Automated Structured Reporting for Thyroid Ultrasound: Effect on Reporting Errors and Efficiency

[Standardized acquisition and documentation of cine loops on conventional thyroid ultrasound]

Ultrasound is the basic imaging method for the assessment of the thyroid gland. Due to the high prevalence of structural disease, the examination procedure is used very frequently in Germany, in many cases in the context of follow-up. The assessment of thyroid pathologies and their dynamics is subjected to relevant inter- and intraobserver variability. Findings that were not identified during live ultrasound cannot be assessed retrospectively. Applying an SOP for the acquisition and documentation of standardized video sequences of ultrasound images (so-called cine loops), allows for a secondary retrospective evaluation of the thyroid gland, taking into account previously acquired images analogous to other cross-sectional imaging methods such as CT or MRI. The cine loops can be acquired by non-physician personnel, stored to the local PACS and used for educational and research purposes.

Patients undergoing endocrine consultation and first diagnosis of nodular disease: Indications of thyroid ultrasound and completeness of ultrasound reports

PURPOSE

To evaluate reasons for performing ultrasonography (US) and completeness of US reports in patients undergoing endocrine consultation with the first diagnosis of nodular disease.

METHODS

Since January 1 to June 30, 2021, we prospectively collected patient data (age and thyroid-stimulating hormone concentrations), reasons for performing thyroid US, and completeness of reports regarding the description of the thyroid gland and nodules. In the case of multiple nodules, we considered the nodule suspected of malignancy and the largest one. To evaluate the accuracy of thyroid nodule description, we referred to the five characteristics suggested by the ACR TI-RADS system.

RESULTS

A total of 341 patients with thyroid nodules received endocrine consultation (female, 78%). The most frequent reasons for performing thyroid US were unrelated to a suspected thyroid disease (31.7%), followed by incidentaloma (23.5%), dysfunction or positivity for thyroid antibodies (19.1%), symptomatic or visible nodules (17.6%), and family history of any thyroid disease (8.2%). Gland texture was not reported in 41.9%. The depth of the lobes was the dimension reported most frequently (42.2%), but any diameter was not reported in 57.8% of the cases. As regards the description of the most relevant nodule, length was reported more frequently (75.9%). Margins and echogenicity were more frequently described (54.5% and 44.3%, respectively) than other characteristics (composition: 27%; shape: 8.8%; echogenic foci: 6.7%). No reports had indicated the malignancy risk stratification.

CONCLUSIONS

The results of the study demonstrate that in patients undergoing endocrine consultation with first detected thyroid nodules, US was mostly performed in asymptomatic cases, US reports were incomplete, and no risk stratification system was reported.

Artificial Intelligence (AI) Tools for Thyroid Nodules on Ultrasound, From the AJR Special Series on AI Applications

Artificial intelligence (AI) methods for evaluating thyroid nodules on ultrasound have been widely described in the literature, with reported performance of AI tools matching or in some instances surpassing radiologists. As these data have accumulated, products for classification and risk stratification of thyroid nodules on ultrasound have become commercially available. This article reviews FDA-approved products currently on the market, with a focus on product features, reported performance, and considerations for implementation. The products perform risk stratification primarily using the Thyroid Imaging Reporting and Data System (TI-RADS), though may provide additional prediction tools independent of TI-RADS. Key issues in implementation include integration with radiologist interpretation, impact on workflow and efficiency, and performance monitoring. AI applications beyond nodule classification, including report construction and incidental findings follow-up, are also described. Anticipated future directions of research and development in AI tools for thyroid nodules are highlighted.

Diagnostic Performance of Kwak, EU, ACR, and Korean TIRADS as Well as ATA Guidelines for the Ultrasound Risk Stratification of Non-Autonomously Functioning Thyroid Nodules in a Region with Long History of Iodine Deficiency: A German Multicenter Trial

Germany has a long history of insufficient iodine supply and thyroid nodules occur in over 30% of the adult population, the vast majority of which are benign. Non-invasive diagnostics remain challenging, and ultrasound-based risk stratification systems are essential for selecting lesions requiring further clarification. However, no recommendation can yet be made about which system performs the best for iodine deficiency areas. In a German multicenter approach, 1211 thyroid nodules from 849 consecutive patients with cytological or histopathological results were enrolled. Scintigraphically hyperfunctioning lesions were excluded. Ultrasound features were prospectively recorded, and the resulting classifications according to five risk stratification systems were retrospectively determined. Observations determined 1022 benign and 189 malignant lesions. The diagnostic accuracies were 0.79, 0.78, 0.70, 0.82, and 0.79 for Kwak Thyroid Imaging Reporting and Data System (Kwak-TIRADS), American College of Radiology (ACR) TI-RADS, European Thyroid Association (EU)-TIRADS, Korean-TIRADS, and American Thyroid Association (ATA) Guidelines, respectively. Receiver Operating Curves revealed Areas under the Curve of 0.803, 0.795, 0.800, 0.805, and 0.801, respectively. According to the ATA Guidelines, 135 thyroid nodules (11.1%) could not be classified. Kwak-TIRADS, ACR TI-RADS, and Korean-TIRADS outperformed EU-TIRADS and ATA Guidelines and therefore can be primarily recommended for non-autonomously functioning lesions in areas with a history of iodine deficiency.

Handoffs in Radiology: Minimizing Communication Errors and Improving Care Transitions

Handoffs are essential to achieving safe care transitions. In radiology practice, frequent transitions of care responsibility among clinicians, radiologists, and patients occur between moments of care such as determining protocol, imaging, interpreting, and consulting. Continuity of care is maintained across these transitions with handoffs, which are the process of communicating patient information and transferring decision-making responsibility. As a leading cause of medical error, handoffs are a major communication challenge that is exceedingly common in both diagnostic and interventional radiology practice. The frequency of handoffs in radiology underscores the importance of using evidence-based strategies to improve patient safety in the radiology department. In this article, reliability science principles and handoff improvement tools are adapted to provide radiology-focused strategies at individual, team, and organizational levels with the goal of minimizing handoff errors and improving care transitions.