Radiology

Negative correlation between habenular volume and duration of gambling disorder: Modulation by symptom severity and personality traits.

BACKGROUND AND AIMS: Gambling disorder (GD) patients continue to gamble despite negative consequences, and this behavior can be partly attributed to their insensitivity to failures and losses. GD may worsen over time and may stem from dysfunctions in the reward system and habenula, which encodes negative reward prediction errors. We aimed to elucidate habenular volume alterations that could intens...

Economic evaluations of AI applications in radiology: a systematic review.

OBJECTIVES: Artificial intelligence (AI) applications in radiology may improve clinical outcomes, but adoption is hindered by limited health economic evidence. This review synthesises economic evaluations of radiology AI, mapping methods, outcomes and metrics to identify trends, support future research, and inform policy. MATERIALS AND METHODS: A systematic search of MEDLINE and Cochrane Central (...

Distinction Between Benign and Borderline/Malignant Phyllodes Tumor in Breast Mammography and Ultrasound Based on Radiomics Methods.

RATIONALE AND OBJECTIVES: This study aims to evaluate whether radiomics methods used on breast mammography (MG) and ultrasound (US) could distinguish ...

Multiparametric MRI-Based Deep Learning and Radiomics for Predicting Progression-Free Survival Benefit in Patients with Hepatocellular Carcinoma Treated with Immunotherapy and Targeted Therapy Plus Transarterial Chemoembolization: A Bicentric Study.

RATIONALE AND OBJECTIVES: The non-invasive biomarkers for predicting progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC) t...

Pericoronary Adipose Tissue Imaging: Quantitative Assessment, Artificial Intelligence Integration, and Therapeutic Modulation.

Pericoronary adipose tissue (PCAT) is increasingly recognised as a biosensor of vascular inflammation. The guideline-driven widespread adoption of cor...

A deep learning-based computer-aided determination method for dominant follicle identification and antral follicle count in pelvic MRI images.

OBJECT: To develop and evaluate a multi-stage computer-aided determination (CAD) method for automated antral follicle count (AFC) and dominant follicl...

Expanding access to cerebrovascular imaging with routine MRI for pre-surgical assessment of intracranial tumor patients: an AI model development and multi-center validation study.

BACKGROUND: Angiography is the gold standard for assessing the relationship between cerebral arteries and intracranial tumors, but its use is limited ...

Multimodal ultrasound and artificial intelligence for characterization of thyroid nodules in Hashimoto's thyroiditis: current challenges and future perspectives.

Patients with Hashimoto's thyroiditis (HT) frequently present with concurrent nodular lesions such as nodular goiter and thyroid cancer (especially pa...

Identifying a better-prognosis pancreatic cancer from its benign inflammatory mimic: a machine learning approach with contrast-enhanced ultrasound for early intervention.

PURPOSE: Hypervascular pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) represent a classic diagnostic mimicry on contrast-...

Fat-containing soft-tissue tumors: Imaging findings and pathologic correlation.

Fat-containing soft-tissue tumors encompass a broad spectrum of entities, ranging from indolent lipomas to aggressive liposarcomas, many of which shar...

Performance Analysis of Deep Learning Models for Segmentation of Carotid Artery Vessel Wall in 3D-MERGE Images.

BACKGROUND: Carotid vessel wall segmentation and determination of the lumen area are crucial for the diagnosis of atherosclerosis. U-Net-based deep le...

Predicting treatment response to neoadjuvant chemotherapy in locally advanced rectal cancer: A combined deep learning and machine learning approach utilizing longitudinal multi-sequence MRI.

PURPOSE: To develop and validate deep leaning-based machine learning models using longitudinal multi-sequence MRI for predicting treatment response of...

Subregional limbic radiomics on FDG-PET provides accurate early detection of Alzheimer's disease.

BACKGROUND: To investigate the radiomics features of the hippocampus and the amygdala subregions in FDG-PET images that can best differentiate Mild Co...

Privacy-preserving multimodal fusion for Alzheimer's staging: A federated vision transformer framework with explainable AI.

Accurate, early-stage staging of Alzheimer's disease (AD) is critical for therapeutic intervention but is hampered by data privacy regulations, multim...

An Automated Hybrid Deep Learning-based Model for Breast Cancer Detection using Mammographic Images.

INTRODUCTION: Breast cancer is a disease in which abnormal breast cells grow uncontrollably and develop into a tumor. It is one of the most common can...

Performance of Radiologist in Interpretation of Non-mass Lesions Detected by Automated Breast Ultrasound: With and Without Commercially Available AI.

OBJECTIVES: To evaluate the performance of radiologists with artificial intelligence (AI)-based computer-aided detection (CAD) systems on automated br...

Two-Step Semiautomated Classification of Choroidal Metastases on MRI: Orbit Localization via Bounding Boxes followed by Binary Classification via Evolutionary Strategies.

BACKGROUND AND PURPOSE: The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies,...

Deep Learning Integration of Endoscopic Ultrasound Features and Serum Data Reveals LTB4 as a Diagnostic and Therapeutic Target in ESCC.

BACKGROUND: Early diagnosis and accurate prediction of treatment response in esophageal squamous cell carcinoma (ESCC) remain major clinical challenge...

Dual-Parallel Artificial Intelligence Framework for Breast Cancer Grading via High-Intensity Ultrasound and Biomarkers.

BACKGROUND: Accurate and noninvasive breast cancer grading and therapy monitoring remain critical challenges in oncology. Traditional methods often re...

Automated Non-Invasive Burn Diagnostic System for Healthcare using Artificial Intelligence: AMBUSH-AI.

OBJECTIVE: Develop technology to predict burn wound depth using a combination of FDA approved ultrasound modalities and interpretation of these images...