CT-based explainable machine learning for predicting benign and malignant thyroid nodules: a multi-center study.

Objective: This study intends to construct a CT-based explainable machine learning model for preoperative prediction of thyroid nodule benignity or malignancy, aiming to provide a more accurate tool for clinical decision-making and management. Materials and methods: A retrospective study included 370 patients with thyroid nodules confirmed by pathology from three centers, divided into a training set ( Results: A total of 17 features were ultimately selected for Rad_Score calculation. The combined model demonstrated the best performance, with the LR combined model achieving AUC values of 0.962, 0.913, and 0.914 in the training set, internal validation set, and external validation set, respectively, all higher than the LR clinical model and LR radiomics model; and the LR combined model outperforms the SVM combined model (0.953, 0.885, and 0.842). The SHAP analysis revealed the relative importance of the key feature (Rad_score) in model prediction, enhancing model transparency. Conclusion: The combined model performs better under the LR algorithm. Combined with SHAP explainable analysis, it provides a non-invasive, efficient, and transparent tool for preoperative differentiation of benign and malignant thyroid nodules, potentially optimizing individualized clinical management.