Thyroid nodule ultrasound accuracy in predicting thyroid malignancy based on TIRADS system

Nie, Wanlu; Zhu, Lili; Yan, Ping; Sun, Jie

doi:10.17219/acem/146776

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Advances in Clinical and Experimental Medicine

2022, vol. 31, nr 6, June, p. 597–606

doi: 10.17219/acem/146776

Publication type: meta-analysis

Language: English

License: Creative Commons Attribution 3.0 Unported (CC BY 3.0)

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Nie W, Zhu L, Yan P, Sun J. Thyroid nodule ultrasound accuracy in predicting thyroid malignancy based on TIRADS system. Adv Clin Exp Med. 2022;31(6):597–606. doi:10.17219/acem/146776

Thyroid nodule ultrasound accuracy in predicting thyroid malignancy based on TIRADS system

Wanlu Nie^1,A, Lili Zhu^2,C, Ping Yan^1,B, Jie Sun^1,E,F

¹ Department of Ultrasound, Penglai People’s Hospital, Yantai, China

² Deptartment of Endocrinology, Penglai People’s Hospital, Yantai, China

Abstract

Background. A frequent prevalence of thyroid nodules in patients prioritizes the need for an accurate method that characterizes them as benign or malignant. Fine-needle aspiration biopsy (FNAB) and thyroid ultrasonography (USG) are currently used for this purpose. However, since FNAB is complicated, time-consuming and expensive, thyroid USG, a fast and highly sensitive method, is preferably used. Although USG is reported as a suitable method for characterization of thyroid nodules, there are some contrasting studies available which report its limited use in the differentiation of benign and malignant thyroid nodules.
Objectives. This meta-analysis aims to assess the accuracy of ultrasound in predicting thyroid cancer in terms of sensitivity, specificity and diagnostic odds ratios (ORs) for positive and negative results.
Material and Methods. Systematic and extensive literature search on the use of ultrasound (US) to predict thyroid cancer was conducted in the databases of Scopus, CINAHL (via EBSCO), MEDLINE (via PubMed), and Web of Science, covering the period from 2010 till 2021. The morphological features of thyroid nodules observed during the USG were analyzed based on Thyroid Imaging Reporting And Data System (TIRADS) guidelines. The accuracy of thyroid US was determined using parameters such as sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic ORs. Moreover, the respective forest plot and hierarchical summary receiver operating characteristics (HSROC) curve were plotted.
Results. A total of 2765 reference studies were examined, and among them, 15 relevant references were selected. The selected studies were heterogeneous and included retrospective and prospective studies. The risk of publication bias is low as the p-value for both Egger’s and Begg’s tests is >0.05. The overall sensitivity of 92.53% (95% confidence interval (95% CI): [84.55%; 96.33%]), specificity of 33.88% (95% CI: [23.16%; 45.53%]) and diagnostic OR of 12.36 (95% CI: [3.90%; 54.11%]) are achieved. These results were statistically significant with a p-value < 0.001 and are predictive of US accuracy in detecting cancer.
Conclusion. The present meta-analysis, on the basis of statistically significant results, demonstrated the high accuracy of thyroid ultrasound in detection of malignant nature of nodules in patients suspected with a worrisome thyroid nodule.

Key words

ultrasound, thyroid nodule, fine-needle aspiration biopsy (FNAB), thyroid imaging – reporting and data system (TIRADS), benign and malignant nodule

Tables

Table 1. Demographic summary of included studies with thyroid ultrasound in suspected cases of thyroid malignancy

