Web-based prediction models for predicting overall survival and cancer specific survival in lung metastasis of patients with thyroid cancer: a study based on the SEER database and a Chinese cohort

Background: The current high incidence of thyroid cancer (TC) is usually accompanied by poor prognosis of patients who also develop lung metastasis. Therefore, the present study aimed to develop a survival prediction model to guide clinical decision-making. Methods: This study retrospectively analyzed 679 patients with TCLM from 2010 to 2015 using the Surveillance, Epidemiology, and End Results (SEER) database. The external validation cohort consisted of 48 patients from Tianjin Medical University General Hospital (TMUGP) and Tianjin Cancer Hospital (TCH). Cox proportional risk regression models were used to analyze prognostic influences on patients and the screened variables were used to build the survival prediction models. The present study used the C-index, time-dependent ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the performance of the nomogram models. Results: The Cox proportional risk regression model analysis identified independent prognostic factors in patients with TCLM. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.813, cancer specific survival (CSS) was 0.822. The area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.884, 0.879 and 0.883. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.887, 0.885 and 0.886. The C-index, time-dependent ROC curve, calibration curve, and DCA for the training group, internal validation group, and external validation group showed that the Nomogram had a clear advantage. Conclusion: In this study, two new nomograms were constructed to predict the risk of TCLM patients. The nomograms can be applied in clinical practice to help clinicians assess patient prognosis.