Development and validation of a Web-based malignancy risk-stratification system of thyroid nodules.
real-time elastography
risk stratification
thyroid nodules
ultrasonography
Journal
Clinical endocrinology
ISSN: 1365-2265
Titre abrégé: Clin Endocrinol (Oxf)
Pays: England
ID NLM: 0346653
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
16
11
2019
revised:
02
05
2020
accepted:
04
05
2020
pubmed:
21
5
2020
medline:
19
8
2021
entrez:
21
5
2020
Statut:
ppublish
Résumé
Previous publications on risk-stratification systems for malignant thyroid nodules were based on conventional ultrasound only. We aimed to develop a practical and simplified prediction model for categorizing the malignancy risk of thyroid nodules based on clinical data, biochemical data, conventional ultrasound and real-time elastography. Retrospective cohort study. A total of 2818 patients (1890 female, mean age, 45.5 ± 13.2 years) with 2850 thyroid nodules were retrospectively evaluated between April 2011 and October 2016. 26.8% nodules were malignant. We used a randomly divided sample of 80% of the nodules to perform a multivariate logistic regression analysis. Cut-points were determined to create a risk-stratification scoring system. Patients were classified as having low, moderate and high probability of malignancy according to their scores. We validated the models to the remaining 20% of the nodules. The area under the curve (AUC) was used to evaluate the discrimination ability of the systems. Ten variables were selected as predictors of malignancy. The point-based scoring systems with and without elasticity score achieved similar AUCs of 0.916 (95% confidence interval [CI]: 0.885-0.948) and 0.906 (95% CI: 0.872-0.941) when validated. Malignancy risk was segmented from 0% to 100.0% and was positively associated with an increase in risk scores. We then developed a Web-based risk-stratification system of thyroid nodules (http: thynodscore.com). A simple and reliable Web-based risk-stratification system could be practically used in stratifying the risk of malignancy in thyroid nodules.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
729-738Informations de copyright
© 2020 John Wiley & Sons Ltd.
Références
Ahn HS, Kim HJ, Welch HG. Korea's thyroid-cancer "epidemic"-screening and overdiagnosis. N Engl J Med. 2014;371:1765-1767.
Chi J, Walia E, Babyn P, et al. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging. 2017;30:477-486.
Gharib H, Papini E, Paschke R, et al. American Association of Clinical Endocrinologists, Associazione Medici Endocrinologi, and European Thyroid Association medical guidelines for clinical practice for the diagnosis and management of thyroid nodules: executive summary of recommendations. J Endocrinol Invest. 2010;33:51-56.
Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74:2913-2921.
Burman KD, Wartofsky L. Clinical practice. Thyroid nodules. N Engl J Med. 2015;373:2347-2356.
Remonti LR, Kramer CK, Leitao CB, Pinto LC, Gross JL. Thyroid ultrasound features and risk of carcinoma: a systematic review and meta-analysis of observational studies. Thyroid. 2015;25:538-550.
Ha EJ, Baek JH, Na DG, et al. Risk stratification of thyroid nodules on ultrasonography: current status and perspectives. Thyroid. 2017;7(12):1463-1468.
Haugen Bryan R, Alexander Erik K, Bible Keith C, et al. 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;26:1-133.
Grant EG, Tessler FN, Hoang JK, et al. Thyroid ultrasound reporting lexicon: White Paper of the ACR Thyroid Imaging, Reporting and Data System (TIRADS) Committee. J Am Coll Radiol. 2015;12(12 Pt A):1272-1279.
Park JY, Lee HJ, Jang HW, et al. A proposal for a thyroid imaging reporting and data system for ultrasound features of thyroid carcinoma. Thyroid. 2009;19(11):1257-1264.
Horvath E, Majlis S, Rossi R, et al. An ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management. J Clin Endocrinol Metab. 2009;94(5):1748-1751.
Kwak JY, Han KH, Yoon JH, et al. Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology. 2011;260(3):892-899.
Cheng SP, Lee JJ, Lin JL, et al. Characterization of thyroid nodules using the proposed thyroid imaging reporting and data system (TI-RADS). Head Neck. 2013;35(4):541-547.
Russ G, Royer B, Bigorgne C, et al. Prospective evaluation of thyroid imaging reporting and data system on 4550 nodules with and without elastography. Eur J Endocrinol. 2013;168:649-655.
