Prevalence and Prediction for Malignancy of Additional Thyroid Nodules Coexisting with Proven Papillary Thyroid Microcarcinoma

2013 ◽  
Vol 149 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Sung Yong Choi ◽  
Seung Hoon Woo ◽  
Jung Hee Shin ◽  
Noorie Choi ◽  
Young-Ik Son ◽  
...  
2022 ◽  
Vol 11 ◽  
Author(s):  
Jing Du ◽  
Ruijun Han ◽  
Cui Chen ◽  
Xiaowei Ma ◽  
Yuling Shen ◽  
...  

BackgroundUltrasound, cytology, and BRAFV600E mutation analysis were applied as valuable tools in the differential diagnosis of thyroid nodules. The aim of the present study was to evaluate the diagnostic efficiency of the three methods and their combined use in screening for papillary thyroid microcarcinoma (PTMC).MethodsA total of 1,081 patients with 1,157 thyroid nodules (0.5–1 cm in maximum diameter) classified as thyroid imaging reporting and data system (TIRADS) 4–5 were recruited. All patients underwent ultrasound, fine-needle aspiration (FNA) examination, and an additional BRAFV600E mutation test. TIRADS and Bethesda System for Reporting Thyroid Cytopathology (BSRTC) were adopted to judge the ultrasound and cytological results. The receiver operating characteristic (ROC) curve was established to assess the diagnostic values of different methods.ResultsOf the 1,157 nodules, 587 were benign and 570 were PTMCs. BRAFV600E mutation test had highest sensitivity (85.4%), specificity (97.1%), accuracy (91.4%), and area under the ROC curve (Az) value (0.913) among the three methods. The combination of BSRTC and BRAFV600E mutation analysis yielded a considerably high sensitivity (96.0%), accuracy (94.3%), and negative predictive value (95.9%) than either BSRTC or BRAFV600E mutation alone (P < 0.0001 for all comparisons). Of all the methods, the combined use of the three methods produced the best diagnostic performance (Az = 0.967), which was significantly higher than that (Az = 0.943) for the combination of BSRTC and BRAFV600E mutation (P < 0.0001). The diagnostic accuracy of the molecular method in the 121 nodules with indeterminate cytology was 90.1% (109/121), which was significantly higher than that of TIRADS classification, 74.4% (90/121) (P = 0.002).ConclusionThe combined use of ultrasound, cytology, and BRAFV600E mutation analysis is the most efficient and objective method for diagnosing PTMC. Both BRAFV600E mutation and TIRADS classification are potentially useful adjuncts to differentiate thyroid nodules, especially indeterminate samples classified as BSRTC III.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yi-Feng Zhang ◽  
Chang Liu ◽  
Hui-Xiong Xu ◽  
Jun-Mei Xu ◽  
Jing Zhang ◽  
...  

Purpose. To evaluate the diagnostic performance of ARFI imaging in differentiating between benign and malignant thyroid nodules <1 cm.Materials and Methods. 173 pathologically proven thyroid nodules (77 benign, 96 malignant) in 157 patients were included in this study. Receiver-operating characteristic curve (ROC) analyses were performed to assess the diagnostic performance of conventional ultrasound (US) and ARFI imaging in papillary thyroid microcarcinoma (PTMC). The independent risk factors for predicting PTMC were evaluated.Results. The mean SWV value of benign and malignant thyroid nodules were 2.57 ± 0.79 m/s (range: 0.90–4.92 m/s) and 3.88 ± 2.24 m/s (range: 1.49–9.00 m/s) (P=0.000). Az for VTI elastography score was higher than that for hypoechoic, absence of halo sign, and type III vascularity (P<0.05). The optimal cut-offs for VTI elastography score and SWV were score 4 and 3.10 m/s. Gender, hypoechoic, taller than wide, VTI elastography score ≥ 4, and SWV > 3.10 m/s had been found to be independent risk factors for predicting PTMC.Conclusion. ARFI elastography can provide elasticity information of PTMC quantitatively (VTQ) and directly reflects the overall elastic properties (VTI). Gender, hypoechogenicity, taller than wide, VTI elastography score ≥ 4, and SWV > 3.10 m/s are independent risk factors for predicting PTMC. ARFI elastography seems to be a new tool for the diagnosis of PTMC.


Author(s):  
Jae Won Kim ◽  
Dong Youl Lee ◽  
Young Up Cho ◽  
Chang Hyo Kim ◽  
Yoon Suk Oh ◽  
...  

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