcopenia is a progressive muscle disorder linked to aging, frailty, and increased healthcare burden. While ultrasound imaging offers a practical and...

Can LLMs Turn French PET/CT Narrative Reports into Structured Knowledge?

This study evaluates large language models (LLMs) for information extraction from French PET/CT reports related to cognitive impairment, focusing on d...

Development of a Hybrid Algorithm of Claims Data and EMRs with NLP for Lung Cancer Identification.

The use of real-world data, which encompassing administrative claims and electronic medical records, has gained significance in clinical research. Alt...

MAP-CARE: Enhancing Cross-Lingual Medical Intervention Terms.

Traditional keyword-based or single-language systems are not able to align data from surgical and interventional procedures, especially from non-Engli...

Empirical Comparison of Causal Machine Learning and Post-Hoc AI Interpretability Models for Risk Factor Analysis: An Application to Medical Specialty Choice.

How medical students choose specialties shapes access to care. Prior work mostly describes patterns; newer prediction tools can rank influential facto...