Accuracy of thyroid imaging reporting and data system category 4 or 5 for diagnosing malignancy: a systematic review and meta-analysis

Objectives To determine the accuracies of the American College of Radiology (ACR)–thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European (EU)-TIRADS for diagnosing malignancy in thyroid nodules. Methods Original studies reporting the diagnostic accuracy of TIRADS for de...

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Published inEuropean radiology Vol. 30; no. 10; pp. 5611 - 5624
Main Authors Kim, Dong Hwan, Chung, Sae Rom, Choi, Sang Hyun, Kim, Kyung Won
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020
Springer Nature B.V
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Abstract Objectives To determine the accuracies of the American College of Radiology (ACR)–thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European (EU)-TIRADS for diagnosing malignancy in thyroid nodules. Methods Original studies reporting the diagnostic accuracy of TIRADS for determining malignancy on ultrasound were identified in MEDLINE and EMBASE up to June 23, 2019. The meta-analytic summary sensitivity and specificity were obtained for TIRADS category 5 (TR-5) and category 4 or 5 (TR-4/5), using a bivariate random effects model. To explore study heterogeneity, meta-regression analyses were performed. Results Of the 34 eligible articles (37,585 nodules), 25 used ACR-TIRADS, 12 used K-TIRADS, and seven used EU-TIRADS. For TR-5, the meta-analytic sensitivity was highest for EU-TIRADS (78% [95% confidence interval, 64–88%]), followed by ACR-TIRADS (70% [61–79%]) and K-TIRADS (64% [58–70%]), although the differences were not significant. K-TIRADS showed the highest meta-analytic specificity (93% [91–95%]), which was similar to ACR-TIRADS (89% [85–92%]) and EU-TIRADS (89% [77–95%]). For TR-4/5, all three TIRADS systems had sensitivities higher than 90%. K-TIRADS had the highest specificity (61% [50–72%]), followed by ACR-TIRADS (49% [43–56%]) and EU-TIRADS (48% [35–62%]), although the differences were not significant. Considerable threshold effects were noted with ACR- and K-TIRADS ( p  ≤ 0.01), with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity ( p  ≤ 0.05). Conclusions There was no significant difference among these three international TIRADS, but the trend toward higher sensitivity with EU-TIRADS and higher specificity with K-TIRADS. Key Points • For TIRADS category 5, the meta-analytic sensitivity was highest for the EU-TIRADS, followed by the ACR-TIRADS and the K-TIRADS, although the differences were not significant. • For TIRADS category 5, K-TIRADS showed the highest meta-analytic specificity, which was similar to ACR-TIRADS and EU-TIRADS. • Considerable threshold effects were noted with ACR- and K-TIRADS, with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity.
AbstractList To determine the accuracies of the American College of Radiology (ACR)-thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European (EU)-TIRADS for diagnosing malignancy in thyroid nodules.OBJECTIVESTo determine the accuracies of the American College of Radiology (ACR)-thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European (EU)-TIRADS for diagnosing malignancy in thyroid nodules.Original studies reporting the diagnostic accuracy of TIRADS for determining malignancy on ultrasound were identified in MEDLINE and EMBASE up to June 23, 2019. The meta-analytic summary sensitivity and specificity were obtained for TIRADS category 5 (TR-5) and category 4 or 5 (TR-4/5), using a bivariate random effects model. To explore study heterogeneity, meta-regression analyses were performed.METHODSOriginal studies reporting the diagnostic accuracy of TIRADS for determining malignancy on ultrasound were identified in MEDLINE and EMBASE up to June 23, 2019. The meta-analytic summary sensitivity and specificity were obtained for TIRADS category 5 (TR-5) and category 4 or 5 (TR-4/5), using a bivariate random effects model. To explore study heterogeneity, meta-regression analyses were performed.Of the 34 eligible articles (37,585 nodules), 25 used ACR-TIRADS, 12 used K-TIRADS, and seven used EU-TIRADS. For TR-5, the meta-analytic sensitivity was highest for EU-TIRADS (78% [95% confidence interval, 64-88%]), followed by ACR-TIRADS (70% [61-79%]) and K-TIRADS (64% [58-70%]), although the differences were not significant. K-TIRADS showed the highest meta-analytic specificity (93% [91-95%]), which was similar to ACR-TIRADS (89% [85-92%]) and EU-TIRADS (89% [77-95%]). For TR-4/5, all three TIRADS systems had sensitivities higher than 90%. K-TIRADS had the highest specificity (61% [50-72%]), followed by ACR-TIRADS (49% [43-56%]) and EU-TIRADS (48% [35-62%]), although the differences were not significant. Considerable threshold effects were noted with ACR- and K-TIRADS (p ≤ 0.01), with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity (p ≤ 0.05).RESULTSOf the 34 eligible articles (37,585 nodules), 25 used ACR-TIRADS, 12 used K-TIRADS, and seven used EU-TIRADS. For TR-5, the meta-analytic sensitivity was highest for EU-TIRADS (78% [95% confidence interval, 64-88%]), followed by ACR-TIRADS (70% [61-79%]) and K-TIRADS (64% [58-70%]), although the differences were not significant. K-TIRADS showed the highest meta-analytic specificity (93% [91-95%]), which was similar to ACR-TIRADS (89% [85-92%]) and EU-TIRADS (89% [77-95%]). For TR-4/5, all three TIRADS systems had sensitivities higher than 90%. K-TIRADS had the highest specificity (61% [50-72%]), followed by ACR-TIRADS (49% [43-56%]) and EU-TIRADS (48% [35-62%]), although the differences were not significant. Considerable threshold effects were noted with ACR- and K-TIRADS (p ≤ 0.01), with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity (p ≤ 0.05).There was no significant difference among these three international TIRADS, but the trend toward higher sensitivity with EU-TIRADS and higher specificity with K-TIRADS.CONCLUSIONSThere was no significant difference among these three international TIRADS, but the trend toward higher sensitivity with EU-TIRADS and higher specificity with K-TIRADS.• For TIRADS category 5, the meta-analytic sensitivity was highest for the EU-TIRADS, followed by the ACR-TIRADS and the K-TIRADS, although the differences were not significant. • For TIRADS category 5, K-TIRADS showed the highest meta-analytic specificity, which was similar to ACR-TIRADS and EU-TIRADS. • Considerable threshold effects were noted with ACR- and K-TIRADS, with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity.KEY POINTS• For TIRADS category 5, the meta-analytic sensitivity was highest for the EU-TIRADS, followed by the ACR-TIRADS and the K-TIRADS, although the differences were not significant. • For TIRADS category 5, K-TIRADS showed the highest meta-analytic specificity, which was similar to ACR-TIRADS and EU-TIRADS. • Considerable threshold effects were noted with ACR- and K-TIRADS, with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity.
