超音波特徴量のデジタル化による甲状腺結節の分類における解釈可能な機械学習モデル

Purpose To develop a digitized integrated feature-based interpretable machine learning classification model to accurately recognize complex thyroid nodules while efficiently diagnosing conventional thyroid nodules (thyroid nodules with typical benign or malignant ultrasound features). Materials and Methods Thyroid ultrasound images with pathologically confirmed nodules were retrospectively collected from seven medical centers in China (January 2011 to December 2021). An interpretable classification model consisting of two independent masks was developed and defined as UltraMC. The front-end network was trained to identify conventional thyroid nodules using four digitized features, and the back-end network collected nodules classified as benign in the previous framework for secondary analysis to clarify their final diagnosis. UltraMC performance was evaluated using accuracy, sensitivity, specificity and confusion matrices. Results The total dataset included 73826 patients with thyroid ultrasound images (mean age, 45.56 ± [SD] 11.21years; 54398 female). Diagnostic accuracy of the front-end network for detecting conventional thyroid nodules was 92.9% (13718/14765), and accuracy of the back-end network for classifying mummified thyroid nodules (MTNs) was 88.5% (652/737). The overall diagnostic accuracy of the ultrasound MTN classification model (UltraMC) was 91.8% (14228/15502). The areas under the receiver operating characteristic curve of the front-end network and UltraMC in identifying conventional thyroid nodules were 0.98 (95% CI 0.98-0.98) and 0.96 (95% CI 0.96-0.97), respectively. Conclusion The proposed two-layer interpretable classification model achieved high diagnostic accuracy for both conventional and mummified thyroid nodules. These findings demonstrate that digitized ultrasound features integrated into a white-box framework can effectively support classification of complex thyroid nodules. ©RSNA, 2026.