A Machine Learning-Based Model for Preoperative Assessment and Malignancy Prediction in Patients with Atypia of Undetermined Significance Thyroid Nodules

<b>Objectives:</b> The aim of this study was to investigate the preoperative clinical and hematologic variables, including the neutrophil-to-lymphocyte ratio (NLR), that can be used to predict malignancy in patients with atypia of undetermined significance (AUS) thyroid nodules; we further aimed to develop a machine learning-based prediction model. <b>Methods:</b> We enrolled 280 patients who underwent surgery for AUS nodules at the Wonju Severance Christian Hospital between 2018 and 2022. A logistic regression-based model was trained and tested using cross-validation, with the performance evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC). <b>Results:</b> Among the 280 patients, 116 (41.4%) were confirmed to have thyroid malignancies. Independent predictors of malignancy included age, tumor size, and the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification, particularly in patients under 55 years of age. The addition of NLR to these predictors significantly improved the malignancy prediction accuracy in this subgroup. <b>Conclusions:</b> Incorporating NLR into preoperative assessments provides a cost-effective, accessible tool for refining surgical decision making in younger patients with AUS nodules.