ObjectivesTo determine the accuracies of the American College of Radiology (ACR)–thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European (EU)-TIRADS for diagnosing malignancy in thyroid nodules.MethodsOriginal studies reporting the diagnostic accuracy of TIRADS for determining malignancy on ultrasound were identified in MEDLINE and EMBASE up to June 23, 2019. The meta-analytic summary sensitivity and specificity were obtained for TIRADS category 5 (TR-5) and category 4 or 5 (TR-4/5), using a bivariate random effects model. To explore study heterogeneity, meta-regression analyses were performed.ResultsOf the 34 eligible articles (37,585 nodules), 25 used ACR-TIRADS, 12 used K-TIRADS, and seven used EU-TIRADS. For TR-5, the meta-analytic sensitivity was highest for EU-TIRADS (78% [95% confidence interval, 64–88%]), followed by ACR-TIRADS (70% [61–79%]) and K-TIRADS (64% [58–70%]), although the differences were not significant. K-TIRADS showed the highest meta-analytic specificity (93% [91–95%]), which was similar to ACR-TIRADS (89% [85–92%]) and EU-TIRADS (89% [77–95%]). For TR-4/5, all three TIRADS systems had sensitivities higher than 90%. K-TIRADS had the highest specificity (61% [50–72%]), followed by ACR-TIRADS (49% [43–56%]) and EU-TIRADS (48% [35–62%]), although the differences were not significant. Considerable threshold effects were noted with ACR- and K-TIRADS (p ≤ 0.01), with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity (p ≤ 0.05).ConclusionsThere was no significant difference among these three international TIRADS, but the trend toward higher sensitivity with EU-TIRADS and higher specificity with K-TIRADS.Key Points• For TIRADS category 5, the meta-analytic sensitivity was highest for the EU-TIRADS, followed by the ACR-TIRADS and the K-TIRADS, although the differences were not significant.• For TIRADS category 5, K-TIRADS showed the highest meta-analytic specificity, which was similar to ACR-TIRADS and EU-TIRADS.• Considerable threshold effects were noted with ACR- and K-TIRADS, with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity.
Objectives To determine the accuracies of the American College of Radiology (ACR)–thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European (EU)-TIRADS for diagnosing malignancy in thyroid nodules. Methods Original studies reporting the diagnostic accuracy of TIRADS for determining malignancy on ultrasound were identified in MEDLINE and EMBASE up to June 23, 2019. The meta-analytic summary sensitivity and specificity were obtained for TIRADS category 5 (TR-5) and category 4 or 5 (TR-4/5), using a bivariate random effects model. To explore study heterogeneity, meta-regression analyses were performed. Results Of the 34 eligible articles (37,585 nodules), 25 used ACR-TIRADS, 12 used K-TIRADS, and seven used EU-TIRADS. For TR-5, the meta-analytic sensitivity was highest for EU-TIRADS (78% [95% confidence interval, 64–88%]), followed by ACR-TIRADS (70% [61–79%]) and K-TIRADS (64% [58–70%]), although the differences were not significant. K-TIRADS showed the highest meta-analytic specificity (93% [91–95%]), which was similar to ACR-TIRADS (89% [85–92%]) and EU-TIRADS (89% [77–95%]). For TR-4/5, all three TIRADS systems had sensitivities higher than 90%. K-TIRADS had the highest specificity (61% [50–72%]), followed by ACR-TIRADS (49% [43–56%]) and EU-TIRADS (48% [35–62%]), although the differences were not significant. Considerable threshold effects were noted with ACR- and K-TIRADS ( p  ≤ 0.01), with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity ( p  ≤ 0.05). Conclusions There was no significant difference among these three international TIRADS, but the trend toward higher sensitivity with EU-TIRADS and higher specificity with K-TIRADS. Key Points • For TIRADS category 5, the meta-analytic sensitivity was highest for the EU-TIRADS, followed by the ACR-TIRADS and the K-TIRADS, although the differences were not significant. • For TIRADS category 5, K-TIRADS showed the highest meta-analytic specificity, which was similar to ACR-TIRADS and EU-TIRADS. • Considerable threshold effects were noted with ACR- and K-TIRADS, with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity.
