Publications by authors named "Anna M Marcinkiewicz"
Article Synopsis
- - The study focuses on developing an automated system to quantify [18F]-fluorodeoxyglucose (FDG) PET activity in diagnosing cardiac sarcoidosis using deep learning for segmenting cardiac chambers from CT scans.
- - The analysis included 69 patients, revealing that the cardiometabolic activity (CMA) showed the best predictive accuracy for cardiac sarcoidosis, followed by volume of inflammation (VOI) and target to background ratio (TBR).
- - The findings indicate that this automated method provides rapid, objective measurements of cardiac inflammation, showing high sensitivity and specificity for diagnosing cardiac sarcoidosis.
Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites.
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