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Clinical value of grayscale ultrasound combined with real-time shear wave elastography nomogram in risk prediction of thyroid cancer

BMC Medical Imaging volume 23, Article number: 123 (2023) Cite this article

Abstract

Objectives

This study constructed a nomogram based on grayscale ultrasound features and real-time shear wave elastography (SWE) parameters to predict thyroid cancer.

Methods

Clinical data of 217 thyroid nodules of 201 patients who underwent grayscale ultrasound, real-time SWE, and thyroid function laboratory examination in Ma’anshan People’s Hospital from January 2019 to December 2020 were retrospectively analyzed. The subjects were divided into a benign nodule group (106 nodules) and a malignant nodule group (111 nodules). The differences in grayscale ultrasound features, quantitative parameters of real-time SWE, and laboratory results of thyroid function between benign and malignant thyroid nodules were analyzed. We used a chi-square test for categorical variables and a t-test for continuous variables. Then, the independent risk factors for thyroid cancer were analyzed using multivariate logistic regression. Based on the independent risk factors, a nomogram for predicting thyroid cancer risk was constructed using the RMS package of the R software.

Results

Multivariate logistic regression showed that the grayscale ultrasound features of thyroid nodules were the shape, margin, echogenicity, and echogenic foci of the nodules,the maximum Young’s modulus (SWE-max) of thyroid nodules, and the ratio of thyroid nodule and peripheral gland (SWE-ratio) measured by real-time SWE were independent risk factors for thyroid cancer (all p < 0.05), and the other variables had no statistical difference (p > 0.05). Based on the shape (OR = 5.160, 95% CI: 2.252–11.825), the margin (OR = 9.647, 95% CI: 2.048–45.443), the echogenicity (OR = 6.512, 95% CI: 1.729–24.524), the echogenic foci (OR = 2.049, 95% CI: 1.118–3.756), and the maximum Young’s modulus (SWE-max) (OR = 1.296, 95% CI: 1.140–1.473), the SWE-ratio (OR = 2.001, 95% CI: 1.403–2.854) of the thyroid nodule to peripheral gland was used to establish the related nomogram prediction model. The bootstrap self-sampling method was used to verify the model. The consistency index (C-index) was 0.979, ROC curve was used to analyze the nomogram scores of all patients, and the AUC of nomogram prediction of thyroid cancer was 0.976, indicating that the nomogram model had high accuracy in the risk prediction of thyroid cancer.

Conclusions

The nomogram model of grayscale ultrasound features combined with SWE parameters can accurately predict thyroid cancer.

Peer Review reports

Introduction

Thyroid nodules are a common disease of the endocrine system [1]. Thyroid cancer accounts for only 5-15% of thyroid nodules, but the incidence rate is increasing by 4-6.2% every year [2, 3]. Ultrasound (US) is the preferred imaging method for the diagnosis of thyroid nodules. The grayscale acoustic characteristics of thyroid nodules could be used to characterize thyroid nodules. Scholars from different countries have established their own hierarchical risk assessment system (Thyroid Imaging Reporting and Data System, TI-RADS) according to the grayscale US characteristics of thyroid nodules [4,5,6,7,8,9,10]. Despite the widespread use of TI-RADS, the interpretation of thyroid images remains inconsistent, as over 10 different TI-RADS systems are currently used across Chinese hospitals [11]. Additionally, the sensitivity and specificity of these systems in detecting thyroid cancer vary widely, making it challenging to compare data between hospitals.

Shear wave elastography (SWE) is a non-invasive medical imaging technique used to evaluate tissue elasticity by measuring the propagation speed of high-frequency ultrasound waves within tissues. This new imaging technology has improved the detection of various cancer, particularly thyroid cancer [12,13,14,15]. In addition, some studies have found a correlation between the extent of the disease on SWE and the serum thyroid-stimulating hormone (TSH) concentrations. However, these studies had conflicting results, and it remains unclear whether TSH promotes or protects against thyroid cancer [16, 17].