To determine the accuracies of the American College of Radiology (ACR)-thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European (EU)-TIRADS for diagnosing malignancy in thyroid nodules. Original studies reporting the diagnostic accuracy of TIRADS for determining malignancy on ultrasound were identified in MEDLINE and EMBASE up to June 23, 2019. The meta-analytic summary sensitivity and specificity were obtained for TIRADS category 5 (TR-5) and category 4 or 5 (TR-4/5), using a bivariate random effects model. To explore study heterogeneity, meta-regression analyses were performed. Of the 34 eligible articles (37,585 nodules), 25 used ACR-TIRADS, 12 used K-TIRADS, and seven used EU-TIRADS. For TR-5, the meta-analytic sensitivity was highest for EU-TIRADS (78% [95% confidence interval, 64-88%]), followed by ACR-TIRADS (70% [61-79%]) and K-TIRADS (64% [58-70%]), although the differences were not significant. K-TIRADS showed the highest meta-analytic specificity (93% [91-95%]), which was similar to ACR-TIRADS (89% [85-92%]) and EU-TIRADS (89% [77-95%]). For TR-4/5, all three TIRADS systems had sensitivities higher than 90%. K-TIRADS had the highest specificity (61% [50-72%]), followed by ACR-TIRADS (49% [43-56%]) and EU-TIRADS (48% [35-62%]), although the differences were not significant. Considerable threshold effects were noted with ACR- and K-TIRADS (p ≤ 0.01), with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity (p ≤ 0.05). There was no significant difference among these three international TIRADS, but the trend toward higher sensitivity with EU-TIRADS and higher specificity with K-TIRADS. • For TIRADS category 5, the meta-analytic sensitivity was highest for the EU-TIRADS, followed by the ACR-TIRADS and the K-TIRADS, although the differences were not significant. • For TIRADS category 5, K-TIRADS showed the highest meta-analytic specificity, which was similar to ACR-TIRADS and EU-TIRADS. • Considerable threshold effects were noted with ACR- and K-TIRADS, with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity.
Author Choi, Sang Hyun
Chung, Sae Rom
Kim, Dong Hwan
Kim, Kyung Won
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/32356157$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1111/j.1365-2362.2009.02162.x
10.3348/kjr.2018.19.3.534
10.1089/thy.2017.0560
10.3348/kjr.2016.17.3.370
10.2214/AJR.18.20510
10.1159/000478927
10.1111/cen.12515
10.3233/CH-180477
10.1089/thy.2015.0020
10.1530/EJE-18-0083
10.1148/radiol.2019182128
10.1097/RUQ.0000000000000350
10.1111/cen.13997
10.1002/cam4.2217
10.1002/hed.25530
10.1016/j.ultrasmedbio.2019.03.014
10.3348/kjr.2016.17.5.811
10.1089/thy.2018.0094
10.1016/j.ejrad.2017.10.027
10.1089/thy.2016.0603
10.1002/hed.23878
10.4158/EP-2018-0369
10.1155/2018/4923050
10.1136/bmj.b2700
10.1002/hed.25049
10.4158/EP161208.GL
10.1210/jc.2013-2928
10.3389/fonc.2019.00378
10.1007/s12020-019-01886-0
10.1089/thy.2008.0354
10.14366/usg.15027
10.1007/s00330-018-5992-z
10.1007/s12020-018-1620-6
10.1148/radiol.2473070944
10.7326/0003-4819-155-8-201110180-00009
10.1016/j.ultrasmedbio.2019.05.001
10.1210/jc.2008-1724
10.1089/thy.2017.0363
10.1055/a-0743-7326
10.1210/jc.2018-01674
10.1148/radiol.2018172572
10.5114/pjr.2018.81556
10.2214/AJR.16.17613
10.1002/9780470743386
10.31557/APJCP.2019.20.4.1283
10.1016/j.surg.2018.04.094
10.1056/NEJMp1409841
10.2214/AJR.18.20961
10.1007/s12020-018-1817-8
10.1002/jum.14316
10.1001/jamaoto.2014.1
10.1007/s12020-019-01843-x
10.1016/j.jacr.2017.01.046
10.1089/thy.2017.0034
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Fri Jul 25 19:03:50 EDT 2025
Mon Jul 21 06:01:46 EDT 2025
Tue Jul 01 03:08:15 EDT 2025
Thu Apr 24 22:55:40 EDT 2025
Fri Feb 21 02:33:02 EST 2025
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Issue 10
Keywords Thyroid neoplasms
Systematic review
Diagnostic imaging
Ultrasonography
Meta-analysis
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References Ha, Ahn, Baek (CR24) 2017; 27
Horvath, Majlis, Rossi (CR8) 2009; 94
Hang, Li, Qiao, Ye, Li, Du (CR29) 2018; 2018
Wildman-Tobriner, Buda, Hoang (CR47) 2019; 292
Sahli, Karipineni, Hang (CR44) 2019; 165
Davies, Welch (CR52) 2014; 140
Haugen, Alexander, Bible (CR2) 2016; 26
Wei, Li, Zhang, Gao (CR12) 2016; 38
Liberati, Altman, Tetzlaff (CR21) 2009; 339
Ahn, Na, Baek, Sung, Kim (CR37) 2019; 41
Koseoglu Atilla, Ozgen Saydam, Erarslan (CR18) 2018; 61
Hong, Na, Baek, Sung, Kim (CR31) 2018; 19
Middleton, Teefey, Reading (CR14) 2017; 208
Chen, Zhan, Diao (CR38) 2019; 45
Perros, Boelaert, Colley (CR51) 2014; 81
Phuttharak, Boonrod, Klungboonkrong, Witsawapaisan (CR42) 2019; 20
Gao, Liu, Jiang (CR27) 2018; 40
Hong, Na, Baek, Sung, Kim (CR25) 2017; 27
Brito, Gionfriddo, Al Nofal (CR4) 2014; 99
Ha, Kim, Baek (CR23) 2017; 27
CR46
Moon, Jung, Lee (CR5) 2008; 247
Ha, Moon, Na (CR13) 2016; 17
Li, Hou, Du (CR41) 2019; 72