Study ID and year	Study type	Study duration	Total sample size	Age [years]	Gender M/F	Type of US probe
Arpana et al. 2018¹²	cross-sectional	1 year	85	14–70	15/70	NR
Al-Ghanimi et al. 2020¹³	retrospective	2 years	68	8–82	20/48	Esaote US machine (MyLab™ ClassC, Esaote, Genoa, Italy) and electronically focused near-field probes with a bandwidth of 7–12 MHz
Smith-Bindman et al. 2013¹⁴	retrospective	5 years	11618	30–70	2277/9341	NR
Liu et al. 2019¹⁵	retrospective	5 years	1568	18–80	412/1156	IU22 device (Philips Medical Systems, Bothell, USA; 5–12 MHz linear probe) or the S3000 device (Siemens Medical Solutions, Mountain View, USA; 5–14 MHz linear probe)
Luo et al. 2020¹⁶	retrospective	2 years	296	30–50	54/168	The Mylab™ 90 (Esaote SpA, Genoa, Italy) ultrasound image system was used for US examination, the L522 probe (4–9 MHz; Esaote SpA) for CEUS and the L523 probe (7.5–13.0 MHz, Esaote SpA) for conventional gray-scale US, CDUS and ES.
Kwak et al. 2011¹⁷	retrospective	8 months	1638	11–81	265/1373	5–12 MHz linear-array transducer (iU22; Philips Medical Systems.
Srinivas et al. 2016¹⁸	prospective	4 years	365	18–68	22/334	GE VOLUSON 730 PRO machine (GE Healthcare, Milwaukee, USA) equipped with a 7.5–12 MHz high-frequency linear array transducer with color and power Doppler capability.
Mohanty et al. 2019¹⁹	prospective	1 year	50	40–50	10/40	GE Logic F8 ultrasound machine with a 6–12 MHz linear array transducer and Samsung HS70A ultrasound machine with 4–18 MHz linear array transducer (Samsung Neurologica Corp., Danvers, USA)
Nabahati et al. 2019²⁰	cross-sectional	2 years	718	14–83	NR	Samsung H60 ultrasound machine, with a 3–14 MHz linear array transducer (Samsung Neurologica Corp.)
Ghani et al. 2018²¹	retrospective	2 years	91	27–80	21/83	linear array transducer (5–12 MHz) on ultrasound scanners HD11/HD11 XE/iU22 (Phillips Medical Systems) or Toshiba Xario200 (Toshiba Corp., Tokyo, Japan)
Ram et al. 2015²²	cross-sectional	2 years	101	15–73	20/81	High frequency linear probe with 7.5 MHz bandwidth (models Zario and Nemio; Toshiba Corp.)
Wettasinghe et al. 2019²³	prospective	1.5 years	263	16–74	16/247	NR
Azizi et al. 2021²⁴	prospective	1 year	355	40–50	45/310	virtual organ computer-aided analysis; (VOCAL; GE Healthcare) and a 3-D multi-planar display with rendering in HDLive and HDLive Silhouette (GE Healthcare).
Zayadeen et al. 2016²⁵	retrospective	3 years	1466	11–96	265/1201	5–12 MHz linear probe (iU22, Philips Healthcare) or a 6–15-MHz linear probe (Logiq E9, GE Healthcare)
Richie and Mellonie 2021²⁶	retrospective	2 years	226	18–62	39/187	NR

US – ultrasound; NR – not reported; CEUS – contrast-enhanced ultrasound; CDUS – color Doppler ultrasonography: ES – elastosonography.

Table 2. Exploration of heterogeneity sources; the impact of sample subgroups or participant characteristics on overall sensitivity and specificity

Subgroup	p-value
Full texts compared to abstracts	NA
High compared to low risk of bias	NA
Prospective compared to retrospective studies	0.024*
Adults compared to mixed population	0.924
Proportion of female participants	0.05*
Proportion of obese participants	NA
Type of ultrasound probe	0.034*
Ultrasonographer experience	0.001*
Clinical probability of TC	0.001*

TC – thyroid cancer; NA – not available. The details could not be retrieved from the report, or only one party was present; *significant impact of the subgroup on summary results.

Table 3. Sensitivity and specificity of different studies

Study ID and year	Specificity [%]	95% CI upper limit	95% CI lower limit	Sensitivity [%]	95% CI upper limit	95% CI lower limit
Kwak et al. 2011¹⁷	24.06	21.51	26.75	96.66	94.88	97.95
Smith-Bindman et al. 2013¹⁴	23.61	18.83	28.95	87.94	83.56	91.50
Ram et al. 2015²²	8.06	2.67	17.83	97.50	86.84	99.94
Zayadeen et al. 2016²⁵	14.25	11.98	16.78	97.57	96.46	98.41
Srinivas et al. 2016¹⁸	48.15	28.67	68.05	96.45	93.88	98.15
Ghani et al. 2018²¹	23.08	11.13	39.33	93.62	82.46	98.66
Arpana et al. 2018¹²	32.35	17.39	50.53	88.57	73.26	96.80
Wettasinghe et al. 2019²³	13.68	9.55	18.75	96.55	82.24	99.91
Luo et al. 2020¹⁶	84.52	77.84	89.82	74.63	62.51	84.47
Liu et al. 2019¹⁵	57.93	55.12	60.71	84.00	79.89	87.56
Nabahati et al. 2019²⁰	8.98	6.37	12.21	96.18	94.09	97.68
Mohanty et al. 2019¹⁹	60.00	36.05	80.88	95.45	77.16	99.88
Azizi et al. 2021²⁴	14.08	10.21	18.74	92.65	83.67	97.57
Al-Ghanimi et al. 2020¹³	50.00	15.70	84.30	91.67	81.61	97.24
Richie and Mellonie 2021²⁶	45.45	24.39	67.79	98.53	95.76	99.70

95% CI – 95% confidence interval.

