Clinical trial NCT05982821: Artificial Intelligent Accelerates the Learning Curve for Mastering Contrast-enhanced Ultrasound of Thyroid Nodules
Official title: Artificial Intelligent Accelerates the Learning Curve for Mastering Thyroid Imaging Reporting and Data System of Contrast-enhanced Ultrasound
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Thyroid Nodule
Keywords: Ultrasonography
Contrast Media
Deep Learning
Thyroid Nodule
Learning Curve
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
First posted: 2023-08-09
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Study population with the same indication for contrast-enhanced ultrasound and fine-needle aspiration biopsy:

* Planned ablation or surgery;
* At least one suspicious ultrasound feature, such as hypoechoic/very hypoechoic, irregular/lobulated margin, taller than wide, or punctate echogenic foci.

Brief summary: 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.


Eligibility criteria: Inclusion Criteria:

* 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.

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Area under curve.
timeFrame: At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.
measure: The number of cases
timeFrame: At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.
measure: The cases time.
timeFrame: At the end of the first (M1), third (M3), and sixth (M6) months of the trainees' rotation.
Secondary outcomes:
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Sun Yat-sen Memorial Hospital, Sun Yat-sen University
state: Guangdong
Study officials:
affiliation: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
name: Jingliang Ruan, PhD
role: PRINCIPAL_INVESTIGATOR
References:
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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.
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: 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: 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: 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.