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Background: Radiomics provides quantitative features of pulmonary nodules (PNs) which could aid lung cancer diagnosis, but medical image acquisition variability is an obstacle to clinical application. Acquisition effects may differ between radiomic features from benign vs. malignant PNs.
View Article and Find Full Text PDFRegional brain iron correlates with transcriptional and cellular signatures in Alzheimer's disease.
Alzheimers Dement
January 2025
Department of Radiology, China-Japan Friendship Hospital, Beijing, China.
Introduction: The link between overload brain iron and transcriptional/cellular signatures in Alzheimer's disease (AD) remains inconclusive.
Methods: Iron deposition in 41 cortical and subcortical regions of 30 AD patients and 26 healthy controls (HCs) was measured using quantitative susceptibility mapping (QSM). The expression of 15,633 genes was estimated in the same regions using transcriptomic data from the Allen Human Brain Atlas (AHBA).
Estimation of end-of-outbreak probabilities in the presence of delayed and incomplete case reporting.
Proc Biol Sci
January 2025
Mathematical Institute, University of Oxford, Oxford, UK.
Towards the end of an infectious disease outbreak, when a period has elapsed without new case notifications, a key question for public health policymakers is whether the outbreak can be declared over. This requires the benefits of a declaration (e.g.
View Article and Find Full Text PDFAs a simple and widely used quantitative phase imaging (QPI) approach, the transport of intensity equation (TIE) is plagued by accuracy and applicability issues due to the limitations of conventional solution algorithms. To address these problems, we present an accurate fast QPI method using an accelerated iteration-based TIE solution. In this method, a gradient acceleration iterative solution is constructed by TIE itself.
View Article and Find Full Text PDFEnhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation.
Int J Cardiovasc Imaging
January 2025
Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address manual QCA's limitations in reproducibility and correction process. The present study aimed to assess the performance of an updated AI-QCA solution (MPXA-2000) in lesion detection and quantification using manual QCA as the reference standard, and to demonstrate its superiority over visual estimation. This multi-center retrospective study analyzed 1,076 coronary angiography images obtained from 420 patients, comparing AI-QCA and visual estimation against manual QCA as the reference standard.
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