Diagnostic accuracy of S-Detect in distinguishing benign and malignant thyroid nodules: A meta-analysis

Comparison of artificial intelligence, elastic imaging, and the thyroid imaging reporting and data system in the differential diagnosis of suspicious nodules

Background

Ultrasound is widely used for detecting thyroid nodules in clinical practice. This retrospective study aimed to assess the diagnostic efficacy of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS), S-Detect, and elastography of the carotid artery for suspicious thyroid nodules and to determine the complementary value of artificial intelligence and elastography.

Methods

Between January 2021 and November 2021, 101 consecutive patients with 138 thyroid nodules were enrolled in The First Hospital of China Medical University. All nodules were evaluated using ACR-TIRADS categories (TR), S-Detect, and elastography, and then the diagnostic performance of the different methods and the combined assessment were compared. The inclusion criteria were the following: (I) TR3, TR4, and TR5 nodules, which were defined as "suspicious nodules"; (II) patients who had surgical or cytopathological results after ultrasound examination; and (III) voluntary enrollment in this study. Meanwhile, the exclusion criteria were the following: (I) TR1 and TR2 nodules, (II) patients who had undergone fine-needle aspiration before ultrasound examination, and (III) inconclusive cytologic findings.

Results

A total of 71 patients (12 men and 59 women) with 94 suspicious thyroid nodules (42 benign nodules and 52 malignant nodules) were finally included in this study. S-Detect had a significantly better sensitivity than did ACR-TIRADS [S-Detect: 98.1%, 95% confidence interval (CI): 89.7-100.0%; ACR-TIRADS: 84.6%, 95% CI: 71.9-93.1%; P=0.036], but its specificity was much lower (S-Detect: 19.0%; 95% CI: 8.6-34.1%; ACR-TIRADS: 40.5%, 95% CI: 25.6-56.7%; P=0.032). The accuracy was not significantly different between S-Detect (62.8%; 95% CI: 52.2-72.5%) and ACR-TIRADS (64.9%; 95% CI: 54.4-74.5%) (P=0.761). The elasticity contrast index (ECI) was not definitively useful in identifying suspicious thyroid nodules (P=0.592). Compared with the use of ACR-TIRADS and S-Detect alone, the specificity (45.2%; 95% CI: 29.8-61.3%), positive predictive value (65.2%; 95% CI: 52.4-76.5%), accuracy (66.0%; 95% CI: 55.5-75.4%), and the area under the receiver operating characteristic curve (0.640; 95% CI: 0.534-0.736) of their combination were higher but not significantly so.

Conclusions

At present, S-Detect cannot replace manual diagnosis, and the value of elastography of the carotid artery in diagnosing suspected thyroid nodules remains unclear.

Artificial Intelligence for Evaluation of Thyroid Nodules: A Primer

Background: Artificial intelligence (AI) is broadly defined as the ability of machines to apply human-like reasoning to problem solving. Recent years have seen a rapid growth of AI in many disciplines. This review will focus on AI applications in the assessment of thyroid nodules. Summary: AI encompasses two related computational techniques: machine learning, in which computers learn by observing data provided by humans, and deep learning, which employs neural networks that mimic brain structure and function to analyze data. Some experts believe the way AI systems reach a conclusion should be transparent, or explainable, while others disagree. Most AI platforms in thyroid disease have focused on malignancy risk stratification of nodules. To date, four have been approved by the United States Food and Drug Administration. While the results of validation studies have been mixed, there is ample evidence that AI can exceed the performance of some humans, particularly physicians with less experience. AI has also been applied to assessment of lymph nodes and cytopathology specimens. Conclusions: Adoption of AI in thyroid disease will require vendors to demonstrate that their software works as intended, is readily usable in real-world settings, and is cost effective. AI platforms that perform best in head-to-head comparisons will dominate and spur wider adoption.