xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT05982821</url>
    </required_header>
    <id_info>
      <org_study_id>SYSKY-2023-702-01</org_study_id>
      <nct_id>NCT05982821</nct_id>
    </id_info>
    <brief_title>Artificial Intelligent Accelerates the Learning Curve for Mastering Contrast-enhanced Ultrasound of Thyroid Nodules</brief_title>
    <official_title>Artificial Intelligent Accelerates the Learning Curve for Mastering Thyroid Imaging Reporting and Data System of Contrast-enhanced Ultrasound</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</source>
    <oversight_info>
      <has_dmc>Yes</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The goal of this observational study is to learn about the learning curve for mastering the&#xD;
        thyroid imaging reporting and data system of contrast-enhanced ultrasound with the assistance&#xD;
        of artificial intelligence in patients with thyroid nodules. The main questions it aims to&#xD;
        answer are:&#xD;
  &#xD;
          1. Can we develop a artificial intelligent software to assist doctors in the diagnosis of&#xD;
             thyroid nodules using contrast-enhanced ultrasound?&#xD;
  &#xD;
          2. Can artificial intelligent reduce the number of cases and time for doctors to master the&#xD;
             contrast-enhanced ultrasound diagnosis of thyroid nodules?&#xD;
  &#xD;
        Participants will be asked to undergo contrast-enhanced ultrasound examination and&#xD;
        ultrasound-guided fine-needle aspiration of thyroid nodules. Researchers will compare the&#xD;
        number of cases and time for doctors with and without artificial intelligent assistance to&#xD;
        master the contrast-enhanced ultrasound diagnosis of thyroid nodules to see if artificial&#xD;
        intelligent reduce the number of cases and time.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Recruiting</overall_status>
    <start_date type="Anticipated">January 3, 2024</start_date>
    <completion_date type="Anticipated">December 31, 2026</completion_date>
    <primary_completion_date type="Anticipated">July 30, 2026</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Area under curve.</measure>
      <time_frame>At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.</time_frame>
      <description>Receiver operating characteristic curve analysis.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>The number of cases</measure>
      <time_frame>At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.</time_frame>
      <description>The faculty responsible for the training program assessed the skills of each resident.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>The cases time.</measure>
      <time_frame>At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.</time_frame>
      <description>The faculty responsible for the training program assessed the skills of each resident.</description>
    </primary_outcome>
    <number_of_groups>3</number_of_groups>
    <enrollment type="Anticipated">1000</enrollment>
    <condition>Thyroid Nodule</condition>
    <arm_group>
      <arm_group_label>Training set</arm_group_label>
      <description>Patients with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2018 and December 2020 in Sun Yat-sen Memorial Hospital Sun Yat-sen University.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Internal test set</arm_group_label>
      <description>Patients with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2021 and May 2023 in Sun Yat-sen Memorial Hospital Sun Yat-sen University.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>External test set</arm_group_label>
      <description>Patients with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2022 and June 2023 in Houjie Hospital of Dongguan and Central People's Hospital of Zhanjiang.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Artificial Intelligent</intervention_name>
      <description>Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.</description>
      <arm_group_label>External test set</arm_group_label>
      <arm_group_label>Internal test set</arm_group_label>
      <arm_group_label>Training set</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Study population with the same indication for contrast-enhanced ultrasound and fine-needle&#xD;
          aspiration biopsy:&#xD;
  &#xD;
            -  Planned ablation or surgery;&#xD;
  &#xD;
            -  At least one suspicious ultrasound feature, such as hypoechoic/very hypoechoic,&#xD;