In order to address these challenges, we developed a novel machine-learning algorithm to identify the most relevant risk factors for thyroid cancer based on US grayscale characteristics of the nodules, two-dimensional real-time SWE measurements, and patient serum indices. In addition, we also created a nomogram to facilitate the implementation of the machine learning model clinically. Nomograms are statistical models based on logistic regression or Cox regression that can quantitatively and graphically represent prediction indicators and provide a more intuitive evaluation of clinical events [18,19,20,21]. This innovative approach could potentially provide a more accurate individualized risk assessment for thyroid nodules than the traditional TI-RADS system.

Materials and methods

Study population

We retrospectively analyzed the clinical data of 217 thyroid nodules in 201 patients who were subjected to grayscale US, SWE, and laboratory examination in Maanshan people’s Hospital from January 2019 to December 2020. The inclusion criterion was thyroid nodules confirmed by pathology during operation or cytological puncture. The exclusion criteria were nodules without surgery, poor or unsatisfactory cytological diagnosis of nodules, and atypical or unclear follicular lesions.

Grayscale US and SWE examinations

The grayscale US and SWE examinations were performed with the same sonography machine (Aixplorer; Supersonic Imagine, Paris, France) with a 5–14 MHz high-frequency linear transducer. The size of thyroid nodules was measured from longitudinal and transverse sections, and the shape of thyroid nodules was evaluated from transverse sections. The shape, margin, echogenicity, echogenic foci, and composition of thyroid nodules were observed and recorded. The shape was divided into wider than tall and taller than wide. The margin was divided into smooth, ill-defined, lobulated or irregular, and extrathyroidal extension. The echogenicity of the solid portion was classified as hyperechogenicity or isoechogenicity, hypoechogenicity, or very hypoechogenicity. The hyperechoic foci were divided into non-calcification or large comet-tail artifacts, macrocalcifications, peripheral (rim) calcifications, punctate echogenic foci, or mixed calcifications. The internal compositions were categorized as cystic or almost completely cystic, spongiform, mixed cystic and solid, and solid or almost completely solid.

SWE was used to examine the thyroid nodules. The maximum value (SWE-max), minimum value (SWE-min), mean value (SWE-mean), and the ratio of nodules to glands (SWE-ratio) were measured from longitudinal sections. During the measurements, the diameter of the Q-box was 2 mm, and the unit was kPa. The average of each value was calculated from measurements taken three times.

All images are collected and read by two radiologists with more than 10 years of experience.

Laboratory examination

The thyroid-related biochemical indexes were measured using a DPP-800 biochemical automatic analyzer (Roche, Germany). The free triiodothyronine (FT3), serum-free thyroxine (FT4), total triiodoethyl-3 (T3), total thyroxine-4 (T4), and TSH were also measured.

Statistical analysis

The R software (v3.6.1) was used for statistical analyses. Chi-square tests were used for categorical variables, and t-tests were used for numerical variables. Multivariate logistic regression was used to analyze the influencing factors in thyroid nodule malignant incidence. The independent risk factors were analyzed by R software’s RMS package, and the nomogram of thyroid cancer risk was constructed. The test level was 0.05, and p < 0.05 meant that the difference was statistically significant.

Diagnostic performance and validation of the nomogram

In the nomogram, a vertical line was made on the score axis at the top of the coordinate axis of the independent variable to obtain the corresponding score; the score of each independent variable was summed up to obtain the total score. The bootstrap self-sampling method was used to evaluate the results of the nomogram, and the consistency index (C-index) was obtained: C-index = 0.50–0.70 was low accuracy, 0.70–0.90 was moderate accuracy, and > 0.90 was high accuracy.

Results

Comparison of baseline data, US parameters, and laboratory tests for the prediction of thyroid malignancy

The results showed 28 males and 78 females with a mean age ± standard deviation (SD) of 51. 90 ± 10.13 years (range: 18–71 years) with benign nodules. There were 34 males and 77 females with a mean age ± SD of 44.28 ± 46 years (range: 21–65 years) with malignant nodules. The mean nodule size was 17.9 ± 1.16 mm (range: 3.8-69.9 mm).There were significant differences in the shape, margin, echogenicity of the solid portion, hyperechoic foci, internal composition of grayscale US nodule features, SWE-max, SWE-min, SWE-mean, and SWE-ratio between benign and malignant nodules (p < 0.05). There was no significant difference in other variables between the two groups (p > 0.05) (Table 1).

