AUTHOR=Xue Yuxiang , Zhuang Yizhen , Chen Shengxiang 

TITLE=Dynamic survival outcomes of the tall-cell variant of papillary thyroid carcinoma patients after surgery

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1517907

DOI=10.3389/fendo.2025.1517907

ISSN=1664-2392

ABSTRACT=BackgroundTall cell variant (TCV) represents the predominant aggressive subtype of papillary thyroid carcinoma (PTC). This study aimed to precisely characterize the evolving survival prognosis of these patients using extensive long-term follow-up data from a large cohort.MethodsUtilizing the Surveillance, Epidemiology, and End Results (SEER) database, a cohort of 1004 eligible TCV patients diagnosed from 2004 to 2016 were included in this investigation. Conditional survival (CS) analysis was used to describe the evolving nature of survival changes for long-term TCV survivors. Following this, the cohort was divided into training and validation sets using a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) model was utilized to identify prognostically significant factors, which were subsequently integrated to construct a CS-nomogram model. Multiple evaluation methods, including calibration curves, the area under the receiver operating characteristic (ROC) curve, C-index, and decision curve analysis (DCA), were employed to assess the performance of this model.ResultsAmong included patients, the Kaplan–Meier method estimated a 10-year OS rate at diagnosis of 85%. In contrast, the CS analysis revealed annual increases, with survival rates improving from 85% at the initial diagnosis to 88%, 90%, 91%, 92%, 94%, 95%, 97%, 99%, and 99% for patients surviving 1 to 9 years after diagnosis, respectively. Through LASSO regression analysis, this study identified age, sex, N status, M status, AJCC stage, tumor size, surgery and radioactive iodine as key predictors for developing the CS-based nomogram. Calibration curves, ROC curves, C-index values, and DCA further determined nomogram model’s effectiveness and reliability. Moreover, based on this CS-nomogram, we calculated risk scores for each patient and used risk scores to categorized patients into high- and low-risk groups in both training and validation cohorts. The Kaplan-Meier analysis with log-rank tests further validated the prognostic discriminative power of our risk stratification.ConclusionsThe findings of our study comprehensively outlined the 10-year CS outcomes for TCV patients, revealing a steady increase in 10-year OS corresponding to each additional year of survival in TCV survivors. We also developed a CS-nomogram model, an individualized tool integrating time-varying covariates and patient-specific characteristics delivers real-time prognostic information tailored to each TCV patient.