               irregular/lobulated margin, taller than wide, or punctate echogenic foci.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients with thyroid nodules with a solid component ≥5 mm confirmed by conventional&#xD;
               ultrasound;&#xD;
  &#xD;
            -  Patients who underwent conventional ultrasound, contrast-enhanced ultrasound, and&#xD;
               fine-needle aspiration biopsy;&#xD;
  &#xD;
            -  Patients with a final benign or malignant pathological results.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients with cytopathology of Bethesda I, III, or IV and without final benign or&#xD;
               malignant pathology；&#xD;
  &#xD;
            -  Patients with a history of thyroid ablation or surgery;&#xD;
  &#xD;
            -  Patients with low-quality ultrasound images.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
    </eligibility>
    <overall_official>
      <last_name>Jingliang Ruan, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Sun Yat-sen Memorial Hospital,Sun Yat-sen University</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Jingliang Ruan, PhD</last_name>
      <phone>+8613694202230</phone>
      <email>ruanjl3@mail.sysu.edu.cn</email>
    </overall_contact>
    <location>
      <facility>
        <name>Sun Yat-sen Memorial Hospital, Sun Yat-sen University</name>
        <address>
          <city>Guangzhou</city>
          <state>Guangdong</state>
          <zip>510289</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Jingliang Ruan, PhD</last_name>
        <phone>+8613694202230</phone>
        <email>ruanjl3@mail.sysu.edu.cn</email>
      </contact>
      <investigator>
        <last_name>Jingliang Ruan, PhD</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <reference>
      <citation>Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, Pacini F, Randolph GW, Sawka AM, Schlumberger M, Schuff KG, Sherman SI, Sosa JA, Steward DL, Tuttle RM, Wartofsky L. 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. Thyroid. 2016 Jan;26(1):1-133. doi: 10.1089/thy.2015.0020.</citation>
      <PMID>26462967</PMID>
    </reference>
    <reference>
      <citation>Burgos N, Ospina NS, Sipos JA. The Future of Thyroid Nodule Risk Stratification. Endocrinol Metab Clin North Am. 2022 Jun;51(2):305-321. doi: 10.1016/j.ecl.2021.12.002. Epub 2022 May 4.</citation>
      <PMID>35662443</PMID>
    </reference>
    <reference>
      <citation>Zhou J, Yin L, Wei X, Zhang S, Song Y, Luo B, Li J, Qian L, Cui L, Chen W, Wen C, Peng Y, Chen Q, Lu M, Chen M, Wu R, Zhou W, Xue E, Li Y, Yang L, Mi C, Zhang R, Wu G, Du G, Huang D, Zhan W; Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of the Chinese Medical Association; Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. 2020 Chinese guidelines for ultrasound malignancy risk stratification of thyroid nodules: the C-TIRADS. Endocrine. 2020 Nov;70(2):256-279. doi: 10.1007/s12020-020-02441-y. Epub 2020 Aug 21.</citation>
      <PMID>32827126</PMID>
    </reference>
    <reference>
      <citation>Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, Cronan JJ, Beland MD, Desser TS, Frates MC, Hammers LW, Hamper UM, Langer JE, Reading CC, Scoutt LM, Stavros AT. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017 May;14(5):587-595. doi: 10.1016/j.jacr.2017.01.046. Epub 2017 Apr 2.</citation>
      <PMID>28372962</PMID>
    </reference>
    <reference>
      <citation>Russ G, Bonnema SJ, Erdogan MF, Durante C, Ngu R, Leenhardt L. European Thyroid Association Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults: The EU-TIRADS. Eur Thyroid J. 2017 Sep;6(5):225-237. doi: 10.1159/000478927. Epub 2017 Aug 8.</citation>
      <PMID>29167761</PMID>
    </reference>
    <reference>
      <citation>Shin JH, Baek JH, Chung J, Ha EJ, Kim JH, Lee YH, Lim HK, Moon WJ, Na DG, Park JS, Choi YJ, Hahn SY, Jeon SJ, Jung SL, Kim DW, Kim EK, Kwak JY, Lee CY, Lee HJ, Lee JH, Lee JH, Lee KH, Park SW, Sung JY; Korean Society of Thyroid Radiology (KSThR) and Korean Society of Radiology. Ultrasonography Diagnosis and Imaging-Based Management of Thyroid Nodules: Revised Korean Society of Thyroid Radiology Consensus Statement and Recommendations. Korean J Radiol. 2016 May-Jun;17(3):370-95. doi: 10.3348/kjr.2016.17.3.370. Epub 2016 Apr 14.</citation>
      <PMID>27134526</PMID>
    </reference>
    <reference>
      <citation>Kwak JY, Han KH, Yoon JH, Moon HJ, Son EJ, Park SH, Jung HK, Choi JS, Kim BM, Kim EK. Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology. 2011 Sep;260(3):892-9. doi: 10.1148/radiol.11110206. Epub 2011 Jul 19.</citation>