Table 4. Diagnostic OR of cases studied

Study ID and year	Benign nodule (simple cyst)	Benign nodule (solid cyst)	Malignant nodule (solid cyst)	Malignant nodule (simple/mixed cyst)	Diagnostic odds ratio	95% CI upper limit
Kwak et al. 2011¹⁷	578.00	805.00	255.00	20.00	9.15	5.74	14.61
Bindmann et al. 2013¹⁴	248.00	220.00	68.00	34.00	2.25	1.44	3.54
Ram et al. 2015²²	39.00	57.00	5.00	1.00	3.42	0.38	30.43
Zayadeen et al. 2016²⁵	1043.00	734.00	122.00	26.00	6.67	4.32	10.29
Srinivas et al. 2016¹⁸	326.00	14.00	13.00	12.00	25.23	9.76	65.20
Ghani et al. 2017²¹	44.00	30.00	9.00	3.00	4.40	1.09	17.60
Arpana et al. 2018¹²	31.00	23.00	11.00	4.00	3.71	1.05	13.13
Wettasinghe et al. 2019²³	28.00	202.00	32.00	1.00	4.44	0.58	33.75
Luo et al. 2020¹⁶	50.00	24.00	131.00	17.00	16.05	7.50	32.37
Liu et al. 2019¹⁵	315.00	517.00	712.00	60.00	7.23	5.36	9.74
Nabahati et al. 2019²⁰	478.00	365.00	36.00	19.00	2.48	1.40	4.39
Mohanty et al. 2019¹⁹	21.00	8.00	12.00	1.00	31.50	3.50	283.30
Azizi et al. 2021²⁴	63.00	238.00	39.00	5.00	2.06	0.78	5.45
Ghanimi et al. 2021¹³	55.00	4.00	4.00	5.00	11.00	2.08	57.91
Richi et al. 2021²⁶	201.00	3.00	10.00	12.00	55.80	13.50	229.90

OR – odds ratio; 95% CI – 95% confidence interval.

Figures

Fig. 1. Thyroid Imaging Reporting and Data System (TIRADS) guidelines

Fig. 2. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram

TIRADS – Thyroid Imaging Reporting And Data System; 18-FDG PET – 18-fluorodeoxyglucose, positron emission tomography.

Fig. 3. Duke funnel plot test for publication bias

Fig. 4. Hierarchical summary receiver operating characteristics (HSROC) curve sensitivity compared to specificity

Fig. 5. Box and whisker plot for cumulative positive value (CPV) compared to cumulative negative value (CNV) of samples studied

Fig. 6. Forest plot for the diagnostic odds ratio (OR) of case studies100100

References (28)