Hong, Lee (CR40) 2019; 213
Guth, Theune, Aberle, Galach, Bamberger (CR1) 2009; 39
Yoon, Na, Gwon (CR49) 2019; 213
Lee, Yoon, Seo (CR7) 2018; 37
Russ, Bonnema, Erdogan, Durante, Ngu, Leenhardt (CR11) 2017; 6
Zhu, Li, Wei (CR20) 2019; 8
Ahmadi, Oyekunle, Jiang (CR36) 2019; 25
Ruan, Yang, Liu (CR43) 2019; 29
Hoang, Middleton, Farjat (CR30) 2018; 287
Skowronska, Milczarek-Banach, Wiechno (CR34) 2018; 83
Ahn, Kim, Welch (CR53) 2014; 371
Shen, Liu, He (CR45) 2019; 9
Gharib, Papini, Garber (CR3) 2016; 22
Lauria Pantano, Maddaloni, Briganti (CR32) 2018; 178
Russ (CR50) 2016; 35
Choi, Kim, Kwak, Kim, Son (CR6) 2010; 20
Rosario, da Silva, Nunes, Borges (CR33) 2018; 50
Xu, Wu, Wu (CR48) 2019; 64
Wu, Du, Wang (CR19) 2019; 65
Shin, Baek, Chung (CR9) 2016; 17
Whiting, Rutjes, Westwood (CR22) 2011; 155
Ha, Na, Moon, Lee, Choi (CR28) 2018; 28
Jin, Yu, Mo, Su (CR16) 2019; 45
Tessler, Middleton, Grant (CR10) 2017; 14
Chung, Choi, Suh (CR26) 2018; 28
Bae, Hahn, Shin, Ko (CR17) 2018; 98
Borenstein, Hedges, Higgins, Rothstein (CR54) 2009
Grani, Lamartina, Ascoli (CR15) 2019; 104
Gao, Xi, Jiang (CR39) 2019; 64
Zheng, Xu, Kang, Zhan (CR35) 2018; 34
S Ahmadi (6875_CR36) 2019; 25
L Gao (6875_CR39) 2019; 64
6875_CR46
G Russ (6875_CR50) 2016; 35
SH Choi (6875_CR6) 2010; 20
FD Koseoglu Atilla (6875_CR18) 2018; 61
S Guth (6875_CR1) 2009; 39
JH Shin (6875_CR9) 2016; 17
A Liberati (6875_CR21) 2009; 339
HJ Lee (6875_CR7) 2018; 37
ZQ Jin (6875_CR16) 2019; 45
Y Shen (6875_CR45) 2019; 9
FN Tessler (6875_CR10) 2017; 14
PF Whiting (6875_CR22) 2011; 155
A Lauria Pantano (6875_CR32) 2018; 178
E Horvath (6875_CR8) 2009; 94
HS Ahn (6875_CR37) 2019; 41
SM Ha (6875_CR24) 2017; 27
G Grani (6875_CR15) 2019; 104
MJ Hong (6875_CR31) 2018; 19
JK Hoang (6875_CR30) 2018; 287
L Chen (6875_CR38) 2019; 45
J Hang (6875_CR29) 2018; 2018
B Wildman-Tobriner (6875_CR47) 2019; 292
WD Middleton (6875_CR14) 2017; 208
JL Ruan (6875_CR43) 2019; 29
MJ Hong (6875_CR25) 2017; 27
W Phuttharak (6875_CR42) 2019; 20
PW Rosario (6875_CR33) 2018; 50
XL Wu (6875_CR19) 2019; 65
SJ Yoon (6875_CR49) 2019; 213
SR Chung (6875_CR26) 2018; 28
EJ Ha (6875_CR28) 2018; 28
H Gharib (6875_CR3) 2016; 22
G Russ (6875_CR11) 2017; 6
SM Ha (6875_CR23) 2017; 27
HS Ahn (6875_CR53) 2014; 371
X Li (6875_CR41) 2019; 72
M Borenstein (6875_CR54) 2009
EJ Ha (6875_CR13) 2016; 17
BR Haugen (6875_CR2) 2016; 26
JM Bae (6875_CR17) 2018; 98
A Skowronska (6875_CR34) 2018; 83
HS Hong (6875_CR40) 2019; 213
JP Brito (6875_CR4) 2014; 99
L Gao (6875_CR27) 2018; 40
T Xu (6875_CR48) 2019; 64
L Davies (6875_CR52) 2014; 140
WJ Moon (6875_CR5) 2008; 247
X Wei (6875_CR12) 2016; 38
ZT Sahli (6875_CR44) 2019; 165
Y Zheng (6875_CR35) 2018; 34
J Zhu (6875_CR20) 2019; 8
P Perros (6875_CR51) 2014; 81
References_xml – volume: 20
  start-page: 167
  year: 2010
  end-page: 172
  ident: CR6
  article-title: Interobserver and intraobserver variations in ultrasound assessment of thyroid nodules
  publication-title: Thyroid
– volume: 98
  start-page: 14
  year: 2018
  end-page: 19
  ident: CR17
  article-title: Inter-exam agreement and diagnostic performance of the Korean thyroid imaging reporting and data system for thyroid nodule assessment: real-time versus static ultrasonography
  publication-title: Eur J Radiol
– volume: 27
  start-page: 953
  year: 2017
  end-page: 959
  ident: CR25
  article-title: Cytology-ultrasonography risk-stratification scoring system based on fine-needle aspiration cytology and the Korean-thyroid imaging reporting and data system
  publication-title: Thyroid
– volume: 64
  start-page: 90
  year: 2019
  end-page: 96
  ident: CR39
  article-title: Comparison among TIRADS (ACR TI-RADS and KWAK- TI-RADS) and 2015 ATA guidelines in the diagnostic efficiency of thyroid nodules
  publication-title: Endocrine
– volume: 72
  start-page: 279
  year: 2019
  end-page: 291
  ident: CR41
  article-title: Virtual touch tissue imaging and quantification (VTIQ) combined with the American College of Radiology thyroid imaging reporting and data system (ACR TI-RADS) for malignancy risk stratification of thyroid nodules
  publication-title: Clin Hemorheol Microcirc
– volume: 45
  start-page: 1627
  year: 2019
  end-page: 1637
  ident: CR16
  article-title: Clinical study of the prediction of malignancy in thyroid nodules: modified score versus 2017 American College of Radiology’s thyroid imaging reporting and data system ultrasound lexicon
  publication-title: Ultrasound Med Biol
– volume: 26
  start-page: 1
  year: 2016
  end-page: 133
  ident: CR2
  article-title: 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer
  publication-title: Thyroid
– volume: 213
  start-page: W76
  year: 2019
  end-page: W84
  ident: CR49
  article-title: Similarities and differences between thyroid imaging reporting and data systems
  publication-title: AJR Am J Roentgenol
– volume: 25
  start-page: 413
