Establishment and validation of a nomogram model for poorly differentiated thyroid cancer based on the Surveillance, Epidemiology, and End Result database.

PURPOSE: To retrospectively analyze the clinicopathological characteristics and prognosis-related factors of poorly differentiated thyroid cancer (PDTC), construct a nomogram model, and predict the 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS).

METHODS

PDTC patients between 1975 and 2020 from the Surveillance, Epidemiology, and End Result Program were divided into training (70%) and validation (30%) sets. Univariate and multivariate Cox regression analyses identified independent prognostic factors for PDTC, and a nomogram prognostic model was constructed. The predictive ability of the model was assessed using the receiver operating characteristic curve, decision curve analysis, and calibration curve.

RESULTS

A total of 983 PDTC patients were enrolled. Univariate and multivariate Cox regression analyses showed that age, gender, malignant tumor behavior, time from diagnosis to treatment, tumor size, treatment modality, N and M stages were independent prognostic factors for OS. For CSS, age, malignant tumor behavior, time from diagnosis to treatment, treatment modality, regional nodes examined, N and M stages were identified as independent prognostic factors. The nomogram model constructed based on these factors exhibited good predictive ability, clinical applicability, and calibration accuracy.

CONCLUSION

The nomogram model constructed based on clinicopathological characteristics such as age, gender, malignant tumor behavior, time from diagnosis to treatment, tumor size, treatment modality, N and M stages can effectively predict the OS of PDTC patients. Age, malignant tumor behavior, time from diagnosis to treatment, treatment modality, regional nodes examined, N and M stages can effectively predict the CSS.