Table 1 Comparison of baseline data, US parameters, and laboratory tests for the prediction of thyroid malignancy
Full size table

Analysis of influencing factors of thyroid cancer

The indexes with statistical significance in Table 1 were taken as independent variables and analyzed by multivariate logistic regression. The results showed that the shape, margin, echogenicity, echogenic foci, and elastic measurement values (SWE-max and SWE-ratio) were independent risk factors for thyroid cancer (Table 2).

Table 2 The shape, margin, echogenicity, echogenic foci, and elastic measurement values (SWE-max and SWE-ratio) were independent risk factors for thyroid cancer
Full size table

Nomogram for predicting the risk of thyroid cancer

The nomogram was constructed with shape, margin, echogenicity, echogenic foci, and the elastic measurement values (SWE-max and SWE-ratio) as dependent variables. After bootstrap self-sampling, the C-index was 0.979, with high accuracy. In clinical application, the possible risk of thyroid cancer can be predicted using the nomogram. See Figs. 1 and 2.

Fig. 1
figure 1

The nomogram was constructed with the shape, margin, echogenicity, and echogenic foci of thyroid nodule and the elastic measurement values SWE-max and SWE-ratio as dependent variables. Shape (1: wider-than-tall; 2: taller-than-wide), margin (1: smooth, 2: ill-defined, 3: lobulated or irregular, 4: extrathyroidal extension), echogenicity (2: isoechoic, 3: hypoechoic, 4: very hypoechoic), echogenic foci (1: non-calcification or large comet-tail artifacts, 2: macrocalcifications, 3: peripheral (rim) calcifications, 4: punctate echogenic foci). To use, find the corresponding value in the ‘Total points’ score, and make a vertical line down to the ‘Risk of thyroid malignancy’ to determine the risk of thyroid nodules

Full size image
Fig. 2
figure 2

ROC curve was used to analyze the nomogram scores of all patients, and the AUC of nomogram prediction of thyroid cancer was 0.976

Full size image

An example of the proposed nomogram in use for the risk of thyroid malignancy

For example, patient one had a nodule with irregular margins, wider-than-tall, hypoechoic, and punctate echogenic foci (Fig. 3a). In addition, the SWE-Max value was 132.5 kPa, and the SWE-ratio was 5.0 (Fig. 3b). The proposed nomogram value was calculated at more than 99% (Fig. 3c). Pathology proves that it is a papillary thyroid carcinoma (Fig. 3d, h&e, ×40). Patient two had a nodule with ill-defined margins, wider-than-tall shape, hypoechoic, and punctate echogenic foci (Fig. 3e). In addition, the SWE-max value was 32.2 kPa, and the SWE-ratio was 1.9 (Fig. 3f). The proposed nomogram showed a risk of less than 12%. (Fig. 3g). Pathology confirmed it was a nodular goiter with fibrosis (Fig. 3h, h & e, ×40).

Fig. 3
figure 3

Example of the nomogram in use. (a), (b) Patient one with thyroid nodule (white arrow) with irregular margin, wider-than-tall shape, hypoechoic, punctate echogenic foci, SWE-Max of 132.5 kPa, and SWE-ratio of 5.0. (c) Risk of malignancy calculated by nomogram was more than 90%. (d, ×40) Pathological result confirms papillary thyroid carcinoma. (e),(f) Patient two with thyroid nodule (white arrow) with ill-defined margin, wider-than-tall shape, hypoechoic, punctate echogenic foci, SWE-Max of 32.2 kPa, and SWE-Ratio of 1.9. (g) Risk of less than 12%. (h, ×40) Pathological result demonstrates nodular goiter with cystic degeneration

Full size image

Discussion

Current assessment tools used to assess the risk of malignancy of thyroid nodules provide inconsistent results and are prone to observer variation. In addition, these tools are based solely on imaging or clinical features, thus further limiting their prediction accuracy. Therefore, this study aimed to develop a novel nomogram that integrates grayscale US characteristics, two-dimensional real-time shear wave elastography (SWE) measurements, and patient serum indices to characterize thyroid nodules.

