Abstract
OBJECTIVES: This study aimed to develop a model for predicting peripheral lymph node metastasis (LNM) in thyroid cancer patients by combining enhanced CT radiomic features with machine learning algorithms. It increased the clinical utility and interpretability of the predictions through SHAP (SHapley Additive exPlanation) values and nomograms for model explanation and visualization. METHODS: Clinical and enhanced CT image data from 375 patients with thyroid cancer confirmed by postoperative pathology at Xiangyang No. 1 People's Hospital were collected from January 2015 to July 2023. Among them, there were 88 patients in the LNM group and 287 patients in the non-LNM group. The delta radiomic features of the tumours were extracted. Various machine learning algorithms (such as SVM, GBM, RF, XGBoost, KNN, and LightGBM) were trained on the clinical and radiomic feature data sets and used to construct a reliable prediction model. During model training, cross-validation was used to evaluate model performance, and the optimal model was selected. In addition, SHAP values were used to interpret the prediction results of the optimal model, analyse the contribution of each feature to the prediction results, and further develop a nomogram to visually display the prediction results. RESULTS: Univariate analysis confirmed that sex, Hashimoto's disease, tumour adjacency to the thyroid capsule, pathological subtype, Delta Radscore, and Radscore 1 are risk factors for peripheral lymph node metastasis in thyroid cancer patients. The machine learning model based on enhanced CT radiomics performed well in predicting peripheral lymph node metastasis in thyroid cancer patients. In the test set, the optimal model, SVM, achieved high AUC (0.879), sensitivity (0.849), and specificity (0.769) values. Through SHAP value analysis, the importance and contribution of tumour adjacency to the thyroid capsule, pathological subtype, Delta Radscore, and Radscore 1 in the prediction were clarified, providing a more detailed and intuitive basis for clinical decision-making. The nomogram illustrated the model prediction process, facilitating understanding and application by clinicians. CONCLUSIONS: This study successfully constructed a model for predicting peripheral lymph node metastasis in thyroid cancer patients on the basis of enhanced CT radiomics combined with machine learning and improved the interpretability and clinical utility of the model through SHAP values and nomograms. The model not only improves the accuracy of predictions but also provides a more scientific and intuitive basis for clinical decision-making, with potential clinical application value.