In this methodological study, EI measured by thyroid SWE seems suboptimal for clinical use, due to a low inter- and intrarater agreement. That EI varies from day to day furthermore jeopardizes the validity of the method. Although the proportion of agreement was acceptable for some EI parameters, it is questionable whether EI assessments can reliably differentiate malignant from benign nodules in the individual patient.

Thyroid nodular disease is common, but predicting the risk of malignancy can be difficult. In this prospective study, we aimed to assess the diagnostic accuracy of shear wave elastography (SWE) in predicting thyroid malignancy. Patients with thyroid nodules were enrolled from a surgical tertiary unit. Elasticity index (EI) measured by SWE was registered for seven EI outcomes assessing nodular stiffness and heterogeneity. The diagnosis was determined histologically. In total, 329 patients (mean age: 55 ± 13 years) with 413 thyroid nodules (mean size: 32 ± 13 mm, 88 malignant) were enrolled. Values of SWE region of interest (ROI) for malignant and benign nodules were highly overlapping (ranges for SWE-ROImean: malignant 3–100 kPa; benign 4–182 kPa), and no difference between malignant and benign nodules was found for any other EI outcome investigated (P = 0.13–0.96). There was no association between EI and the histological diagnosis by receiver operating characteristics analysis (area under the curve: 0.51–0.56). Consequently, defining a cut-off point of EI for the prediction of malignancy was not clinically meaningful. Testing our data on previously proposed cut-off points revealed a low accuracy of SWE (56–80%). By regression analysis, factors affecting EI included nodule size >30 mm, heterogeneous echogenicity, micro- or macrocalcifications and solitary nodule. In conclusion, EI, measured by SWE, showed huge overlap between malignant and benign nodules, and low diagnostic accuracy in the prediction of thyroid malignancy. Our study supports that firmness of thyroid nodules, as assessed by SWE, should not be a key feature in the evaluation of such lesions.

s-TSH was significantly higher in patients with DTC than in those with benign TND. However, this difference can be explained by abnormally lower s-TSH level in the latter group, probably caused by subtle nodular functional autonomy. Due to the huge overlap and the small difference in median s-TSH between patients with benign and malignant TND, s-TSH is not suitable as a biomarker of DTC in a clinical setting.

Background: Artificial intelligence algorithms could be used to risk stratify thyroid nodules and may reduce subjectivity of ultrasonography. One such algorithm is AIBx which has shown good performance. However, external validation is crucial prior to a clinical implementation. Materials and methods: Patients harboring thyroid nodules 1-4 cm in size, undergoing thyroid surgery from 2014 to 2016 in a single institution, were included. A histological diagnosis was obtained in all cases. Medullary thyroid cancer, metastasis from other cancers, thyroid lymphomas, and purely cystic nodules were excluded. Retrospectively, transverse ultrasound images of the nodules were analyzed by AIBx, and the results were compared with histopathology and TIRADS, calculated by experienced physicians. Results: Out of 329 patients, 257 nodules from 209 individuals met the eligibility criteria. Fifty-one nodules (20%) were malignant. AIBx had a negative predictive value (NPV) of 89.2%. Sensitivity, specificity, and positive predictive values (PPV) were 78.4%, 44.2% and 25.8% respectively. Considering both TIRADS 4 and TIRADS 5 nodules as malignant lesions resulted in an NPV of 93.0%, while PPV and specificity were only 22.4% and 19.4%, respectively. By combining AIBx with TIRADS, no malignant nodules were overlooked. Conclusion: When applied to ultrasound images obtained in a different setting than used for training, AIBx had comparable negative predictive values to TIRADS. AIBx performed even better when combined with TIRADS, thus reducing false negative assessments. These data support the concept of AIBx for thyroid nodules, and this tool may help less experienced operators by reducing the subjectivity inherent to thyroid ultrasound interpretation.