  year: 2019
  end-page: 422
  ident: CR36
  article-title: A direct comparison of the ATA and TI-RADS ultrasound scoring systems
  publication-title: Endocr Pract
– volume: 20
  start-page: 1283
  year: 2019
  end-page: 1288
  ident: CR42
  article-title: Interrater reliability of various thyroid imaging reporting and data system (TIRADS) classifications for differentiating benign from malignant thyroid nodules
  publication-title: Asian Pac J Cancer Prev
– volume: 28
  start-page: 762
  year: 2018
  end-page: 768
  ident: CR26
  article-title: Thyroid incidentalomas detected on (18)F-fluorodeoxyglucose positron emission tomography with computed tomography: malignant risk stratification and management plan
  publication-title: Thyroid
– volume: 81
  start-page: 1
  issue: Suppl 1
  year: 2014
  end-page: 122
  ident: CR51
  article-title: Guidelines for the management of thyroid cancer
  publication-title: Clin Endocrinol (Oxf)
– year: 2009
  ident: CR54
  publication-title: Introduction to meta-analysis
– volume: 208
  start-page: 1331
  year: 2017
  end-page: 1341
  ident: CR14
  article-title: Multiinstitutional analysis of thyroid nodule risk stratification using the American College of Radiology thyroid imaging reporting and data system
  publication-title: AJR Am J Roentgenol
– volume: 64
  start-page: 299
  year: 2019
  end-page: 307
  ident: CR48
  article-title: Validation and comparison of three newly-released thyroid imaging reporting and data systems for cancer risk determination
  publication-title: Endocrine
– ident: CR46
– volume: 38
  start-page: 309
  year: 2016
  end-page: 315
  ident: CR12
  article-title: Meta-analysis of thyroid imaging reporting and data system in the ultrasonographic diagnosis of 10,437 thyroid nodules
  publication-title: Head Neck
– volume: 140
  start-page: 317
  year: 2014
  end-page: 322
  ident: CR52
  article-title: Current thyroid cancer trends in the United States
  publication-title: JAMA Otolaryngol Head Neck Surg
– volume: 40
  start-page: 778
  year: 2018
  end-page: 783
  ident: CR27
  article-title: Computer-aided system for diagnosing thyroid nodules on ultrasound: a comparison with radiologist-based clinical assessments
  publication-title: Head Neck
– volume: 35
  start-page: 25
  year: 2016
  end-page: 38
  ident: CR50
  article-title: Risk stratification of thyroid nodules on ultrasonography with the French TI-RADS: description and reflections
  publication-title: Ultrasonography
– volume: 94
  start-page: 1748
  year: 2009
  end-page: 1751
  ident: CR8
  article-title: An ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management
  publication-title: J Clin Endocrinol Metab
– volume: 61
  start-page: 398
  year: 2018
  end-page: 402
  ident: CR18
  article-title: Does the ACR TI-RADS scoring allow us to safely avoid unnecessary thyroid biopsy? single center analysis in a large cohort
  publication-title: Endocrine
– volume: 29
  start-page: 4871
  year: 2019
  end-page: 4878
  ident: CR43
  article-title: Fine needle aspiration biopsy indications for thyroid nodules: compare a point-based risk stratification system with a pattern-based risk stratification system
  publication-title: Eur Radiol
– volume: 9
  start-page: 378
  year: 2019
  ident: CR45
  article-title: Comparison of different risk-stratification systems for the diagnosis of benign and malignant thyroid nodules
  publication-title: Front Oncol
– volume: 8
  start-page: 3389
  year: 2019
  end-page: 3400
  ident: CR20
  article-title: The application value of modified thyroid imaging report and data system in diagnosing medullary thyroid carcinoma
  publication-title: Cancer Med
– volume: 165
  start-page: 69
  year: 2019
  end-page: 74
  ident: CR44
  article-title: The association between the ultrasonography TIRADS classification system and surgical pathology among indeterminate thyroid nodules
  publication-title: Surgery
– volume: 83
  start-page: e579
  year: 2018
  end-page: e586
  ident: CR34
  article-title: Accuracy of the European thyroid imaging reporting and data system (EU-TIRADS) in the valuation of thyroid nodule malignancy in reference to the post-surgery histological results
  publication-title: Pol J Radiol
– volume: 27
  start-page: 1307
  year: 2017
  end-page: 1315
  ident: CR23
  article-title: Detection of malignancy among suspicious thyroid nodules <1 cm on ultrasound with various thyroid image reporting and data systems
  publication-title: Thyroid
– volume: 104
  start-page: 95
  year: 2019
  end-page: 102
  ident: CR15
  article-title: Reducing the number of unnecessary thyroid biopsies while improving diagnostic accuracy: toward the “right” TIRADS
  publication-title: J Clin Endocrinol Metab
– volume: 50
  start-page: 735
  year: 2018
  end-page: 737
  ident: CR33