      <PMID>21771959</PMID>
    </reference>
    <reference>
      <citation>Burgos N, Zhao J, Brito JP, Hoang JK, Pitoia F, Maraka S, Castro MR, Lee JH, Singh Ospina N. Clinician Agreement on the Classification of Thyroid Nodules Ultrasound Features: A Survey of 2 Endocrine Societies. J Clin Endocrinol Metab. 2022 Jul 14;107(8):e3288-e3294. doi: 10.1210/clinem/dgac279.</citation>
      <PMID>35521676</PMID>
    </reference>
    <reference>
      <citation>Sidhu PS, Cantisani V, Dietrich CF, Gilja OH, Saftoiu A, Bartels E, Bertolotto M, Calliada F, Clevert DA, Cosgrove D, Deganello A, D'Onofrio M, Drudi FM, Freeman S, Harvey C, Jenssen C, Jung EM, Klauser AS, Lassau N, Meloni MF, Leen E, Nicolau C, Nolsoe C, Piscaglia F, Prada F, Prosch H, Radzina M, Savelli L, Weskott HP, Wijkstra H. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version). Ultraschall Med. 2018 Apr;39(2):e2-e44. doi: 10.1055/a-0586-1107. Epub 2018 Mar 6.</citation>
      <PMID>29510439</PMID>
    </reference>
    <reference>
      <citation>Zhang Y, Zhou P, Tian SM, Zhao YF, Li JL, Li L. Usefulness of combined use of contrast-enhanced ultrasound and TI-RADS classification for the differentiation of benign from malignant lesions of thyroid nodules. Eur Radiol. 2017 Apr;27(4):1527-1536. doi: 10.1007/s00330-016-4508-y. Epub 2016 Aug 15.</citation>
      <PMID>27525973</PMID>
    </reference>
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      <citation>Tang C, Fang K, Guo Y, Li R, Fan X, Chen P, Chen Z, Liu Q, Zou Y. Safety of Sulfur Hexafluoride Microbubbles in Sonography of Abdominal and Superficial Organs: Retrospective Analysis of 30,222 Cases. J Ultrasound Med. 2017 Mar;36(3):531-538. doi: 10.7863/ultra.15.11075. Epub 2017 Jan 10.</citation>
      <PMID>28072475</PMID>
    </reference>
    <reference>
      <citation>Seifert P, Gorges R, Zimny M, Kreissl MC, Schenke S. Interobserver agreement and efficacy of consensus reading in Kwak-, EU-, and ACR-thyroid imaging recording and data systems and ATA guidelines for the ultrasound risk stratification of thyroid nodules. Endocrine. 2020 Jan;67(1):143-154. doi: 10.1007/s12020-019-02134-1. Epub 2019 Nov 18.</citation>
      <PMID>31741167</PMID>
    </reference>
    <reference>
      <citation>Wildman-Tobriner B, Buda M, Hoang JK, Middleton WD, Thayer D, Short RG, Tessler FN, Mazurowski MA. Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. Radiology. 2019 Jul;292(1):112-119. doi: 10.1148/radiol.2019182128. Epub 2019 May 21.</citation>
      <PMID>31112088</PMID>
    </reference>
    <reference>
      <citation>Zhang B, Tian J, Pei S, Chen Y, He X, Dong Y, Zhang L, Mo X, Huang W, Cong S, Zhang S. Machine Learning-Assisted System for Thyroid Nodule Diagnosis. Thyroid. 2019 Jun;29(6):858-867. doi: 10.1089/thy.2018.0380. Epub 2019 Apr 27.</citation>
      <PMID>30929637</PMID>
    </reference>
    <reference>
      <citation>Peng S, Liu Y, Lv W, Liu L, Zhou Q, Yang H, Ren J, Liu G, Wang X, Zhang X, Du Q, Nie F, Huang G, Guo Y, Li J, Liang J, Hu H, Xiao H, Liu Z, Lai F, Zheng Q, Wang H, Li Y, Alexander EK, Wang W, Xiao H. Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study. Lancet Digit Health. 2021 Apr;3(4):e250-e259. doi: 10.1016/S2589-7500(21)00041-8. Erratum In: Lancet Digit Health. 2021 Jul;3(7):e413.</citation>
      <PMID>33766289</PMID>
    </reference>
    <reference>
      <citation>Jin Z, Pei S, Ouyang L, Zhang L, Mo X, Chen Q, You J, Chen L, Zhang B, Zhang S. Thy-Wise: An interpretable machine learning model for the evaluation of thyroid nodules. Int J Cancer. 2022 Dec 15;151(12):2229-2243. doi: 10.1002/ijc.34248. Epub 2022 Sep 12.</citation>
      <PMID>36095154</PMID>
    </reference>
    <reference>
      <citation>Chen Y, Gao Z, He Y, Mai W, Li J, Zhou M, Li S, Yi W, Wu S, Bai T, Zhang N, Zeng W, Lu Y, Liu H. An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules. Radiology. 2022 Jun;303(3):613-619. doi: 10.1148/radiol.211455. Epub 2022 Mar 22.</citation>
      <PMID>35315719</PMID>
    </reference>
    <reference>
      <citation>Zhao CK, Ren TT, Yin YF, Shi H, Wang HX, Zhou BY, Wang XR, Li X, Zhang YF, Liu C, Xu HX. A Comparative Analysis of Two Machine Learning-Based Diagnostic Patterns with Thyroid Imaging Reporting and Data System for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate. Thyroid. 2021 Mar;31(3):470-481. doi: 10.1089/thy.2020.0305. Epub 2020 Sep 9.</citation>
