Impact of the ultrasonography assessment method on the malignancy risk and diagnostic performance of five risk stratification systems in thyroid nodules

Yang, Go Eun; Na, Dong Gyu

doi:10.1007/s12020-021-02795-x

Cited by 8 publications

(10 citation statements)

References 26 publications

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“…shown by Yang and Na, 15 retrospective review and reclassification of nodules lead to overestimated specificities and underestimated the unnecessary biopsies rates compared to prospective US assessments.…”

Section: Discussionmentioning

confidence: 94%

“…Previous retrospective studies 17–19,33 with a large number of nodules included, mostly surgical series, have tried to estimate the efficacy of the different US classification systems. However, as shown by Yang and Na, 15 retrospective review and reclassification of nodules lead to overestimated specificities and underestimated the unnecessary biopsies rates compared to prospective US assessments. A large prospective Italian study 20 involving 502 thyroid nodules demonstrated sensitivity and specificity of 83.3% (95% CI: 67.2%–93.6%) and 56.2% (95% CI: 51.6%–60.8%), respectively, for ACR‐TIRADS and 86.1% (95% CI: 70.5%–95.3%) and 32.0% (95% CI: 27.8%–36.4%), respectively, for EU‐TIRADS.…”

Section: Discussionmentioning

confidence: 99%

“…10 Many authors have compared US-based RSSs to determine which nodules should be referred for FNA. [11][12][13][14][15][16] Most of the systems had similar discriminatory capacities to identify malignancy 6 but showed variable performance in avoiding unnecessary thyroid biopsy, suggesting that future studies would address whether size cutoffs should be changed to improve the accuracy of the proposed criteria.…”

Section: Introductionmentioning

confidence: 99%

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A prospective comparison of ACR‐TIRADS and EU‐TIRADS in thyroid nodule assessment for FNA‐US

Eidt

Oliveira

Lagos

et al. 2022

Clinical Endocrinology

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Objective Prospective data on the accuracy of ultrasound (US) classification systems in thyroid nodules are still scarce. The aim of this study is to compare the accuracy of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR‐TIRADS) and European (EU)‐TIRADS classification systems. Design and Patients Consecutive patients with one or more thyroid nodule(s) who underwent fine‐needle aspiration (FNA) under ultrasonographic guidance (FNA‐US) were prospectively evaluated. Measurements Clinical evaluation and US data were collected. The reference standard used for this study was FNA‐US cytology and histopathological diagnosis. Results A total of 186 thyroid nodules in 166 patients were evaluated, resulting in 168 nodules from 149 patients with conclusive benign or malignant results. Sensitivity, specificity, negative predictive value (NPV) and false negative (FN) were 100.0%, 28.7%, 100.0% and 0.0%, respectively, for ACR‐TIRADS; and 90.0%, 19.1%, 96.8% and 9.1% (n = 1), respectively, for EU‐TIRADS. The number of unnecessary FNA‐US indicated by ACR‐TIRADS was lower than EU‐TIRADS (71.3% vs. 80.9%, p = .017), and the number of possibly avoided FNA‐US was higher (26.7% vs. 17.8%). Using the same threshold of ACR‐TIRADS to indicate FNA‐US in EU‐TIRADS 3 nodules (2.5 cm), there was an improvement in specificity (30.6%) and avoided FNA‐US (28.6%). The best performance of both systems was demonstrated when FNA‐US would be indicated only in highly suspicious nodules and/or in the presence of lymphadenopathy, with 85.7% and 89.3% of possibly avoided FNA‐US for ACR‐TIRADS and EU‐TIRADS, respectively, without increasing FN. Conclusion Both systems presented high sensitivity, but low specificity in selecting nodules for FNA‐US. The use of nodular size for FNA‐US selection is questioned.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A prospective comparison of ACR‐TIRADS and EU‐TIRADS in thyroid nodule assessment for FNA‐US

Eidt

Oliveira

Lagos

et al. 2022

Clinical Endocrinology

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“…The discrepancy may be partly due to an overestimation of the malignant risk associated with PEF in retrospective US assessments [ 27 ]. Notably, the malignancy risk for K-TIRADS 4 nodules with PEF in their study was significantly higher (47.9%) than the risks estimated in other studies, which ranged from 9.2% to 25.9% [ 25 - 27 ].…”

Section: Discussionmentioning

confidence: 99%

Malignancy risk stratification and subcategorization of K-TIRADS intermediate suspicion thyroid nodules: a retrospective multicenter study

