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- Author: Steen J Bonnema x
- Goiter and thyroid nodules x
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Background
Artificial intelligence algorithms could be used to risk-stratify thyroid nodules and may reduce the subjectivity of ultrasonography. One such algorithm is AIBx which has shown good performance. However, external validation is crucial prior to 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 Thyroid Imaging Reporting and Data System (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 NPVs 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.
Division of Endocrinology, Department of Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
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Objective
Thyroid nodule ultrasound characteristics are used as an indication for fine-needle aspiration cytology, usually as the basis for Thyroid Imaging Reporting and Data System (TIRADS) score calculation. Few studies on interobserver variation are available, all of which are based on analysis of preselected still ultrasound images and often lack surgical confirmation.
Methods
After the blinded online evaluation of video recordings of the ultrasound examinations of 47 consecutive malignant and 76 consecutive benign thyroid lesions, 7 experts from 7 thyroid centers answered 17 TIRADS-related questions. Surgical histology was the reference standard. Interobserver variations of each ultrasound characteristic were compared using Gwet’s AC1 inter-rater coefficients; higher values mean better concordance, the maximum being 1.0.
Results
On a scale from 0.0 to 1.0, the Gwet’s AC1 values were 0.34, 0.53, 0.72, and 0.79 for the four most important features in decision-making, i.e. irregular margins, microcalcifications, echogenicity, and extrathyroidal extension, respectively. The concordance in the discrimination between mildly/moderately and very hypoechogenic nodules was 0.17. The smaller the nodule size the better the agreement in echogenicity, and the larger the nodule size the better the agreement on the presence of microcalcifications. Extrathyroidal extension was correctly identified in just 45.8% of the cases.
Conclusions
Examination of video recordings, closely simulating the real-world situation, revealed substantial interobserver variation in the interpretation of each of the four most important ultrasound characteristics. In view of the importance for the management of thyroid nodules, unambiguous and widely accepted definitions of each nodule characteristic are warranted, although it remains to be investigated whether this diminishes observer variation.
