AIMC Topic: Contrast Media
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Contrast-Enhanced Computed Tomography-Based Machine Learning Radiomics Predicts IDH1 Expression and Clinical Prognosis in Head and Neck Squamous Cell Carcinoma.
Academic radiology
Sep 10, 2024
RATIONALE AND OBJECTIVES: Isocitrate dehydrogenase 1 (IDH1) is a potential therapeutic target across various tumor types. Here, we aimed to devise a radiomic model capable of predicting the IDH1 expression levels in patients with head and neck squamo...
Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning.
Circulation. Cardiovascular imaging
Sep 10, 2024
BACKGROUND: Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is a standard technique for diagnosing myocardial infarction (MI), which, however, poses risks due to gadolinium contrast usage. Techniques enabling MI assessment based on...
From Gadolinium to Generative AI: The Quest for Contrast-Free Cardiac Imaging.
Circulation. Cardiovascular imaging
Sep 10, 2024
Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.
Scientific reports
Sep 9, 2024
Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. Th...
Integrating artificial intelligence (S-Detect software) and contrast-enhanced ultrasound for enhanced diagnosis of thyroid nodules: A comprehensive evaluation study.
Journal of clinical ultrasound : JCU
Sep 5, 2024
PURPOSE: This study aims to assess the diagnostic efficacy of Korean Thyroid imaging reporting and data system (K-TIRADS), S-Detect software and contrast-enhanced ultrasound (CEUS) when employed individually, as well as their combined application, fo...
Machine learning analysis of contrast-enhanced ultrasound (CEUS) for the diagnosis of acute graft dysfunction in kidney transplant recipients.
Medical ultrasonography
Sep 4, 2024
AIM: The aim of the study was to develop machine learning algorithms (MLA) for diagnosing acute graft dysfunction (AGD) in kidney transplant recipients based on contrast-enhanced ultrasound (CEUS) analysis of the graft.Materials and methods: This pro...
Machine learning-assisted diagnosis of parotid tumor by using contrast-enhanced CT imaging features.
Journal of stomatology, oral and maxillofacial surgery
Sep 2, 2024
PURPOSE: This study aims to develop a machine learning diagnostic model for parotid gland tumors based on preoperative contrast-enhanced CT imaging features to assist in clinical decision-making.
Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.
Acta radiologica (Stockholm, Sweden : 1987)
Sep 2, 2024
BACKGROUND: Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, a...
Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.
European radiology
Aug 30, 2024
OBJECTIVES: This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions.
Renal tumor segmentation, visualization, and segmentation confidence using ensembles of neural networks in patients undergoing surgical resection.
European radiology
Aug 23, 2024
OBJECTIVES: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.
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