      <PMID>32781915</PMID>
    </reference>
    <reference>
      <citation>Zhao J, Zhou X, Shi G, Xiao N, Song K, Zhao J, Hao R, Li K. Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification. Appl Intell (Dordr). 2022;52(9):10369-10383. doi: 10.1007/s10489-021-03025-7. Epub 2022 Jan 13.</citation>
      <PMID>35039715</PMID>
    </reference>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>July 24, 2023</study_first_submitted>
    <study_first_submitted_qc>August 8, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 9, 2023</study_first_posted>
    <last_update_submitted>August 8, 2023</last_update_submitted>
    <last_update_submitted_qc>August 8, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 9, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Ultrasonography</keyword>
    <keyword>Contrast Media</keyword>
    <keyword>Deep Learning</keyword>
    <keyword>Thyroid Nodule</keyword>
    <keyword>Learning Curve</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Thyroid Nodule</mesh_term>
      <mesh_term>Thyroid Diseases</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT05982821
  org study id: SYSKY-2023-702-01
  nct id: NCT05982821
  lead sponsor: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  The goal of this observational study is to learn about the learning curve for mastering the
        thyroid imaging reporting and data system of contrast-enhanced ultrasound with the assistance
        of artificial intelligence in patients with thyroid nodules. The main questions it aims to
        answer are:
  
          1. Can we develop a artificial intelligent software to assist doctors in the diagnosis of
             thyroid nodules using contrast-enhanced ultrasound?
  
          2. Can artificial intelligent reduce the number of cases and time for doctors to master the
             contrast-enhanced ultrasound diagnosis of thyroid nodules?
  
        Participants will be asked to undergo contrast-enhanced ultrasound examination and
        ultrasound-guided fine-needle aspiration of thyroid nodules. Researchers will compare the
        number of cases and time for doctors with and without artificial intelligent assistance to
        master the contrast-enhanced ultrasound diagnosis of thyroid nodules to see if artificial
        intelligent reduce the number of cases and time.
  observational model: Cohort
  time perspective: Prospective
  measure: Area under curve.
  time frame: At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.
  description: Receiver operating characteristic curve analysis.
  measure: The number of cases
  time frame: At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.
  description: The faculty responsible for the training program assessed the skills of each resident.
  measure: The cases time.
  time frame: At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.
  description: The faculty responsible for the training program assessed the skills of each resident.
  arm group label: Training set
  description: Patients with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2018 and December 2020 in Sun Yat-sen Memorial Hospital Sun Yat-sen University.
  arm group label: Internal test set
  description: Patients with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2021 and May 2023 in Sun Yat-sen Memorial Hospital Sun Yat-sen University.
  arm group label: External test set
  description: Patients with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2022 and June 2023 in Houjie Hospital of Dongguan and Central People's Hospital of Zhanjiang.
  intervention type: Other
  intervention name: Artificial Intelligent
  description: Artificial intelligence assisted radiologists to extract ultrasound features of thyroid nodules.
  arm group label: External test set
  arm group label: Internal test set
  arm group label: Training set
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  last name: Jingliang Ruan, PhD
  role: Principal Investigator
  affiliation: Sun Yat-sen Memorial Hospital,Sun Yat-sen University
  last name: Jingliang Ruan, PhD
  phone: +8613694202230
  email: ruanjl3@mail.sysu.edu.cn
  facility: 
  status: Recruiting
  contact: 
  investigator: 
  country: China
  citation: Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, Pacini F, Randolph GW, Sawka AM, Schlumberger M, Schuff KG, Sherman SI, Sosa JA, Steward DL, Tuttle RM, Wartofsky L. 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. Thyroid. 2016 Jan;26(1):1-133. doi: 10.1089/thy.2015.0020.
