Clinical value of artificial intelligence ultrasound-assisted diagnostic system in the differential diagnosis of benign and malignant thyroid nodules
TL;DR
The AI-UADS demonstrates good diagnostic performance in differentiating BTNs and MTNs, and can serve as an effective auxiliary tool for ultrasonographers, showing particular advantages in diagnosing nodules with a maximum diameter, located in the left lobe, far from the capsule, and in patients without HT.
Abstract
Accurately distinguishing the benign thyroid nodules (BTNs) and malignant thyroid nodules (MTNs) is crucial for treatment planning and prognosis. This study aims to explore the clinical value of artificial intelligence ultrasound-assisted diagnostic system (AI-UADS) in diagnosing the BTNs and MTNs. In this retrospective case-control study, a total of 161 TNs from 91 patients confirmed by surgical pathology were included. All nodules underwent ultrasound examinations and were evaluated by both senior ultrasonographers and AI-UADS, independently. Using surgical pathology as the gold standard, the diagnostic performance of the 2 methods was analyzed using receiver operating characteristic curves (including classify the nodules based on size, location, capsule relationships, and presence or absence of Hashimoto thyroiditis [HT]). The area under the curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of AI-UADS in the differential diagnosis of BTNs and MTNs were 0.933, 93.2%, 92.4%, 94.2%, 95.5%, and 90.3%, respectively, all of which were higher than ultrasonographers (0.821, 82.0%, 81.5%, 82.6%, 86.2%, and 77.0%, respectively) (all P < .05). Specifically, the accuracy in diagnosing nodules with a maximum diameter ≤10 mm (94.0% vs 80.7%) and in the left lobe (92.5% vs 81.3%); the accuracy, sensitivity, specificity, positive predictive value and negative predictive value in diagnosing nodules with far from the capsule ([93.8% vs 79.5%], [92.5% vs 75.5%], [94.9% vs 83.1%], [94.2% vs 80.0%], [93.3% vs 79.0%]) and without HT ([94.0% vs 82.7%], [93.2% vs 82.4%], [94.9% vs 83.1%], [95.8% vs 85.9%], [91.8% vs 79.0%]) of AI-UADS were higher than ultrasonographers (all P < .05). The AI-UADS demonstrates good diagnostic performance in differentiating BTNs and MTNs, and can serve as an effective auxiliary tool for ultrasonographers. It shows particular advantages in diagnosing nodules with a maximum diameter ≤10 mm, located in the left lobe, far from the capsule, and in patients without HT.