Na DG, Baek JH, Sung JY, et al. Thyroid imaging reporting and data system risk stratification of thyroid nodules: categorization based on solidity and echogenicity. Thyroid. 2016;26(4):562-572.
Shin JH, Baek JH, Chung J, et al. Ultrasonography Revised Korean Society of Thyroid Radiology Consensus Statement and Recommendations. Korean J Radiol. 2016;17:370-395.
Tessler FN, Middleton WD, Grant EG, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017;14(5):587-595.
Moon HJ, Sung JM, Kim EK, Yoon JH, Youk JH, Kwak JY. Diagnostic performance of gray-scale US and elastography in solid thyroid nodules. Radiology. 2012;262:1002-1013.
Ruhlmann M, Stebner V, Gorges R, et al. Diagnosis of hyperfunctional thyroid nodules: impact of US-elastography. Nuklearmedizin. 2014;53:173-177.
Wells PS, Anderson DR, Rodger M, et al. Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thromb. Haemost. 2000;83:416-420.
Sullivan LM, Massaro JM, D'Agostino RS. Presentation of multivariate data for clinical use: The Framingham Study risk score functions. Stat Med. 2004;23:1631-1660.
Al Dawish MA, Alwin Robert A, Thabet MA, et al. Thyroid nodule management: thyroid-stimulating hormone, ultrasound, and cytological classification system for predicting malignancy. Cancer Inform. 2018;17:1176935118765132.
Boi F, Minerba L, Lai ML, et al. Both thyroid autoimmunity and increased serum TSH are independent risk factors for malignancy in patients with thyroid nodules. J Endocrinol Invest. 2013;36(5):313-320.
Huan Q, Wang K, Lou F, et al. Epidemiological characteristics of thyroid nodules and risk factors for malignant nodules: a retrospective study from 6,304 surgical cases. Chin Med J (Engl). 2014;127:2286-2292.
Castro MR, Espiritu RP, Bahn RS, et al. Predictors of malignancy in patients with cytologically suspicious thyroid nodules. Thyroid. 2011;21:1191-1198.
Hong EK, Choi YH, Cheon JE, Kim WS, Kim IO, Kang SY. Accurate measurements of liver stiffness using shear wave elastography in children and young adults and the role of the stability index. Ultrasonography. 2018;37:226-232.
Youk JH, Son EJ, Han K, Gweon HM, Kim JA. Performance of shear-wave elastography for breast masses using different region-of-interest (ROI) settings. Acta Radiol. 2018;59(7):789-797.
Mancini M, Salomone MA, Ragucci M, et al. Reproducibility of shear wave elastography (SWE) in patients with chronic liver disease. PLoS One. 2017;12:e185391.
Bardet S, Ciappuccini R, Pellot-Barakat C, et al. Shear wave elastography in thyroid nodules with indeterminate cytology: results of a prospective bicentric study. Thyroid. 2017;27:1441-1449.
Wang Y, Dan HJ, Dan HY, Li T, Hu B. Differential diagnosis of small single solid thyroid nodules using real-time ultrasound elastography. J Int Med Res. 2010;38:466-472.
Cantisani V, Lodise P, Grazhdani H, et al. Ultrasound elastography in the evaluation of thyroid pathology. Current status. Eur J Radiol. 2014;83:420-428.
Trimboli P, Guglielmi R, Monti S, et al. Ultrasound sensitivity for thyroid malignancy is increased by real-time elastography: a prospective multicenter study. J Clin Endocrinol Metab. 2012;97:4524-4530.
Ragazzoni F, Deandrea M, Mormile A, et al. High diagnostic accuracy and interobserver reliability of real-time elastography in the evaluation of thyroid nodules. Ultrasound Med Biol. 2012;38:1154-1162.
Ha SM, Ahn HS, Baek JH, et al. Validation of three scoring risk-stratification models for thyroid nodules. Thyroid. 2017;27:1550-1557.
Kwak JY, Jung I, Baek JH, et al. Image reporting and characterization system for ultrasound features of thyroid nodules: multicentric Korean retrospective study. Korean J Radiol. 2013;14:110-117.
Choi YJ, Baek JH, Baek SH, et al. Web-based malignancy risk estimation for thyroid nodules using ultrasonography characteristics: development and validation of a predictive model. Thyroid. 2015;25:1306-1312.
Zhang B, Tian J, Pei S, et al. Machine learning-assisted system for thyroid nodule diagnosis. Thyroid. 2019;29:858-867.