  article-title: Risk of malignancy in thyroid nodules using the American College of Radiology thyroid imaging reporting and data system in the NIFTP era
  publication-title: Horm Metab Res
– volume: 155
  start-page: 529
  year: 2011
  end-page: 536
  ident: CR22
  article-title: QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
  publication-title: Ann Intern Med
– volume: 41
  start-page: 967
  year: 2019
  end-page: 973
  ident: CR37
  article-title: False negative rate of fine-needle aspiration in thyroid nodules: impact of nodule size and ultrasound pattern
  publication-title: Head Neck
– volume: 17
  start-page: 370
  year: 2016
  end-page: 395
  ident: CR9
  article-title: Ultrasonography diagnosis and imaging-based management of thyroid nodules: revised Korean Society of Thyroid Radiology consensus statement and recommendations
  publication-title: Korean J Radiol
– volume: 28
  start-page: 1532
  year: 2018
  end-page: 1537
  ident: CR28
  article-title: Diagnostic performance of ultrasound-based risk-stratification systems for thyroid nodules: comparison of the 2015 American Thyroid Association guidelines with the 2016 Korean thyroid association/Korean society of thyroid radiology and 2017 American congress of radiology guidelines
  publication-title: Thyroid
– volume: 22
  start-page: 622
  year: 2016
  end-page: 639
  ident: CR3
  article-title: American Association of Clinical Endocrinologists, American College of Endocrinology, and Associazione Medici Endocrinologi medical guidelines for clinical practice for the diagnosis and management of thyroid nodules--2016 update
  publication-title: Endocr Pract
– volume: 27
  start-page: 1550
  year: 2017
  end-page: 1557
  ident: CR24
  article-title: Validation of three scoring risk-stratification models for thyroid nodules
  publication-title: Thyroid
– volume: 292
  start-page: 112
  year: 2019
  end-page: 119
  ident: CR47
  article-title: Using artificial intelligence to revise ACR TI-RADS risk stratification of thyroid nodules: diagnostic accuracy and utility
  publication-title: Radiology
– volume: 247
  start-page: 762
  year: 2008
  end-page: 770
  ident: CR5
  article-title: Benign and malignant thyroid nodules: US differentiation--multicenter retrospective study
  publication-title: Radiology
– volume: 39
  start-page: 699
  year: 2009
  end-page: 706
  ident: CR1
  article-title: Very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultrasound examination
  publication-title: Eur J Clin Invest
– volume: 6
  start-page: 225
  year: 2017
  end-page: 237
  ident: CR11
  article-title: European thyroid association guidelines for ultrasound malignancy risk stratification of thyroid nodules in adults: the EU-TIRADS
  publication-title: Eur Thyroid J
– volume: 99
  start-page: 1253
  year: 2014
  end-page: 1263
  ident: CR4
  article-title: The accuracy of thyroid nodule ultrasound to predict thyroid cancer: systematic review and meta-analysis
  publication-title: J Clin Endocrinol Metab
– volume: 178
  start-page: 595
  year: 2018
  end-page: 603
  ident: CR32
  article-title: Differences between ATA, AACE/ACE/AME and ACR TI-RADS ultrasound classifications performance in identifying cytological high-risk thyroid nodules
  publication-title: Eur J Endocrinol
– volume: 371
  start-page: 1765
  year: 2014
  end-page: 1767
  ident: CR53
  article-title: Korea’s thyroid-cancer “epidemic”--screening and overdiagnosis
  publication-title: N Engl J Med
– volume: 2018
  start-page: 4923050
  year: 2018
  ident: CR29
  article-title: Combination of maximum shear wave elasticity modulus and TIRADS improves the diagnostic specificity in characterizing thyroid nodules: a retrospective study
  publication-title: Int J Endocrinol
– volume: 45
  start-page: 2040
  year: 2019
  end-page: 2048
  ident: CR38
  article-title: Additional value of superb microvascular imaging for thyroid nodule classification with the thyroid imaging reporting and data system
  publication-title: Ultrasound Med Biol
– volume: 19
  start-page: 534
  year: 2018
  end-page: 541
  ident: CR31
  article-title: Impact of nodule size on malignancy risk differs according to the ultrasonography pattern of thyroid nodules
  publication-title: Korean J Radiol
– volume: 213
  start-page: 444
  year: 2019
  end-page: 450
  ident: CR40
  article-title: Diagnostic performance of ultrasound patterns by K-TIRADS and 2015 ATA guidelines in risk stratification of thyroid nodules and follicular lesions of undetermined significance
  publication-title: AJR Am J Roentgenol
– volume: 37
  start-page: 173
  year: 2018
  end-page: 178
  ident: CR7
  article-title: Intraobserver and interobserver variability in ultrasound measurements of thyroid nodules
  publication-title: J Ultrasound Med
– volume: 14
  start-page: 587
  year: 2017
  end-page: 595
  ident: CR10
