Radiology

Machine learning-based radiomics analysis of PET imaging for early prediction of concurrent chemoradiotherapy response in stage II-III cervical squamous cell carcinoma.

BACKGROUND: Cervical squamous cell carcinoma is a major global health burden, with many patients presenting with locally advanced disease requiring concurrent chemoradiotherapy (CCRT). Early assessment of treatment response (TR) is critical for optimizing therapeutic strategies and improving clinical outcomes; however, conventional imaging parameters provide limited predictive value. This study ai...

PET-CT radiomics and artificial intelligence for predicting lymph node metastasis in cervical cancer: a systematic review and meta-analysis.

PURPOSE: Preoperative identification of lymph node metastasis (LNM) in cervical cancer is crucial for guiding therapeutic strategies but remains clinically challenging. This study represents the first systematic meta-analysis to quantitatively evaluate the diagnostic efficacy of PET/CT-based radiomics and artificial intelligence (AI) models in predicting LNM, providing a robust synthesis of curren...

Performance of Deep Learning Reconstruction for Detection of Early Ischemic Changes in NCCT: Comparison with ASIR-V in Acute Stroke.

PURPOSE: Evaluation of the impact of Deep Learning Image Reconstruction (DLIR) compared to Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) ...

Classification of functional brain patterns elicited by deep brain stimulation of the subthalamic nucleus in Parkinson's disease.

Despite the remarkable success of deep brain stimulation (DBS) in alleviating Parkinson's disease (PD) symptoms, complexities arising from inherent in...

S3F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network.

Convolutional Neural Networks (CNNs) have become a cornerstone of medical image analysis due to their proficiency in learning hierarchical spatial fea...

Medical Spine Sagittal MRI Dataset for Segmentation and Foraminal Stenosis detection.

Lumbar spine disorders represent one of the most prevalent musculoskeletal conditions worldwide, particularly among the elderly population. Magnetic R...

Non-invasive endometriosis staging prediction using integrated radiomics and spatiotemporal transformer model based on dynamic contrast-enhanced MRI.

Precise staging of endometriosis remains a clinical challenge, as current diagnosis depends almost entirely on laparoscopic visualization-an invasive ...

Machine-enhanced reconstruction of functional connectomes unravels discriminative brain sub-systems in health and disease.

Reconstructing functional brain networks from functional Magnetic Resonance Imaging (fMRI) data typically relies on statistical pruning, where pairwis...

Federated CT foundation models for multi-center detection of lymph node metastasis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with prognosis strongly influenced by the presence of lymph node ...

Preoperative MRI-Derived Navigation Reduces Radiation Exposure in Pediatric Spinal Fusion.

BACKGROUND: Minimizing radiation exposure during pediatric spinal deformity correction is critical due to the cumulative lifetime effects of ionizing ...

Early Prediction of Heart Failure From Routine Cardiac CT Using Radiomic Phenotyping of Epicardial Fat.

BACKGROUND: Epicardial adipose tissue (EAT) is a metabolically active visceral fat depot that is both a sensor and a modulator of myocardial biology a...

Integration of advanced ultrasound techniques, radiomics, and artificial intelligence for improved diagnosis of cystic breast lesions.

Cystic breast lesions are commonly encountered on breast ultrasound, encompassing a spectrum from simple benign cysts to complex mixed solid and cysti...

Fine-grained and multi-pattern anti-nuclear antibody recognition: A new dataset and framework.

Indirect immunofluorescence (IIF) staining imaging on human epithelial cell (HEp-2) is a standard technique for anti-nuclear antibodies (ANA) screenin...

Artificial intelligence-enhanced three-dimensional radiological analysis for predicting spread through air spaces in early-stage lung adenocarcinoma.

OBJECTIVES: Spread through air space (STAS) is a pathological feature that correlates with poor prognosis, especially in patients with lung adenocarci...

Opportunistic Cardiovascular Risk Assessment Using Routine Head CT in the Emergency Department.

BACKGROUND: Routine noncardiac computed tomography (CT) imaging may contain information about cardiovascular risk. Head computed tomography (CTH) is a...

Digital Enabled Diagnosis System for Mammogram Images Classification Using Machine Learning.

Breast tumor is the most commonly detected tumors and remain one of the leading causes of cancer-related mortality among women worldwide. Although mam...

Computerized diagnosis of brain tumor using graph based CNN classification.

Magnetic resonance imaging (MRI) is essential in the accurate diagnosis of brain tumors so that sound treatment planning can be done, however, clinica...

Multiparametric MRI-Based Integrated Analysis of Clinical, Radiomics, Deep Learning, and Machine Learning for Predicting Tumor Proliferation and Prognosis in Locally Advanced Rectal Cancer.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a predictive model integrating clinical, radiomics, deep learning (DL), and machine...

Opportunistic Screening on Chest CT, From the AJR Special Series on Screening.

The increase in chest CT volumes affords radiologists the opportunity to systematically assess imaging biomarkers, including coronary and thoracic art...

Association Between Breast Arterial Calcifications and Increased Cardiovascular Risk: A Canadian Perspective on Clinical Implications and the Potential Role in Preventive Care.

Breast arterial calcifications (BAC) are associated with increased cardiovascular risk and have been correlated with other methods of cardiovascular r...