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Faculty of Biomedical Sciences, Università Della Svizzera Italiana, Lugano, Switzerland
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Faculty of Biomedical Sciences, Università Della Svizzera Italiana, Lugano, Switzerland
Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
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Faculty of Biomedical Sciences, Università Della Svizzera Italiana, Lugano, Switzerland
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reliable and recommended by thyroid experts ( 2 , 3 ). Over the past decade, Thyroid Imaging and Reporting Data Systems (TIRADS) have achieved wide global adoption ( 4 ). One of their primary objectives is to standardize the lexicon used in thyroid
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on the “classic pattern” concept, a tool for US classification of thyroid nodules was developed by Horvath et al. [ 14 ] in 2009, known as the Thyroid Imaging Reporting and Data System (TIRADS). This is a system similar to the one used for breast
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(TIRADS) created by the American College of Radiology (ACR), the American Thyroid Association classification system, the French TIRADS, k-TIRADS, and the EU-TIRADS ( 2 , 3 , 4 , 5 , 6 ). These systems are based on a subjective assessment of the nodule
Servizio di Endocrinologia e Diabetologia, Ospedale Regionale di Lugano, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
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University of Latvia, Faculty of Medicine, Riga, Latvia
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Facoltà di Scienze Biomediche, Università della Svizzera Italiana (USI), Lugano, Switzerland
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studies in the field assessed the diagnostic efficacy of RSSs by selecting series of TNs suitable for either FNAC or surgery. This type of cohort may not accurately reflect the distribution of TNs across TIRADS categories among patients regularly visiting
Division of Endocrinology, Department of Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
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extension (ETE) ( 4 , 5 , 6 ). Several US thyroid nodule risk-classification systems have been proposed by scientific societies ( 6 , 7 , 8 , 9 , 10 , 11 ). These thyroid nodule image reporting and data systems (TIRADS) aim at providing indications
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less operator-dependent, a number of classification systems have been proposed, starting with the Thyroid Imaging Reporting and Data System (TIRADS) created by Hovarth et al. [ 3 ] with later modifications by other groups and authors. By the time the
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developed over the past decade. In 2009, Horvath et al. [ 7 ] first proposed US Thyroid Imaging Reporting and Data System (TIRADS) followed by Park et al. [ 8 ], Kwak et al. [ 9 ], and Russ et al. [ 10 ], who tried to improve the diagnostic performance and
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Radiology (ACR) TIRADS; (3) the American Thyroid Association (ATA) guidelines; (4) the European Thyroid Association (EU-TIRADS); and (5) the Korean TIRADS (K-TIRADS). For the purposes of this study, nodules that could not be classified according to the ATA
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]. Several US thyroid nodule risk-classification systems have been proposed by scientific societies [ 10 - 16 ]. These thyroid nodule image reporting and data systems (TIRADS) aim at providing indications for FNA, based on the combined results of the TIRADS
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Department of Medicine, Section of Endocrinology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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Departments of Medicine, Oncology, Pathology and Laboratory Medicine, Biochemistry and Molecular Biology, and Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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) Management Guidelines, the 2016 Korean Thyroid Association/Korean Society of Thyroid Radiology Guidelines (K-TIRADS), the 2017 European Thyroid Imaging and Reporting Data System (EU-TIRADS), the 2020 Chinese Thyroid Imaging Reporting and Data System (C-TIRADS