Risk factors and predictive model for recurrence in papillary thyroid carcinoma: a single-center retrospective cohort study based on 955 cases - Li, Yin; Tian, Jiahe; Jiang, Ke; Wang, Zhongyu; Gao, Songbo; Wei, Keyang; Yang, Ankui; Li, Qiuli (Frontiers in Endocrinology, 2023)
Background The 2015 American Thyroid Association guidelines proposed recurrence risk stratification of differentiated thyroid carcinoma, including papillary thyroid carcinoma (PTC), but this stratification excluded non–initial treatment patients with worse outcomes. This study aimed to explore the potential risk factors for recurrence in PTC and develop a predictive model for both initial and non–initial treatment of patients with PTC. Methods A total of 955 patients were included in this study. Differences between the recurrence (−) and recurrence (+) groups were compared. The 955 patients were randomized into two groups: the training group (671 cases) and the validation group (284 cases). All variables were selected using the LASSO regression analysis. A nomogram was developed based on the results of the univariate and multivariate logistic regression analyses. The nomogram performance was evaluated using discrimination and calibration. Results Patients aged ≥55 years, extranodal extension (ENE), metastatic LN ratio (LNR) >0.5, and non–initial treatment were identified as potential risk factors for recurrence through LASSO regression and univariate and multivariate analyses. The receiver operating characteristic curve (ROC curve) showed high efficiency, with an area under the ROC curve (AUC) of 0.819 (95% confidence interval [CI], 0.729–0.909) and 0.818 (95% CI, 0.670–0.909) in the training and validation groups, respectively. The calibration curve indicated that the nomogram had a good consistency. Conclusion In patients with PTC, age ≥55 years, ENE, LNR >0.5, and non–initial treatment are potential risk factors for recurrence. The predictive model of recurrence was confirmed to be a practical and convenient tool for clinicians to accurately predict PTC recurrence.