AIMC Topic: Tomography, X-Ray Computed
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Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.
BMC gastroenterology
Apr 29, 2025
PURPOSE: This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC)...
Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties.
Radiological physics and technology
Apr 28, 2025
Prediction of severe disease (SVD) in patients with coronavirus disease (COVID-19) pneumonia at an early stage could allow for more appropriate triage and improve patient prognosis. Moreover, the visualization of the topological properties of COVID-1...
Machine Learning-Based Radiomics in Malignancy Prediction of Pancreatic Cystic Lesions: Evidence from Cyst Fluid Multi-Omics.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Apr 28, 2025
The malignant potential of pancreatic cystic lesions (PCLs) varies dramatically, leading to difficulties when making clinical decisions. This study aimed to develop noninvasive clinical-radiomic models using preoperative CT images to predict the mali...
The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses.
Biomedical engineering online
Apr 27, 2025
BACKGROUND: Chronic rhinosinusitis (CRS) is diagnosed with symptoms and objective endoscopy or computed tomography (CT). The Lund-Mackay score (LMS) is often used to determine the radiologic severity of CRS and make clinical decisions. This proof-of-...
A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques.
Scientific reports
Apr 26, 2025
Neuroblastoma presents a wide variety of clinical phenotypes, demonstrating different levels of benignity and malignancy among its subtypes. Early diagnosis is essential for effective patient management. Computed tomography (CT) serves as a significa...
Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.
Scientific reports
Apr 25, 2025
The most common causes of spine fractures, or vertebral column fractures (VCF), are traumas like falls, injuries from sports, or accidents. CT scans are affordable and effective at detecting VCF types in an accurate manner. VCF type identification in...
Impact of CT reconstruction algorithms on pericoronary and epicardial adipose tissue attenuation.
European journal of radiology
Apr 23, 2025
OBJECTIVE: This study aims to investigate the impact of adaptive statistical iterative reconstruction-Veo (ASIR-V) and deep learning image reconstruction (DLIR) algorithms on the quantification of pericoronary adipose tissue (PCAT) and epicardial adi...
Unsupervised non-small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity-based discriminator.
Journal of applied clinical medical physics
Apr 23, 2025
BACKGROUND: Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing deep learning-based automatic segmentation methods rely on manually annotated data for network training.
Deep learning-based post hoc denoising for 3D volume-rendered cardiac CT in mitral valve prolapse.
The international journal of cardiovascular imaging
Apr 23, 2025
We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT for the 3D volume-rendered (VR) imaging of mitral valve (MV) prolapse. We aimed to evaluate the quality of denoised 3D VR images for visualizing MV pr...
SynMSE: A multimodal similarity evaluator for complex distribution discrepancy in unsupervised deformable multimodal medical image registration.
Medical image analysis
Apr 22, 2025
Unsupervised deformable multimodal medical image registration often confronts complex scenarios, which include intermodality domain gaps, multi-organ anatomical heterogeneity, and physiological motion variability. These factors introduce substantial ...
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