  PMID: 26462967
  citation: Burgos N, Ospina NS, Sipos JA. The Future of Thyroid Nodule Risk Stratification. Endocrinol Metab Clin North Am. 2022 Jun;51(2):305-321. doi: 10.1016/j.ecl.2021.12.002. Epub 2022 May 4.
  PMID: 35662443
  citation: Zhou J, Yin L, Wei X, Zhang S, Song Y, Luo B, Li J, Qian L, Cui L, Chen W, Wen C, Peng Y, Chen Q, Lu M, Chen M, Wu R, Zhou W, Xue E, Li Y, Yang L, Mi C, Zhang R, Wu G, Du G, Huang D, Zhan W; Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of the Chinese Medical Association; Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. 2020 Chinese guidelines for ultrasound malignancy risk stratification of thyroid nodules: the C-TIRADS. Endocrine. 2020 Nov;70(2):256-279. doi: 10.1007/s12020-020-02441-y. Epub 2020 Aug 21.
  PMID: 32827126
  citation: Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, Cronan JJ, Beland MD, Desser TS, Frates MC, Hammers LW, Hamper UM, Langer JE, Reading CC, Scoutt LM, Stavros AT. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017 May;14(5):587-595. doi: 10.1016/j.jacr.2017.01.046. Epub 2017 Apr 2.
  PMID: 28372962
  citation: Russ G, Bonnema SJ, Erdogan MF, Durante C, Ngu R, Leenhardt L. European Thyroid Association Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults: The EU-TIRADS. Eur Thyroid J. 2017 Sep;6(5):225-237. doi: 10.1159/000478927. Epub 2017 Aug 8.
  PMID: 29167761
  citation: Shin JH, Baek JH, Chung J, Ha EJ, Kim JH, Lee YH, Lim HK, Moon WJ, Na DG, Park JS, Choi YJ, Hahn SY, Jeon SJ, Jung SL, Kim DW, Kim EK, Kwak JY, Lee CY, Lee HJ, Lee JH, Lee JH, Lee KH, Park SW, Sung JY; Korean Society of Thyroid Radiology (KSThR) and Korean Society of Radiology. Ultrasonography Diagnosis and Imaging-Based Management of Thyroid Nodules: Revised Korean Society of Thyroid Radiology Consensus Statement and Recommendations. Korean J Radiol. 2016 May-Jun;17(3):370-95. doi: 10.3348/kjr.2016.17.3.370. Epub 2016 Apr 14.
  PMID: 27134526
  citation: Kwak JY, Han KH, Yoon JH, Moon HJ, Son EJ, Park SH, Jung HK, Choi JS, Kim BM, Kim EK. Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology. 2011 Sep;260(3):892-9. doi: 10.1148/radiol.11110206. Epub 2011 Jul 19.
  PMID: 21771959
  citation: Burgos N, Zhao J, Brito JP, Hoang JK, Pitoia F, Maraka S, Castro MR, Lee JH, Singh Ospina N. Clinician Agreement on the Classification of Thyroid Nodules Ultrasound Features: A Survey of 2 Endocrine Societies. J Clin Endocrinol Metab. 2022 Jul 14;107(8):e3288-e3294. doi: 10.1210/clinem/dgac279.
  PMID: 35521676
  citation: Sidhu PS, Cantisani V, Dietrich CF, Gilja OH, Saftoiu A, Bartels E, Bertolotto M, Calliada F, Clevert DA, Cosgrove D, Deganello A, D'Onofrio M, Drudi FM, Freeman S, Harvey C, Jenssen C, Jung EM, Klauser AS, Lassau N, Meloni MF, Leen E, Nicolau C, Nolsoe C, Piscaglia F, Prada F, Prosch H, Radzina M, Savelli L, Weskott HP, Wijkstra H. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version). Ultraschall Med. 2018 Apr;39(2):e2-e44. doi: 10.1055/a-0586-1107. Epub 2018 Mar 6.
  PMID: 29510439
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  responsible party type: Sponsor
  mesh term: Thyroid Nodule
  mesh term: Thyroid Diseases
  sharing ipd: No