CT-based explainable machine learning for predicting benign and malignant thyroid nodules: a multi-center study - He, Haijun; Luo, Mingquan; Hu, Kai; Ke, Tengfei; Yang, Juntao; Zi, Xinyue; Jiang, Tingting; Yong, Liangping; Chen, Tong; Chen, Jun; He, Zhengliang; Gao, Qiangrong; et al. (Frontiers in Oncology, 2025)
ObjectiveThis 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 methodsA retrospective study included 370 patients with thyroid nodules confirmed by pathology from three centers, divided into a training set (n = 229) and an internal validation set (n = 100) in a 7:3 ratio, with patients from the third center serving as an external validation set (n = 41). Radiomics features were extracted from preoperative CT images, and the optimal features were selected to construct a radiomics score (Rad_Score). Clinical risk factors were identified using univariate and multivariate logistic regression. LR and SVM algorithms were used to establish three models: a clinical model, an imaging model, and a combined model (integrating clinical factors and Rad_Score). The combined model was visualized using SHAP (SHapley Additive exPlanations) analysis. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).ResultsA 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.ConclusionThe 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.