This study used multiple logistic regression to analyze the current mainstream academic views. The results showed that the shape, margin, echogenicity, echogenic foci in grayscale US characteristics, and the values of SWE-Max and SWE-ratio were independent risk factors for thyroid cancer. Among them, the composition in grayscale US features was not an independent risk factor, which may be related to the included cases; there were no cystic or almost completely cystic cases. At present, simple cystic nodules are considered benign and do not need treatment [22]. Only three cases of cavernous nodules had pathological results. The other four grayscale US features are consistent in differentiating thyroid nodules. According to previous relevant studies, the elevated blood TSH level in patients with thyroid nodules is associated with an increased risk of thyroid cancer [23, 24]. TSH has no significance in the diagnosis of benign and malignant thyroid nodules in this study, which may be related to the number of samples in this study. SWE imaging is a new imaging technology developed in recent years. The American Thyroid Association (ATA), the American Association of Clinical Endocrinologists (AACE), the American Society of Endocrinology (ACE), and the Italian Association of Clinical Endocrinologists (AME) have recognized this method. It is recommended as an important means to evaluate the nature of thyroid nodules. Compared with quasi-static elastic imaging and strain ratio imaging, two-dimensional real-time SWE imaging provides a color-coded map of regional elastic value, quantitatively measures tissue hardness in kPa, and improves the positive predictive value. Papillary thyroid carcinoma mainly comprises diffuse and scattered sandy calcifications and collagen in papillary proliferative thyroid epithelial cells, tissues, stromal tissues, and hardened fibrous matrices. Studies [25, 26] have confirmed that the Young’s modulus varies in different thyroid tissues; the young’s modulus of normal thyroid tissues, cysts, thyroid adenomas, nodular goiters, and thyroid papillary carcinoma increased in turn. This study found that SWE-Max and SWE-ratio were independent risk factors for predicting thyroid cancer, which was consistent with Bardet S et al.’s [27] study. The European Federation of ultrasound (EFSUM) and the World Federation of ultrasound (WFUMB) released clinical application guidelines or an expert consensus of pre- and post-elastic imaging, affirming its value in examining benign and malignant thyroid nodules.

benign and malignant thyroid nodules. These guidelines clearly show that the applications of elastic imaging in evaluating thyroid nodules cannot replace conventional US but can be used as an auxiliary approach. In this study, a nomogram was constructed by combining grayscale US features with real-time SWE imaging to predict the risk of thyroid cancer. After internal verification by bootstrap and self-sampling, the C-index was 0.979 with high accuracy; the combination of the two improves the diagnostic efficiency. Compared to traditional TI-RADS, the model developed in this study can accurately determine the risk value of thyroid nodules rather than the interval range. Compared with the nomogram model constructed using grayscale ultrasound alone by other authors [28], the nomogram model constructed by combining grayscale ultrasound and shear wave elastography in this study showed good discrimination, with an AUC of 0.976 and good calibration.

Limitations

There were some shortcomings in this study. First, this is a single-center study with a small sample size. Thus, we need to expand the sample size to improve the accuracy of the nomogram. Second, US-FNAB may lead to wrong puncture results. Third, the nomogram model passed an internal verification, but it also needs to pass an external verification to clarify the model’s abilities.

