[This corrects the article DOI: 10.1002/ags3.12703.].
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| http://dx.doi.org/10.1002/ags3.12797 | DOI Listing |
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Contrast-enhanced US of High-Risk Indeterminate Focal Liver Observations Categorized as LR-4 or LR-M at CT/MRI.
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January 2025
From the Department of Radiology, Thomas Jefferson University Hospital, 132 S 10th St, 763G Main Bldg, Philadelphia, PA 19107 (A.L., C.K.Y.E., T.S.X., S.K.R., C.E.W., K.B., J.R.E., F.F.); Division of Internal Medicine, Hepatobiliary and Immunoallergic Diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy (F.P.); Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy (F.P.); University of California San Diego, San Diego, Calif (Y.K.); University of Calgary, Calgary, Canada (A.M.K., S.R.W.); Einstein Medical Center, Philadelphia, Pa (S.K.R.); Vanderbilt University, Nashville, Tenn (V.P.); Stanford University, Stanford, Calif (A.K.); UT Southwestern Medical Center, Dallas, Tex (D.T.F.); Department of Visceral Surgery and Medicine, Bern University Hospital, University of Bern, Bern, Switzerland (A.B., I.P.R.); Department of Imaging Sciences, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom (P.S.S.); and Department of Radiology, King's College Hospital, London, United Kingdom (P.S.S.).
Background Indeterminate focal liver observations in patients at risk for hepatocellular carcinoma (HCC) may require invasive biopsy or follow-up, which could lead to delays in definitive categorization and to postponement of treatment. Purpose To examine clinical effect of contrast-enhanced US (CEUS) in participants with high-risk indeterminate liver observations categorized as Liver Imaging Reporting and Data System (LI-RADS) category LR-4 (probably HCC) or LI-RADS category LR-M (probably or definitely malignant but not HCC specific) at CT or MRI. Materials and Methods This was a secondary analysis of a prospective international multicenter validation study for CEUS LI-RADS (January 2018 to August 2021).
View Article and Find Full Text PDFMulti-center brain age prediction via dual-modality fusion convolutional network.
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Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain age prediction models to multi-center datasets, particularly those with small-sample sizes, remains a significant challenge that is yet to be addressed. To this end, we propose a multi-center data correction method, which employs a domain adaptation correction strategy with Wasserstein distance of optimal transport, along with maximum mean discrepancy to improve the generalizability of brain-age prediction models on small-sample datasets.
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