LEADER 04089nam 2201045z- 450 001 9910566457803321 005 20220506 035 $a(CKB)5680000000037803 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/80956 035 $a(oapen)doab80956 035 $a(EXLCZ)995680000000037803 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRisk Stratification of Thyroid Nodule: From Ultrasound Features to TIRADS 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (212 p.) 311 08$a3-0365-3760-0 311 08$a3-0365-3759-7 330 $aSince the 1990s, ultrasound (US) has played a major role in the assessment of thyroid nodules and their risk of malignancy. Over the last decade, the most eminent international societies have published US-based systems for the risk stratification of thyroid lesions, namely, Thyroid Imaging Reporting And Data Systems (TIRADSs). The introduction of TIRADSs into clinical practice has significantly increased the diagnostic power of US to a level approaching that of fine-needle aspiration cytology (FNAC). At present, we are probably approaching a new era in which US could be the primary tool to diagnose thyroid cancer. However, before using US in this new dominant role, we need further proof. This Special Issue, which includes reviews and original articles, aims to pave the way for the future in the field of thyroid US. Highly experienced thyroidologists focused on US are asked to contribute to achieve this goal. 517 $aRisk Stratification of Thyroid Nodule 606 $aClinical and internal medicine$2bicssc 606 $aMedicine and Nursing$2bicssc 610 $aartificial intelligence 610 $abenign thyroid nodules 610 $abiopsy 610 $acancer 610 $aclassification system 610 $acontrast-enhanced ultrasound (CEUS) 610 $adeep learning 610 $adiagnosis 610 $aDTC 610 $aDTC recurrences 610 $aelastosonography 610 $afine-needle 610 $afine-needle aspiration 610 $afine-needle aspiration biopsy 610 $afollicular lesion of unknown significance 610 $afollicular neoplasm 610 $afollicular thyroid cancer 610 $afollow-up 610 $along term 610 $amachine learning 610 $amedical imaging 610 $an/a 610 $aneck ultrasound 610 $aneoplasm metastasis 610 $anodule 610 $anon-autonomously functioning 610 $apaediatrics 610 $apapillary thyroid cancer 610 $apapillary thyroid carcinoma 610 $apediatric thyroid nodules 610 $aprediction 610 $aPTMC 610 $aradiofrequency ablation 610 $aradiomics 610 $aradiotherapy 610 $aregrowth 610 $aRFA 610 $arisk assessment 610 $arisk of malignancy (ROM) 610 $arisk stratification 610 $ascintigraphy 610 $athyroglobulin 610 $athyroid 610 $athyroid cancer 610 $athyroid imaging reporting and data systems (TIRADS) 610 $aThyroid Imaging Reporting and Data Systems (TIRADS) 610 $athyroid neoplasm 610 $athyroid nodule 610 $athyroid nodules 610 $aTI-RADS 610 $aTIRAD 610 $aTIRADS 610 $aultrasonography 610 $aultrasound 610 $aultrasound classification system 610 $aUS-guided minimally invasive techniques 615 7$aClinical and internal medicine 615 7$aMedicine and Nursing 700 $aTrimboli$b Pierpaolo$4edt$01326286 702 $aTrimboli$b Pierpaolo$4oth 906 $aBOOK 912 $a9910566457803321 996 $aRisk Stratification of Thyroid Nodule: From Ultrasound Features to TIRADS$93037266 997 $aUNINA