Conclusions

In conclusion, the nomogram developed in this study represents a significant step forward in the diagnosis of thyroid cancer. Our novel nomogram integrated grayscale US characteristics and SWE measurements and provided a more accurate and comprehensive risk assessment for thyroid cancer. Compared with the traditional TI-RADS system, our proposed nomogram provides a simpler tool for the risk stratification of thyroid nodules. This innovation has the potential to improve the accuracy of thyroid cancer diagnosis and could ultimately lead to better patient outcomes.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Durante C, Grani G, Lamartina L, Filetti S, Mandel SJ, Cooper DS. The diagnosis and management of thyroid nodules:a Review.JAMA2018;319(9):914–24. https://doi.org/10.1001/jama.2018.0898.

  2. Yin J, Wang C, Shao Q, Qu D, Song Z, Shan P, Zhang T, Xu J, Liang Q, Zhang S, Huang J. Relationship between the prevalence of thyroid nodules and metabolic syndrome in the iodine-adequate area of Hangzhou, China: a cross-sectional and Cohort Study. Int J Endocrinol. 2014;2014:675796. https://doi.org/10.1155/2014/675796.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Xu W, Chen Z, Li N, Liu H, Huo L, Huang Y, Jin X, Deng J, Zhu S, Zhang S, Yu Y. Relationship of anthropometric measurements to thyroid nodules in a chinese population. BMJ Open. 2015;5(12):e008452. https://doi.org/10.1136/bmjopen-2015-008452.

    Article  PubMed  PubMed Central  Google Scholar 

  4. 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;260(3):892–9. https://doi.org/10.1148/radiol.11110206.

    Article  PubMed  Google Scholar 

  5. Koc AM, Adıbelli ZH, Erkul Z, Sahin Y, Dilek I. Comparison of diagnostic accuracy of ACR-TIRADS, american thyroid association (ATA), and EU-TIRADS guidelines in detecting thyroid malignancy. Eur J Radiol. 2020;133:109390. https://doi.org/10.1016/j.ejrad.2020.109390. Epub 2020 Nov 2. PMID: 33181485.

    Article  PubMed  Google Scholar 

  6. Russ G, Bigorgne C, Royer B, Rouxel A, Bienvenu-Perrard M. Le système TIRADS en échographie thyroïdienne [The Thyroid Imaging Reporting and Data System (TIRADS) for ultrasound of the thyroid]. J Radiol. 2011 Jul-Aug;92(7–8):701 – 13. French. https://doi.org/10.1016/j.jradio.2011.03.022. Epub 2011 Jul 13. PMID: 21819912.

  7. Rahimi M, Farshchian N, Rezaee E, Shahebrahimi K, Madani H. To differentiate benign from malignant thyroid nodule comparison of sonography with FNAC findings. Pak J Med Sci. 2013;29(1):77–80. https://doi.org/10.12669/pjms.291.2595. PMID: 24353512; PMCID: PMC3809174.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Akhavan A, Jafari SM, Khosravi MH, Khajehpour H, Karimi-Sari H. Reliability of fine-needle aspiration and ultrasound-based characteristics of thyroid nodules for diagnosing malignancy in iranian patients. Diagn Cytopathol. 2016;44(4):269–73. https://doi.org/10.1002/dc.23430. Epub 2016 Jan 18. PMID: 26780293.

    Article  PubMed  Google Scholar 

  9. Grani G, Sponziello M, Pecce V, Ramundo V, Durante C. Contemporary thyroid nodule evaluation and management. J Clin Endocrinol Metab. 2020;105(9):2869–83. https://doi.org/10.1210/clinem/dgaa322. PMID: 32491169; PMCID: PMC7365695.

    Article  PubMed  PubMed Central  Google Scholar 

  10. NAL CLUB. Retrospective evaluation of Ultrasound features of thyroid nodules to assess malignancy risk: a step toward TIRADS. AJR Am J Roentgenol. 2016;207(3):460–9. https://doi.org/10.2214/AJR.15.15121. Epub 2016 Jun 28. PMID: 27352123.

    Article  Google Scholar 

  11. 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;70(2):256–79. https://doi.org/10.1007/s12020-020-02441-y. Epub 2020 Aug 21. PMID: 32827126.

