A High-Quality Nomogram for Predicting Lung Metastasis in Newly Diagnosed Stage IV Thyroid Cancer: A Population-Based Study.

Abstract

Introduction: Lung metastasis (LM) implies a very dismal event in patients with thyroid cancer. We aimed to construct a nomogram to predict LM for newly diagnosed stage IV thyroid cancer. Methods: A total of 1407 stage IV thyroid cancer patients were gathered from the surveillance, epidemiology, and end results (SEER) database. Pearson's Chi-squared test or Fisher's exact test was used to identify LM-related factors, and logistic regression analysis was employed to identify independent risk parameters of LM, which were included to establish a nomogram model by R software. The discriminative ability and predictive accuracy of the nomogram were assessed using the area under the curve (AUC) and calibration plots. Cox regression analysis and Kaplan-Meier analysis were applied to evaluate the clinical utility of this model. A simulation trial was conducted to verify the health economic value of this nomogram in predicting TCLM. Results: Five variables were found to be independent risk predictors of LM, including grade, histology, N stage, bone metastasis, and liver metastasis. The results of the AUC and calibration curves demonstrated that the nomogram exhibited outstanding performance for predicting the risk of LM patients both internally and externally. The LM prediction risk was an independent prognostic factor for stage IV thyroid cancer patients [P = .009, hazard ratio (HR): 1.812, 95% CI: 1.163-2.824]. Conclusion: We successfully developed a predictive model for stage IV thyroid cancer, which provides important information for identifying patients at high risk of LM and implementing early preventive interventions to improve their outcomes.