The 2017 ACR Commission on Human Resources Workforce Survey

Interobserver Variability of Sonographic Features Used in the American College of Radiology Thyroid Imaging Reporting and Data System

OBJECTIVE

The purpose of this study was to assess interobserver variability in assigning features in the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) lexicon and in making recommendations for thyroid nodule biopsy.

MATERIALS AND METHODS

The study cohort comprised 100 nodules in 92 patients who underwent fine-needle aspiration with definitive cytologic results (Bethesda category II or VI) or diagnostic lobectomy between April 2009 and May 2010. Eight board-certified radiologists evaluated the nodules according to the five feature categories that constitute ACR TI-RADS and gave a biopsy recommendation based on their own practice. Variability in feature assignment and biopsy recommendation was assessed with the Fleiss kappa statistic.

RESULTS

Agreement in interpretation was fair to moderate for all features except shape (κ = 0.61) and macrocalcifications (κ = 0.73), which had substantial agreement. The features with the poorest agreement were margin and other types of echogenic foci, which had kappa values ranging from 0.25 to 0.39, indicating fair agreement. Interobserver agreement regarding biopsy recommendation was fair (κ = 0.22) based on radiologists' current practice. Applying ACR TI-RADS resulted in moderate agreement (κ = 0.51).

CONCLUSION

Variability in interpreting thyroid nodule sonographic features was highest for margin and all types of echogenic foci, except for macrocalcifications. Because radiologists' interpretations of these features change the level of suspicion of thyroid malignancy, the results of this study suggest a need for further education. Despite the variability in assigning features, adoption of ACR TI-RADS improves agreement for recommending biopsy.

The Role of Artificial Intelligence in Diagnostic Radiology: A Survey at a Single Radiology Residency Training Program

PURPOSE

Advances in artificial intelligence applied to diagnostic radiology are predicted to have a major impact on this medical specialty. With the goal of establishing a baseline upon which to build educational activities on this topic, a survey was conducted among trainees and attending radiologists at a single residency program.

METHODS

An anonymous questionnaire was distributed. Comparisons of categorical data between groups (trainees and attending radiologists) were made using Pearson χ² analysis or an exact analysis when required. Comparisons were made using the Wilcoxon rank sum test when the data were not normally distributed. An α level of 0.05 was used.

RESULTS

The overall response rate was 66% (69 of 104). Thirty-six percent of participants (n = 25) reported not having read a scientific medical article on the topic of artificial intelligence during the past 12 months. Twenty-nine percent of respondents (n = 12) reported using artificial intelligence tools during their daily work. Trainees were more likely to express doubts on whether they would have pursued diagnostic radiology as a career had they known of the potential impact artificial intelligence is predicted to have on the specialty (P = .0254) and were also more likely to plan to learn about the topic (P = .0401).

CONCLUSIONS

Radiologists lack exposure to current scientific medical articles on artificial intelligence. Trainees are concerned by the implications artificial intelligence may have on their jobs and desire to learn about the topic. There is a need to develop educational resources to help radiologists assume an active role in guiding and facilitating the development and implementation of artificial intelligence tools in diagnostic radiology.