    Article  CAS  PubMed  Google Scholar 

  12. Sigrist RMS, Liau J, Kaffas AE, Chammas MC, Willmann JK. Ultrasound Elastography: review of techniques and clinical applications. Theranostics. 2017;7(5):1303–29. https://doi.org/10.7150/thno.18650. PMID: 28435467; PMCID: PMC5399595.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kim H, Kim JA, Son EJ, Youk JH. Quantitative assessment of shear-wave ultrasound elastography in thyroid nodules: diagnostic performance for predicting malignancy. Eur Radiol. 2013;23(9):2532–7. https://doi.org/10.1007/s00330-013-2847-5. Epub 2013 Apr 19. PMID: 23604801.

    Article  PubMed  Google Scholar 

  14. Hang J, Li F, Qiao XH, Ye XH, Li A, Du LF. Combination of Maximum Shear Wave elasticity Modulus and TIRADS improves the diagnostic specificity in characterizing thyroid nodules: a retrospective study. Int J Endocrinol 2018 Oct 9;2018:4923050. https://doi.org/10.1155/2018/4923050. PMID: 30402095; PMCID: PMC6198550.

  15. Veyrieres JB, Albarel F, Lombard JV, Berbis J, Sebag F, Oliver C, Petit P. A threshold value in Shear Wave elastography to rule out malignant thyroid nodules: a reality? Eur J Radiol. 2012;81(12):3965–72. https://doi.org/10.1016/j.ejrad.2012.09.002. Epub 2012 Sep 30. PMID: 23031543.

    Article  PubMed  Google Scholar 

  16. Mao A, An N, Wang J, Wu Y, Wang T, Wang Z, Guan H, Wang J. Association between preoperative serum TSH and tumor status in patients with papillary thyroid microcarcinoma. Endocrine. 2021;73(3):617–24. Epub 2021 Mar 23. PMID: 33755880.

    Article  CAS  PubMed  Google Scholar 

  17. Luongo C, Morra R, Gambale C, Porcelli T, Sessa F, Matano E, Damiano V, Klain M, Schlumberger M, Salvatore D. Higher baseline TSH levels predict early hypothyroidism during cancer immunotherapy. J Endocrinol Invest. 2021 Feb 12. https://doi.org/10.1007/s40618-021-01508-5. Epub ahead of print. PMID: 33576954.

  18. Xiao J, Xiao Q, Cong W, et al. Discriminating malignancy in thyroid nodules: the Nomogram Versus the Kwak and ACR TI-RADS. Otolaryngol Head Neck Surg. 2020;163(6):1156–65. https://doi.org/10.1177/0194599820939071.

    Article  PubMed  Google Scholar 

  19. Wang J, Chen L, Nie Y, Wu W, Yao Y. Nomogram for Predicting the overall survival of patients with breast Cancer with pathologic nodal status N3. Clin Breast Cancer. 2020;20(6):e778–85. https://doi.org/10.1016/j.clbc.2020.06.002. Epub 2020 Jun 7. PMID: 32636150.

    Article  PubMed  Google Scholar 

  20. Wu J, Zhang H, Li L, Hu M, Chen L, Xu B, Song Q. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: a population-based analysis. Cancer Commun (Lond). 2020;40(7):301–12. Epub 2020 Jun 18. PMID: 32558385; PMCID: PMC7365459.

    Article  PubMed  Google Scholar 

  21. Gittleman H, Sloan AE, Barnholtz-Sloan JS. An independently validated survival nomogram for lower-grade glioma. Neuro Oncol. 2020;22(5):665–74. https://doi.org/10.1093/neuonc/noz191. PMID: 31621885; PMCID: PMC7229246.

    Article  PubMed  Google Scholar 

  22. Brito JP, Gionfriddo MR, Al Nofal A, Boehmer KR, Leppin AL, Reading C, Callstrom M, Elraiyah TA, Prokop LJ, Stan MN, Murad MH, Morris JC, Montori VM. The accuracy of thyroid nodule ultrasound to predict thyroid cancer: systematic review and meta-analysis. J Clin Endocrinol Metab. 2014;99(4):1253–63. https://doi.org/10.1210/jc.2013-2928. Epub 2013 Nov 25. PMID: 24276450; PMCID: PMC3973781.

