Artificial Intelligence-assisted Contrast-enhanced Ultrasound Diagnosis of Thyroid Nodules

NCT05982821 | Completed

• 1 other identifier: SYSKY-2023-702-01
• Study Type: interventional
• Target: 1,955
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this intervention study is to learn about the diagnostic performance of artificial intelligence to assist the contrast-enhanced ultrasound diagnosis of thyroid nodules. The main questions it aims to answer are:

1. 1.Can we develop an artificial intelligence model to assist the contrast-enhanced ultrasound diagnosis of thyroid nodules?
2. 2.Can artificial intelligent reduce the unnecessary biopsy of thyroid nodules?

Conditions

Thyroid Nodule

Keywords

Artificial interligence; Contrast media; diagnosis; Thyroid nodule; Ultrasonography

Outcome Measures

Primary Outcomes (2)

• Diagnostic performance

  Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the curve

  First week after primary completion date.

• The necessity of biopsy

  Unnecessary biopsy rate

  First week after primary completion date.

Study Arms (1)

Test set

OTHER

Participants with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2021 and December 2022 in Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Houjie Hospital of Dongguan and Affiliated Huadu Hospital of Southern Medical University.

Other: Artificial Intelligence

Interventions

Artificial Intelligence OTHER

Artificial intelligence assisted contrast-enhanced ultrasound diagnosis of thyroid nodules.

Test set

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients with thyroid nodules with a solid component ≥5 mm confirmed by conventional ultrasound;
• Patients who underwent conventional ultrasound, contrast-enhanced ultrasound, and fine-needle aspiration biopsy;
• Patients with a final benign or malignant pathological results.

You may not qualify if:

• Patients with cytopathology of Bethesda I, III, or IV and without final benign or malignant pathology；
• Patients with a history of thyroid ablation or surgery;
• Patients with low-quality ultrasound images.

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead

Study Sites (1)

Sun Yat-sen Memorial Hospital, Sun Yat-sen University

Guangzhou, Guangdong, 510289, China

Location

Related Publications (19)

• 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 BACKGROUND

• 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 BACKGROUND

• 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.

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• 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 BACKGROUND

• 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.

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  PMID: 27134526 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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.

  PMID: 27525973 BACKGROUND

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• 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.

  PMID: 31741167 BACKGROUND

• 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.

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• 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.

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• 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.

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  PMID: 35039715 BACKGROUND

MeSH Terms

Conditions

Thyroid Nodule; Disease

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Thyroid Neoplasms; Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Head and Neck Neoplasms; Endocrine System Diseases; Thyroid Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Jingliang Ruan, PhD

  Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Masking Details: Cytopathologists, histopathologists and statistical analysts were blinded to the intervention.
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: A deep-learning based artificial intelligence model.

Study Record Dates

• First Submitted: July 24, 2023
• First Posted: August 9, 2023
• Study Start: June 3, 2023
• Primary Completion: March 2, 2026
• Study Completion: March 16, 2026
• Last Updated: March 25, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: The first week after the publication of the research papers.
• Access Criteria: Data generated or analyzed during the study are available from the principal investigator by request.

Locations

CN (1)