Trends in incidence, mortality, and conditional survival of anaplastic thyroid cancer over the last two decades in the USA.

BACKGROUND

Anaplastic thyroid carcinoma (ATC) is a highly aggressive malignancy, and there is currently a lack of up-to-date epidemiological data. Traditional survival analysis fails to capture the dynamic changes in prognosis for long-term survivors, while conditional survival (CS) analysis, a critical tool for adaptive risk stratification, remains underexplored in ATC.

METHODS

Patients diagnosed with ATC between 2000 and 2021 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Temporal trends in age-adjusted incidence and incidence-based mortality were analyzed using Joinpoint regression to calculate annual percentage changes (APCs) with 95% confidence intervals (CIs). Overall survival (OS) was estimated using the Kaplan-Meier method. CS rates were calculated using the formula: CS(y/x) = OS(y+x)/OS(x). Prognostic factors were identified using Best Subset Regression (BSR), LASSO, and univariate and multivariate Cox regression analyses, and these factors were incorporated into a CS-nomogram model. The predictive performance of the model was validated using evaluation metrics, including the area under the receiver operating characteristic curve (AUC). Point values were assigned to the model's predictive factors, and a risk stratification system was developed based on the optimal threshold of the total score.

RESULTS

From 2000 to 2021, the age-adjusted incidence of ATC increased from 0.066 to 0.077 per 100,000 (APC: 2.308%, 95% CI: 1.187-3.441), peaking at 0.119 in 2018. Mortality trends paralleled this rise, with age-adjusted mortality increasing from 0.037 to 0.051 per 100,000 (APC: 2.380%, 95% CI: 1.129-3.646). CS analysis demonstrated a progressive increase in survival rates over time, with the 24-month cumulative survival rate rising from 14.0% to 93.8%, with the most pronounced temporal changes observed in patients with distant disease. Prognostic factors identified through BSR, LASSO, and Cox regression included age, SEER stage, and treatment. A novel CS-nomogram was successfully developed and validated for dynamic real-time survival prediction, enabling identification of high- and low-risk patient groups.

CONCLUSION

The incidence and incidence-based mortality of ATC have increased over the past few decades. The CS rates of ATC patients have dynamically improved over time. The CS-nomogram, integrating age, SEER stage, and treatment, provides clinicians with a personalized, dynamic, and real-time survival prediction tool that helps alleviate survivors' psychological distress, reduces anxiety, and optimizes precision follow-up strategies.