    Article  CAS  PubMed  Google Scholar 

  23. Golbert L, de Cristo AP, Faccin CS, Farenzena M, Folgierini H, Graudenz MS, Maia AL. Serum TSH levels as a predictor of malignancy in thyroid nodules: a prospective study. PLoS ONE. 2017;12(11):e0188123. https://doi.org/10.1371/journal.pone.0188123. PMID: 29145466; PMCID: PMC5690674.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Khider MO, Ayad C, Suliman AG, Alshoabi SA, Gameraddin M, Elzaki M, Alsharif W, Arafat M, Alali A, Abu Odeh K. Can Thyrotropin, Tri-Iodothyronine, and Thyroxine Hormones be predictors of Cancer in thyroid lesions? Cureus. 2022;14(12):e32422. https://doi.org/10.7759/cureus.32422. PMID: 36644055; PMCID: PMC9832318.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhang B, Ma X, Wu N, Liu L, Liu X, Zhang J, Yang J, Niu T. Shear wave elastography for differentiation of benign and malignant thyroid nodules: a meta-analysis. J Ultrasound Med. 2013;32(12):2163–9. https://doi.org/10.7863/ultra.32.12.2163. PMID: 24277899.).

    Article  PubMed  Google Scholar 

  26. Yoo MH, Kim HJ, Choi IH, Park S, Yun S, Park HK, Byun DW, Suh K. Efficacy of Differential diagnosis of thyroid nodules by Shear Wave Elastography-the stiffness map. J Endocr Soc. 2021;5(11):bvab154. https://doi.org/10.1210/jendso/bvab154. PMID: 34703960; PMCID: PMC8533983.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bardet S, Ciappuccini R, Pellot-Barakat C, Monpeyssen H, Michels JJ, Tissier F, Blanchard D, Menegaux F, de Raucourt D, Lefort M, Reznik Y, Rouxel A, Heutte N, Brenac F, Leconte A, Buffet C, Clarisse B, Leenhardt L. Shear Wave Elastography in thyroid nodules with indeterminate cytology: results of a prospective bicentric study. Thyroid. 2017;27(11):1441–9. https://doi.org/10.1089/thy.2017.0293. PMID: 28982296.

    Article  CAS  PubMed  Google Scholar 

  28. Guo BL, Ouyang FS, Ouyang LZ, Liu ZW, Lin SJ, Meng W, Huang XY, Chen HX, Yang SM, Hu QG. Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules. Eur Radiol. 2019;29(3):1518–1526. doi: 10.1007/s00330-018-5715-5. Epub 2018 Sep 12. PMID: 30209592.

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Authors and Affiliations

  1. Department of Medical Ultrasound, Maanshan People’s Hospital, Hubei Road, Anhui Maanshan, 243032, China

    Tiantian Ren, Jiawei Wu & Fan Zhang

  2. School of Public Health, Southeast University, Hunan Road, Nanjing, Jiangsu, 210000, China

    Mingfei Jiang

  3. Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Meishan Road, AnHui Hefei, 230000, China

    Chaoxue Zhang

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The author declares that this work is completed by them and will bear the claims and responsibilities related to this article. Tiantian Ren and Chaoxue Zhang: Study design. Fan Zhang and Jiawei Wu: Data collection. Mingfei Jiang: Analyses, interpretation, and writing of the final manuscript. The authors read and approved the final manuscript.

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The retrospective study was carried out in accordance with releveant guidelines and regulations or declaration of Helsinki and was approved by the ethics committee of Maanshan people’s Hospital (2019019). The ethics committee of Maanshan people’s Hospital (2019019) waived the requirement for informed consent due to its retrospective status.

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Ren, T., Jiang, M., Wu, J. et al. Clinical value of grayscale ultrasound combined with real-time shear wave elastography nomogram in risk prediction of thyroid cancer. BMC Med Imaging 23, 123 (2023). https://doi.org/10.1186/s12880-023-01099-y

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