  article-title: ACR thyroid imaging, reporting and data system (TI-RADS): white paper of the ACR TI-RADS committee
  publication-title: J Am Coll Radiol
– volume: 17
  start-page: 811
  year: 2016
  end-page: 821
  ident: CR13
  article-title: A multicenter prospective validation study for the Korean thyroid imaging reporting and data system in patients with thyroid nodules
  publication-title: Korean J Radiol
– volume: 34
  start-page: 77
  year: 2018
  end-page: 83
  ident: CR35
  article-title: A single-center retrospective validation study of the American College of Radiology thyroid imaging reporting and data system
  publication-title: Ultrasound Q
– volume: 65
  start-page: 121
  year: 2019
  end-page: 131
  ident: CR19
  article-title: Comparison and preliminary discussion of the reasons for the differences in diagnostic performance and unnecessary FNA biopsies between the ACR TIRADS and 2015 ATA guidelines
  publication-title: Endocrine
– volume: 339
  start-page: b2700
  year: 2009
  ident: CR21
  article-title: The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration
  publication-title: BMJ
– volume: 287
  start-page: 185
  year: 2018
  end-page: 193
  ident: CR30
  article-title: Reduction in thyroid nodule biopsies and improved accuracy with American College of Radiology thyroid imaging reporting and data system
  publication-title: Radiology
– volume: 39
  start-page: 699
  year: 2009
  ident: 6875_CR1
  publication-title: Eur J Clin Invest
  doi: 10.1111/j.1365-2362.2009.02162.x
– volume: 19
  start-page: 534
  year: 2018
  ident: 6875_CR31
  publication-title: Korean J Radiol
  doi: 10.3348/kjr.2018.19.3.534
– volume: 28
  start-page: 762
  year: 2018
  ident: 6875_CR26
  publication-title: Thyroid
  doi: 10.1089/thy.2017.0560
– volume: 17
  start-page: 370
  year: 2016
  ident: 6875_CR9
  publication-title: Korean J Radiol
  doi: 10.3348/kjr.2016.17.3.370
– volume: 213
  start-page: W76
  year: 2019
  ident: 6875_CR49
  publication-title: AJR Am J Roentgenol
  doi: 10.2214/AJR.18.20510
– volume: 6
  start-page: 225
  year: 2017
  ident: 6875_CR11
  publication-title: Eur Thyroid J
  doi: 10.1159/000478927
– volume: 81
  start-page: 1
  issue: Suppl 1
  year: 2014
  ident: 6875_CR51
  publication-title: Clin Endocrinol (Oxf)
  doi: 10.1111/cen.12515
– volume: 72
  start-page: 279
  year: 2019
  ident: 6875_CR41
  publication-title: Clin Hemorheol Microcirc
  doi: 10.3233/CH-180477
– volume: 26
  start-page: 1
  year: 2016
  ident: 6875_CR2
  publication-title: Thyroid
  doi: 10.1089/thy.2015.0020
– volume: 178
  start-page: 595
  year: 2018
  ident: 6875_CR32
  publication-title: Eur J Endocrinol
  doi: 10.1530/EJE-18-0083
– volume: 292
  start-page: 112
  year: 2019
  ident: 6875_CR47
  publication-title: Radiology
  doi: 10.1148/radiol.2019182128
– volume: 34
  start-page: 77
  year: 2018
  ident: 6875_CR35
  publication-title: Ultrasound Q
  doi: 10.1097/RUQ.0000000000000350
– ident: 6875_CR46
  doi: 10.1111/cen.13997
– volume: 8
  start-page: 3389
  year: 2019
  ident: 6875_CR20
  publication-title: Cancer Med
  doi: 10.1002/cam4.2217
– volume: 41
  start-page: 967
  year: 2019
  ident: 6875_CR37
  publication-title: Head Neck
  doi: 10.1002/hed.25530
– volume: 45
  start-page: 1627
  year: 2019
  ident: 6875_CR16
  publication-title: Ultrasound Med Biol
  doi: 10.1016/j.ultrasmedbio.2019.03.014
– volume: 17
  start-page: 811
  year: 2016
  ident: 6875_CR13
  publication-title: Korean J Radiol
  doi: 10.3348/kjr.2016.17.5.811
– volume: 28
  start-page: 1532
  year: 2018
  ident: 6875_CR28
  publication-title: Thyroid
  doi: 10.1089/thy.2018.0094
– volume: 98
  start-page: 14
  year: 2018
  ident: 6875_CR17
  publication-title: Eur J Radiol
  doi: 10.1016/j.ejrad.2017.10.027
– volume: 27
  start-page: 953
  year: 2017
  ident: 6875_CR25
  publication-title: Thyroid
  doi: 10.1089/thy.2016.0603
– volume: 38
  start-page: 309
  year: 2016
  ident: 6875_CR12
  publication-title: Head Neck
  doi: 10.1002/hed.23878
– volume: 25
  start-page: 413
  year: 2019
  ident: 6875_CR36
  publication-title: Endocr Pract
  doi: 10.4158/EP-2018-0369
– volume: 2018
  start-page: 4923050
  year: 2018
  ident: 6875_CR29
  publication-title: Int J Endocrinol
  doi: 10.1155/2018/4923050
– volume: 339
  start-page: b2700
  year: 2009
  ident: 6875_CR21
  publication-title: BMJ
  doi: 10.1136/bmj.b2700
– volume: 40
  start-page: 778
  year: 2018
  ident: 6875_CR27
  publication-title: Head Neck
  doi: 10.1002/hed.25049
– volume: 22
  start-page: 622
  year: 2016
  ident: 6875_CR3
  publication-title: Endocr Pract
  doi: 10.4158/EP161208.GL
– volume: 99
  start-page: 1253
  year: 2014
  ident: 6875_CR4
  publication-title: J Clin Endocrinol Metab
  doi: 10.1210/jc.2013-2928
– volume: 9
  start-page: 378
  year: 2019
  ident: 6875_CR45
  publication-title: Front Oncol
  doi: 10.3389/fonc.2019.00378
