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PLoS One
January 2025
Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, United States of America.
Introduction: Measurement of repeatability and reproducibility (R&R) is necessary to realize the full potential of positron emission tomography (PET). Several studies have evaluated the reproducibility of PET using 18F-FDG, the most common PET tracer used in oncology, but similar studies using other PET tracers are scarce. Even fewer assess agreement and R&R with statistical methods designed explicitly for the task.
View Article and Find Full Text PDFRadiographics
February 2025
From the Washington University School of Medicine, Mallinckrodt Institute of Radiology, 510 S Kingshighway Blvd, St. Louis, MO 63110.
Annual review of false-negative (FN) mammograms is a mandatory and critical component of the Mammography Quality Standards Act (MQSA) annual mammography audit. FN review can help hone reading skills and improve the ability to detect cancers at mammography. Subtle architectural distortion, asymmetries (seen only on one view), small lesions, lesions with probably benign appearance (circumscribed regular borders), isolated microcalcifications, and skin thickening are the most common mammographic findings when the malignancy is visible at retrospective review of FN mammograms.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Sorbonne Université, Paris Brain Institute (ICM), INSERM, CNRS, UMR-1127, Mov'It, DreamTeam, Paris, France.
Background: Spectral power of slow rhythms in resting-state EEG increases along Alzheimer's disease (AD) continuum. Besides, recent studies have revealed 1) the importance of analyzing the aperiodic component of an EEG power spectrum and 2) the intrusions of sleep-like slow waves identifiable in wake EEG of animals and young adults. Importantly, the occurrence of these wake slow waves is known i) to increase after sleep deprivation, ii) to be associated with markers of sleepiness, and iii) to predict behavioral errors at different tasks.
View Article and Find Full Text PDFBackground: The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progression by integrating neuroimaging metrics with demographic data using a novel machine learning technique.
Method: We used supervised Variational Autoencoders (VAEs), a generative AI method, to analyze high-dimensional neuroimaging data for AD progression, incorporating age and gender as covariates.
Acta Orthop
January 2025
Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg; Department of Orthopaedics, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden.
Background And Purpose: Computed tomography radiostereometric analysis (CT-RSA) assesses implant micromovements using low-dose CT scans. We aimed to investigate whether CT-RSA is comparable to marker-based radiostereometric analysis (RSA) measuring early femoral head migration in cemented stems. We hypothesized that CT-RSA is comparable to marker-based RSA in evaluating femoral head subsidence.
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