Better together: what can state-of-the-art ML models add to thyroid nodule diagnostics?
Machine learning model markedly improves malignancy prediction in indeterminate thyroid nodules
Better together: what can state-of-the-art ML models add to thyroid nodule diagnostics?
Thyroid nodules are common, but most are benign.
To develop and validate machine learning models that integrate ACR TI-RADS and Bethesda cytology categories for improved prediction of thyroid nodule malignancy.
We retrospectively analyzed 384 adult patients undergoing thyroid surgery with complete preoperative data on ACR TI-RADS category, Bethesda cytology, nodule size, age, and sex.
Preoperative stratification is critical, especially for cytologically indeterminate cases (Bethesda III–IV), which often lead to unnecessary surgery.
While ACR TI-RADS ultrasound scoring and Bethesda cytology independently aid risk assessment, each has limited diagnostic power alone.
Machine learning (machine learning) offers a potential solution by integrating multi-dimensional inputs.
The full LightGBM model achieved excellent discrimination (AUC = 0.96), outperforming models using only Bethesda (AUC = 0.91), TI-RADS (AUC = 0.78), or both combined (AUC = 0.93).
Prospective multicenter validation is warranted before clinical implementation.