Risk prediction of second primary malignant tumor in primary differentiated thyroid cancer patients: a population-based study

Abstract

Purpose: To investigate the risk factors of second primary malignant tumor (SPMT) in patients with differentiated thyroid cancer (DTC) and establish a competing risk nomogram to predict the probability of SPMT occurrence.

Methods: We retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database for patients diagnosed with DTC between 2000 and 2019. The Fine and Gray subdistribution hazard model was employed to identify SPMT risk factors in the training set and develop a competing risk nomogram. Model evaluation was performed using area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

Results: A total of 112,257 eligible patients were included in the study and randomized into a training set (n = 112,256) and a validation set (n = 33,678). The cumulative incidence rate of SPMT was 15% (n = 9528). Age, sex, race, tumor multifocality, and TNM stage were independent risk factors of SPMT. The calibration plots showed good agreement between the predicted and observed SPMT risks. The 10-year AUCs of the calibration plots were 70.2 (68.7-71.6) in the training set and 70.2 (68.7-71.5) in the validation set. Moreover, DCA showed that our proposed model resulted in higher net benefits within a defined range of risk thresholds. The cumulative incidence rate of SPMT differed among risk groups, classified according to nomogram risk scores.

Conclusion: The competing risk nomogram developed in this study exhibits high performance in predicting the occurrence of SPMT in patients with DTC. These findings may help clinicians identify patients at distinct levels of risk of SPMT and develop corresponding clinical management strategies.

Keywords: Competing risk nomogram; Differentiated thyroid cancer; SEER database; Second primary malignant tumor.