Frontiers in endocrinology · 2025
Machine learning-based quantification of overall and internal ultrasound characteristics for diagnosing malignant partially cystic thyroid nodules.
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Zhang Yutong, Jiang Jue, Chen Aqian, Zhang Dong, Wang Lirong, Yuan Xin, He Xin, Yu Shanshan, Wang Juan, Zhou Qi
Аннотация
INTRODUCTION: Partially cystic thyroid nodules (PCTNs) with malignant potential are frequently underestimated due to limited recognition of their sonographic characteristics.
METHODS: This retrospective analysis included 486 PCTNs identified between March 2021 and September 2022. Machine learning (ML) was employed to quantitatively evaluate the overall ultrasound characteristics of the whole nodule as well as the internal ultrasound characteristics of its solid part. Three diagnostic models were constructed based on different sets of ultrasound data. The dataset was split into training and testing subsets at a 7:3 ratio. Key ultrasound characteristics such as marked hypoechogenicity, calcifications, solid component≥50%, and unclear internal margins were emphasized.
RESULTS: Among the models, the integrated one- incorporating both overall-nodule and internal solid-part characteristics-achieved superior diagnostic performance, with an area under the curve (AUC) of 0.96 (0.93-0.99) on the test data. The model demonstrated an accuracy of 0.91 (0.85-0.95), a sensitivity of 0.88 (0.73-0.97), a specificity of 0.92 (0.85-0.96), a negative predictive value of 0.96 (0.91-0.99), and a positive predictive value of 0.77 (0.61-0.89). This comprehensive model significantly outperformed the model utilizing only overall nodule characteristics (AUC = 0.85, P = 2.35e-6), and demonstrated comparable effectiveness to the model based solely on internal characteristics (AUC = 0.93, P = 1.01e-1).
DISCUSSION: The results support the clinical utility of an ML-driven approach that integrates comprehensive ultrasound metrics for the reliable identification of malignant PCTNs.
METHODS: This retrospective analysis included 486 PCTNs identified between March 2021 and September 2022. Machine learning (ML) was employed to quantitatively evaluate the overall ultrasound characteristics of the whole nodule as well as the internal ultrasound characteristics of its solid part. Three diagnostic models were constructed based on different sets of ultrasound data. The dataset was split into training and testing subsets at a 7:3 ratio. Key ultrasound characteristics such as marked hypoechogenicity, calcifications, solid component≥50%, and unclear internal margins were emphasized.
RESULTS: Among the models, the integrated one- incorporating both overall-nodule and internal solid-part characteristics-achieved superior diagnostic performance, with an area under the curve (AUC) of 0.96 (0.93-0.99) on the test data. The model demonstrated an accuracy of 0.91 (0.85-0.95), a sensitivity of 0.88 (0.73-0.97), a specificity of 0.92 (0.85-0.96), a negative predictive value of 0.96 (0.91-0.99), and a positive predictive value of 0.77 (0.61-0.89). This comprehensive model significantly outperformed the model utilizing only overall nodule characteristics (AUC = 0.85, P = 2.35e-6), and demonstrated comparable effectiveness to the model based solely on internal characteristics (AUC = 0.93, P = 1.01e-1).
DISCUSSION: The results support the clinical utility of an ML-driven approach that integrates comprehensive ultrasound metrics for the reliable identification of malignant PCTNs.