Socioeconomic disparities and regional environment are associated with cervical lymph node metastases in children and adolescents with differentiated thyroid cancer: developing a web-based predictive model

Abstract

Purpose: To establish an online predictive model for the prediction of cervical lymph node metastasis (CLNM) in children and adolescents with differentiated thyroid cancer (caDTC). And analyze the impact between socioeconomic disparities, regional environment and CLNM.

Methods: We retrospectively analyzed clinicopathological and sociodemographic data of caDTC from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2019. Risk factors for CLNM in caDTC were analyzed using univariate and multivariate logistic regression (LR). And use the extreme gradient boosting (XGBoost) algorithm and other commonly used ML algorithms to build CLNM prediction models. Model performance assessment and visualization were performed using the area under the receiver operating characteristic (AUROC) curve and SHapley Additive exPlanations (SHAP).

Results: In addition to common risk factors, our study found that median household income and living regional were strongly associated with CLNM. Whether in the training set or the validation set, among the ML models constructed based on these variables, the XGBoost model has the best predictive performance. After 10-fold cross-validation, the prediction performance of the model can reach the best, and its best AUROC value is 0.766 (95%CI: 0.745-0.786) in the training set, 0.736 (95%CI: 0.670-0.802) in the validation set, and 0.733 (95%CI: 0.683-0.783) in the test set. Based on this XGBoost model combined with SHAP method, we constructed a web-base predictive system.

Conclusion: The online prediction model based on the XGBoost algorithm can dynamically estimate the risk probability of CLNM in caDTC, so as to provide patients with personalized treatment advice.

Keywords: cervical lymph node metastasis; children and adolescents with differentiated thyroid cancer; regional environment; socioeconomic disparities; web-based predictive model.

Figure 1

Performance comparison of XGBoost algorithm and other ML algorithms in predicting lymph node metastasis. (A, B) compare the performance of 8 different ML algorithms in building predictive models. Whether in the training set or the validation set, the XGBoost algorithm has the highest AUROC value and is the best predictive model. (C) is the calibration curve of the prediction model. The abscissa of the graph is the predicted probability, that is, the probability of the event occurrence is predicted by the prediction model. The ordinate is the actual probability, that is, the patient’s actual event rate. Each colored solid line is a fitted line, representing the actual value corresponding to the predicted value. If the predicted value is equal to the actual value, the solid line exactly coincides with the diagonal dashed line. (D) shows the decision curve analysis of each model. The results of the study showed that the population using the ML model benefited well. ML, Machine learning; XGBoost, Extreme gradient boosting; AUROC, Area under the receiver operating characteristic.

Figure 2

Construction of LNM prediction model based on XGBoost algorithm and SHAP method. (A–D) show the optimization process of the XGBoost model based on 10-fold cross-validation. When the learning curves of the training set and validation set converge (C), the prediction performance of the XGBoost model is the best at this time. (E) is the calibration curve based on the XGBoost model. (F) shows SHAP based on the XGBoost model. XGBoost, Extreme gradient boosting; ROC, Receiver operating characteristic; SHAP, Shapley Additive exPlanations.

Figure 3

Web-based visual risk prediction model page for CLNM in caDTC.