Prediction model for recurrent laryngeal nerve injury in microwave ablation of thyroid nodules

This study aims to develop an explainable machine learning (ML) framework integrating clinical, imaging, and procedural features for predicting recurrent laryngeal nerve (RLN) injury risk during microwave ablation (MWA) of thyroid nodules.

A retrospective study is conducted using data from 254 patients who underwent thyroid nodule MWA. Recursive feature elimination (RFE) with 5 × fivefold cross-validation selected 12 optimal features across four ML models: logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM). Model performance is evaluated via AUC, accuracy, sensitivity, specificity, Brier score, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) provides interpretability by quantifying feature contributions.

The RandomForest model achieved the highest discriminative performance (AUC = 0.9755 ± 0.0242, accuracy = 0.9729 ± 0.0155) and calibration (Brier score = 0.038). Key predictors included nodule volume, ablation time, and proximity to the tracheoesophageal groove. SHAP analysis revealed model-specific interactions: nodule orientation (SVM) and post-ablation dimensions (RF) significantly influenced risk. DCA confirmed clinical utility, with RF providing the highest net benefit across thresholds (0.2-0.6). Laboratory markers and echogenicity demonstrated cross-model consistency, underscoring their biological relevance.

This study establishes the first interpretable ML framework for RLN injury prediction in thyroid nodule MWA, highlighting the superiority of ensemble learning and clinical-imaging integration. Future validation in diverse cohorts and real-time integration into ablation systems may enhance procedural safety.