Conditional survival and prognostic nomogram for distant metastatic differentiated thyroid cancer after total...

The long-term prognosis of patients with distant metastasis of differentiated thyroid cancer (DM-DTC) after total thyroidectomy remains poorly defined. This study aims to assess the conditional survival (CS) of DM-DTC patients following total thyroidectomy and to analyze the key prognostic factors, with the objective of developing a more accurate survival prediction tool for clinical application. We retrospectively analyzed data from patients diagnosed with DM-DTC who underwent total thyroidectomy between 2004 and 2019, retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. 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), where CS(y/x) represents the probability of surviving an additional y years after already surviving x years. Prognostic factors were identified using the least absolute shrinkage and selection operator (LASSO) regression and incorporated into a CS-nomogram model developed through multivariate Cox regression. The CS-nomogram was validated, and predictive factors were assigned point values. A risk stratification system was subsequently established using the optimal threshold of the total score. A total of 1,235 patients with DM-DTC who underwent total thyroidectomy were included, with a median follow-up of 51 months. Kaplan-Meier survival analysis revealed 3-year, 5-year, and 10-year OS rates of 81.7%, 70.4%, and 46.9%, respectively. CS analysis demonstrated a progressive increase in survival rates over time. The 10-year cumulative survival rate increased from 46.9% to 51.1%, 54.4%, 57.5%, 61.5%, 66.7%, 72.3%, 79.6%, and 85.5%, ultimately reaching 93.5% after surviving 1 to 9 years. Prognostic factors identified through LASSO regression and multivariate Cox regression analysis included age, sex, histological type, tumor size, and radioactive iodine therapy. A novel CS-nomogram for dynamic real-time survival prediction was successfully developed and validated, enabling identification of high- and low-risk patient groups. This study represents the first evaluation of CS patterns in DM-DTC patients following total thyroidectomy, demonstrating a gradual improvement in postoperative survival rates over time. A novel CS-nomogram model was successfully developed and validated, offering clinicians a personalized, dynamic, and real-time survival prediction tool. The risk stratification system derived from this model effectively distinguishes high- and low-risk patients, providing valuable guidance for follow-up and risk assessment.