Lee,

Na,

Kim

2024

Ultrasonography

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Purpose: This study aimed to develop the ultrasonography (US) criteria for risk stratification of the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) 4 nodules, and to evaluate the diagnostic yield of a modified biopsy criterion in a multicenter cohort.Methods: In total, 1,542 K-TIRADS 4 nodules (≥1 cm) were included in the study. US criteria for the subcategorization of K-TIRADS 4 nodules were developed based on high-risk US features. The diagnostic yields and false referral rates of biopsy criterion 1 (size cut-off of 1 cm), biopsy criterion 2 (size cut-off of 1.5 cm), and modified biopsy criterion 3 (size cut-off of 1 cm for K-TIRADS 4B and 1.5 cm for K-TIRADS 4A) were evaluated.Results: The five high-risk US features (solid composition, marked hypoechogenicity, macrocalcification, punctate echogenic foci, and irregular margin) independently increased the malignancy risk of the K-TIRADS 4 nodules (P<0.001). The K-TIRADS 4 nodules could be subcategorized into higher- and lower-risk subcategories according to the number of high-risk US features: K-TIRADS 4B (≥2 US features) and K-TIRADS 4A (≤1 US feature). The modified biopsy criterion increased the diagnostic yield by 7.8% compared with criterion 2 and reduced the false referral rate by 15.3% compared with criterion 1 (P<0.001).Conclusion: The K-TIRADS 4 nodules were subcategorized as K-TIRADS 4B and K-TIRADS 4A based on high-risk US features. The modified biopsy criterion 3 showed a similar diagnostic yield and reduced false referral rate compared to criterion 1.

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“…FNA and pathological biopsy are currently the "gold standard" for identifying malignant TN, but compared to the US, FNA and pathological biopsies are not only expensive but also have some damage to the patient itself [51]. e application of AI in imaging provides good help for the diagnosis of malignant TN based on the US, and many AI-assisted diagnostic techniques have been used to diagnose TN, but the accuracy of these techniques varies greatly [52][53][54].…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis

Xue

Zhou

Wang

et al. 2022

International Journal of Endocrinology

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Background. Ultrasonography (US) is the most common method of identifying thyroid nodules, but US images require an experienced surgeon for identification. Many artificial intelligence (AI) techniques such as computer-aided diagnostic systems (CAD), deep learning (DL), and machine learning (ML) have been used to assist in the diagnosis of thyroid nodules, but whether AI techniques can improve the diagnostic accuracy of thyroid nodules still needs to be explored. Objective. To clarify the accuracy of AI-based thyroid nodule US images for differentiating benign and malignant thyroid nodules. Methods. A search strategy of “subject terms + key words” was used to search PubMed, Cochrane Library, Embase, Web of Science, China Biology Medicine (CBM), and China National Knowledge Infrastructure (CNKI) for studies on AI-assisted diagnosis of thyroid nodules based on US images. The summarized receiver operating characteristic (SROC) curve and the pooled sensitivity and specificity were used to assess the performance of the diagnostic tests. The quality assessment of diagnostics accuracy studies-2 (QUADAS-2) tool was used to assess the methodological quality of the included studies. The Review Manager 5.3 and Stata 15 were used to process the data. Subgroup analysis was based on the integrity of data collection. Results. A total of 25 studies with 17,429 US images of thyroid nodules were included. AI-assisted diagnostic techniques had better diagnostic efficacy in the diagnosis of benign and malignant thyroid nodules: sensitivity 0.88 (95% CI: (0.85–0.90)), specificity 0.81 (95% CI: 0.74–0.86), diagnostic odds ratio (DOR) 30 (95% CI: 19–46). The SROC curve indicated that the area under the curve (AUC) was 0.92 (95% CI: 0.89–0.94). Threshold effect analysis showed a Spearman correlation coefficient: 0.17 < 0.5, suggesting no threshold effect for the included studies. After a meta-regression analysis of 4 different subgroups, the results showed a statistically significant effect of mean age ≥50 years on heterogeneity. Compared with studies with an average age of ≥50 years, AI-assisted diagnostic techniques had higher diagnostic performance in studies with an average age of <50 years (0.89 (95% CI: 0.87–0.92) vs. 0.80 (95% CI: 0.73–0.88)), (0.83 (95% CI: 0.77–0.88) vs. 0.73 (95% CI: 0.60–0.87)). Conclusions. AI-assisted diagnostic techniques had good diagnostic efficacy for thyroid nodules. For the diagnosis of <50 year olds, AI-assisted diagnostic technology was more effective in diagnosis.

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Impact of the ultrasonography assessment method on the malignancy risk and diagnostic performance of five risk stratification systems in thyroid nodules

Cited by 8 publications

References 26 publications

A prospective comparison of ACR‐TIRADS and EU‐TIRADS in thyroid nodule assessment for FNA‐US

A prospective comparison of ACR‐TIRADS and EU‐TIRADS in thyroid nodule assessment for FNA‐US

Malignancy risk stratification and subcategorization of K-TIRADS intermediate suspicion thyroid nodules: a retrospective multicenter study

Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis

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