Chaturvedi R, Kumar A, Balasubramanian B, Sreehari S. A retrospective study correlating ultrasound based Thyroid Imaging Reporting and Data System (TIRADS) with Bethesda system for thyroid cytopathology in thyroid nodule risk stratification. NEMJ. 2021;2(2):121–128. doi:10.2174/0250688203666210111152307
Chen H, Ye J, Song J, You Y, Chen W, Liu Y. Comparison of different ultrasound classification systems of thyroid nodules for identifying malignant potential: A cross-sectional study. Clinics (Sao Paulo). 2021;76:e2126. doi:10.6061/clinics/2021/e2126
Hahn SY, Shin JH, Oh YL, Park KW. Ultrasound-guided core needle biopsy techniques for intermediate or low suspicion thyroid nodules: Which method is effective for diagnosis? Korean J Radiol. 2019;20(10): 1454–1461. doi:10.3348/kjr.2018.0841
Al-Salam S, Sharma C, Abu Sa’a MT, et al. Ultrasound-guided fine needle aspiration cytology and ultrasound examination of thyroid nodules in the UAE: A comparison. PLoS One. 2021;16(4):e0247807. doi:10.1371/journal.pone.0247807
Kim SC, Kim JH, Choi SH, et al. Off-site evaluation of three-dimensional ultrasound for the diagnosis of thyroid nodules: Comparison with two-dimensional ultrasound. Eur Radiol. 2016;26(10):3353–3360. doi:10.1007/s00330-015-4193-2
Xie C, Cox P, Taylor N, LaPorte S. Ultrasonography of thyroid nodules: A pictorial review. Insights Imaging. 2016;7(1):77–86. doi:10.1007/s13244-015-0446-5
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. doi:10.1016/j. jacr.2017.01.046
Nam SJ, Kwak JY, Moon HJ, Yoon JH, Kim EK, Koo JS. Large (≥3cm) thyroid nodules with benign cytology: Can Thyroid Imaging Reporting and Data System (TIRADS) help predict false-negative cytology? PLoS One. 2017;12(10):e0186242. doi:10.1371/journal.pone.0186242
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;6(5):225–237. doi:10.1159/000478927
Trimboli P, Durante C. Ultrasound risk stratification systems for thyroid nodule: Between lights and shadows, we are moving towards a new era. Endocrine. 2020;69(1):1–4. doi:10.1007/s12020-020-02196-6
Colakoglu B, Yildirim D, Alis D, et al. Elastography in distinguishing benign from malignant thyroid nodules. J Clin Imaging Sci. 2016;6:51. doi:10.4103/2156-7514.197074
Arpana, Panta OB, Gurung G, Pradhan S. Ultrasound findings in thyroid nodules: A radio-cytopathologic correlation. J Med Ultrasound. 2018;26(2):90–93. doi:10.4103/JMU.JMU_7_17
Al-Ghanimi IA, Al-Sharydah AM, Al-Mulhim S, et al. Diagnostic accuracy of ultrasonography in classifying thyroid nodules compared with fine-needle aspiration. Saudi J Med Med Sci. 2020;8(1):25–31. doi:10.4103/sjmms.sjmms_126_18
Smith-Bindman R, Lebda P, Feldstein VA, et al. Risk of thyroid cancer based on thyroid ultrasound imaging characteristics: Results of a population-based study. JAMA Intern Med. 2013;173(19):1788–1796. doi:10.1001/jamainternmed.2013.9245
Liu T, Guo Q, Lian C, et al. Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Med Image Anal. 2019;58:101555. doi:10.1016/j.media.2019.101555
Luo W, Zhang Y, Yuan J, et al. Differential diagnosis of thyroid nodules through a combination of multiple ultrasonography techniques: A decision-tree model. Exp Ther Med. 2020;19(6):3675–3683. doi:10. 3892/etm.2020.8621
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. doi:10.1148/radiol.11110206
Srinivas MN, Amogh VN, Gautam MS, et al. A prospective study to evaluate the reliability of thyroid imaging reporting and data system in differentiation between benign and malignant thyroid lesions. J Clin Imaging Sci. 2016;6:5. doi:10.4103/2156-7514.177551
Mohanty J, Sanket, Mishra P. Role of ACR-TIRADS in risk stratification of thyroid nodules. Int J Res Med Sci. 2019;7(4):1039–1043. doi:10.18203/2320-6012.ijrms20191076
Nabahati M, Moazezi Z, Fartookzadeh S, Mehraeen R, Ghaemian N, Sharbatdaran M. The comparison of accuracy of ultrasonographic features versus ultrasound-guided fine-needle aspiration cytology in diagnosis of malignant thyroid nodules. J Ultrasound. 2019;22(3):315–321. doi:10.1007/s40477-019-00377-2
Ghani FA, Nurismah MI, Husyairi H, Shahrun Niza AS, Radhika S. Reliability of the ultrasound classification system of thyroid nodules in predicting malignancy. Med J Malaysia. 2018;73(5):263–271. PMID:30350802.
Ram N, Hafeez S, Qamar S, et al. Diagnostic validity of ultrasonography in thyroid nodules. J Pak Med Assoc. 2015;65(8):875–878. PMID:26228335.
Wettasinghe MC, Rosairo S, Ratnatunga N, Wickramasinghe ND. Diagnostic accuracy of ultrasound characteristics in the identification of malignant thyroid nodules. BMC Res Notes. 2019;12(1):193. doi:10.1186/s13104-019-4235-y
Azizi G, Faust K, Ogden L, et al. 3-D ultrasound and thyroid cancer diagnosis: A prospective study. Ultrasound Med Biol. 2021;47(5):1299–1309. doi:10.1016/j.ultrasmedbio.2021.01.010
Zayadeen AR, Abu-Yousef M, Berbaum K. Retrospective evaluation of ultrasound features of thyroid nodules to assess malignancy risk: A step toward TIRADS. AJR Am J Roentgenol. 2016;207(3):460–469. doi:10.2214/AJR.15.15121
Richie AJ, Mellonie P. Accuracy of thyroid imaging and reporting data systems in risk stratification of thyroid nodules: A retrospective observational study. Int J Anat Radio Surg. 2021;10(1):58–61. doi:10.7860/IJARS/2021/47306:2627
Latif MA, El Rakhawy MM, Saleh MF. Diagnostic accuracy of B-mode ultrasound, ultrasound elastography and diffusion weighted MRI in differentiation of thyroid nodules (prospective study). Egypt J Radiol Nucl Med. 2021;52:256. doi:10.1186/s43055-021-00640-9
Jiang D, Zang Y, Jiang D, Zhang X, Zhao C. Value of rapid on-site evaluation for ultrasound-guided thyroid fine needle aspiration. J Int Med Res. 2019;47(2):626–634. doi:10.1177/0300060518807060

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Thyroid nodule ultrasound accuracy in predicting thyroid malignancy based on TIRADS system

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