– volume: 65
  start-page: 121
  year: 2019
  ident: 6875_CR19
  publication-title: Endocrine
  doi: 10.1007/s12020-019-01886-0
– volume: 20
  start-page: 167
  year: 2010
  ident: 6875_CR6
  publication-title: Thyroid
  doi: 10.1089/thy.2008.0354
– volume: 35
  start-page: 25
  year: 2016
  ident: 6875_CR50
  publication-title: Ultrasonography
  doi: 10.14366/usg.15027
– volume: 29
  start-page: 4871
  year: 2019
  ident: 6875_CR43
  publication-title: Eur Radiol
  doi: 10.1007/s00330-018-5992-z
– volume: 61
  start-page: 398
  year: 2018
  ident: 6875_CR18
  publication-title: Endocrine
  doi: 10.1007/s12020-018-1620-6
– volume: 247
  start-page: 762
  year: 2008
  ident: 6875_CR5
  publication-title: Radiology
  doi: 10.1148/radiol.2473070944
– volume: 155
  start-page: 529
  year: 2011
  ident: 6875_CR22
  publication-title: Ann Intern Med
  doi: 10.7326/0003-4819-155-8-201110180-00009
– volume: 45
  start-page: 2040
  year: 2019
  ident: 6875_CR38
  publication-title: Ultrasound Med Biol
  doi: 10.1016/j.ultrasmedbio.2019.05.001
– volume: 94
  start-page: 1748
  year: 2009
  ident: 6875_CR8
  publication-title: J Clin Endocrinol Metab
  doi: 10.1210/jc.2008-1724
– volume: 27
  start-page: 1550
  year: 2017
  ident: 6875_CR24
  publication-title: Thyroid
  doi: 10.1089/thy.2017.0363
– volume: 50
  start-page: 735
  year: 2018
  ident: 6875_CR33
  publication-title: Horm Metab Res
  doi: 10.1055/a-0743-7326
– volume: 104
  start-page: 95
  year: 2019
  ident: 6875_CR15
  publication-title: J Clin Endocrinol Metab
  doi: 10.1210/jc.2018-01674
– volume: 287
  start-page: 185
  year: 2018
  ident: 6875_CR30
  publication-title: Radiology
  doi: 10.1148/radiol.2018172572
– volume: 83
  start-page: e579
  year: 2018
  ident: 6875_CR34
  publication-title: Pol J Radiol
  doi: 10.5114/pjr.2018.81556
– volume: 208
  start-page: 1331
  year: 2017
  ident: 6875_CR14
  publication-title: AJR Am J Roentgenol
  doi: 10.2214/AJR.16.17613
– volume-title: Introduction to meta-analysis
  year: 2009
  ident: 6875_CR54
  doi: 10.1002/9780470743386
– volume: 20
  start-page: 1283
  year: 2019
  ident: 6875_CR42
  publication-title: Asian Pac J Cancer Prev
  doi: 10.31557/APJCP.2019.20.4.1283
– volume: 165
  start-page: 69
  year: 2019
  ident: 6875_CR44
  publication-title: Surgery
  doi: 10.1016/j.surg.2018.04.094
– volume: 371
  start-page: 1765
  year: 2014
  ident: 6875_CR53
  publication-title: N Engl J Med
  doi: 10.1056/NEJMp1409841
– volume: 213
  start-page: 444
  year: 2019
  ident: 6875_CR40
  publication-title: AJR Am J Roentgenol
  doi: 10.2214/AJR.18.20961
– volume: 64
  start-page: 299
  year: 2019
  ident: 6875_CR48
  publication-title: Endocrine
  doi: 10.1007/s12020-018-1817-8
– volume: 37
  start-page: 173
  year: 2018
  ident: 6875_CR7
  publication-title: J Ultrasound Med
  doi: 10.1002/jum.14316
– volume: 140
  start-page: 317
  year: 2014
  ident: 6875_CR52
  publication-title: JAMA Otolaryngol Head Neck Surg
  doi: 10.1001/jamaoto.2014.1
– volume: 64
  start-page: 90
  year: 2019
  ident: 6875_CR39
  publication-title: Endocrine
  doi: 10.1007/s12020-019-01843-x
– volume: 14
  start-page: 587
  year: 2017
  ident: 6875_CR10
  publication-title: J Am Coll Radiol
  doi: 10.1016/j.jacr.2017.01.046
– volume: 27
  start-page: 1307
  year: 2017
  ident: 6875_CR23
  publication-title: Thyroid
  doi: 10.1089/thy.2017.0034
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Snippet Objectives To determine the accuracies of the American College of Radiology (ACR)–thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and...
To determine the accuracies of the American College of Radiology (ACR)-thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European...
ObjectivesTo determine the accuracies of the American College of Radiology (ACR)–thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and...
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SubjectTerms Biopsy, Fine-Needle
Bivariate analysis
Clarity
Confidence intervals
Data Systems
Diagnostic Radiology
Diagnostic systems
Europe
Head and Neck
Heterogeneity
Humans
Imaging
Internal Medicine
Interventional Radiology
Malignancy
Mathematical analysis
Medical diagnosis
Medical imaging
Medicine
Medicine & Public Health
Meta-analysis
Neuroradiology
Nodules
Radiology
Regression Analysis
Reproducibility of Results
Republic of Korea
Research Design
Sensitivity analysis
Sensitivity and Specificity
Statistical analysis
Thyroid
Thyroid cancer
Thyroid gland
Thyroid Neoplasms - diagnostic imaging
Thyroid Nodule - diagnostic imaging
Ultrasonography - standards
Ultrasound
United States
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Title Accuracy of thyroid imaging reporting and data system category 4 or 5 for diagnosing malignancy: a systematic review and meta-analysis
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