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The PROPHECI trial: a phase II, double-blind, placebo-controlled, randomized clinical trial for the treatment of pseudoxanthoma elasticum with oral pyrophosphate.
Trials
January 2025
Université Côte d'Azur, CNRS, LP2M, Nice, France.
Background: /aims. Pseudoxanthoma Elasticum (PXE, OMIM 264800) is an autosomal, recessive, metabolic disorder characterized by progressive ectopic calcification in the skin, the vasculature and Bruch's membrane. Variants in the ABCC6 gene are associated with low plasma pyrophosphate (PPi) concentration.
View Article and Find Full Text PDFIntegration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach.
Magn Reson Imaging
January 2025
GE Healthcare, Guangzhou 510623, China.
Background: Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectiveness of integrating MRI radiomic features with clinical data using a deep learning approach to predict vascular invasion in breast cancer patients.
View Article and Find Full Text PDFApplication value of different imaging methods in the early diagnosis of small hepatocellular carcinoma: a network meta-analysis.
Front Oncol
January 2025
Department of Ultrasound Medicine, the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.
Objective: To determine the diagnostic value of ultrasound, multi-phase enhanced computed tomography, and magnetic resonance imaging of small hepatocellular carcinoma.
Methods: Experimental studies on diagnosing small hepatocellular carcinoma in four databases: PubMed, Cochrane Library, Web of Science, and Embase, were comprehensively searched from October 2007 to October 2024. Relevant diagnostic accuracy data were extracted and a Bayesian model that combined direct and indirect evidence was used for analysis.
Combining Low-energy Images in Dual-energy Spectral CT With Deep Learning Image Reconstruction Algorithm to Improve Inferior Vena Cava Image Quality.
J Comput Assist Tomogr
January 2025
Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine.
Objective: To explore the application of low-energy image in dual-energy spectral CT (DEsCT) combined with deep learning image reconstruction (DLIR) to improve inferior vena cava imaging.
Materials And Methods: Thirty patients with inferior vena cava syndrome underwent contrast-enhanced upper abdominal CT with routine dose, and the 40, 50, 60, 70, and 80 keV images in the delayed phase were first reconstructed with the ASiR-V40% algorithm. Image quality was evaluated both quantitatively [CT value, SD, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for inferior vena cava] and qualitatively to select an optimal energy level with the best image quality.
A key contribution to X-ray dark-field (XDF) contrast is the diffusion of X-rays by sample structures smaller than the imaging system's spatial resolution; this is related to position-dependent small-angle X-ray scattering. However, some experimental XDF techniques have reported that XDF contrast is also generated by resolvable sample edges. Speckle-based X-ray imaging (SBXI) extracts the XDF by analyzing sample-imposed changes to a reference speckle pattern's visibility.
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