AUTHOR=Zhao Lingqian , Hu Tao , Cai Yuan , Zhou Tianhan , Zhang Wenhao , Wu Fan , Zhang Yu , Luo Dingcun 

TITLE=Preoperative risk stratification for patients with ≤ 1 cm papillary thyroid carcinomas based on preoperative blood inflammatory markers: construction of a dynamic predictive model

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1254124

DOI=10.3389/fendo.2023.1254124

ISSN=1664-2392

ABSTRACT=Objective: The aim of this study was to investigate the relationships and predictive value of preoperative peripheral blood inflammatory markers as a means by which to assess risk for patients with ≤ 1 cm papillary thyroid carcinomas (PTCs). In addition, a preoperative risk stratification predictive model was constructed and validated.
Methods: Clinical and pathologic data, as well as preoperative blood specimens, were collected from patients who underwent initial thyroid cancer surgery at the First People’s Hospital affiliated with the Zhejiang University School of Medicine, from January 2014 to January 2023. Risk assessment was performed based on postoperative pathology according to the 2015 ATA guidelines for recurrence risk stratification. Using univariate analysis and multivariate logistic regression, we identified independent risk factors associated with risk stratification. A predictive model was established and its discriminative and calibration abilities were validated. An independent validation dataset was used to verify the model, and the model was deployed as an online calculator.
Results: A total of 981 patients were included in the study, with 770 cases (78.5%) classified as low risk and 211 cases (21.5%) classified as intermediate to high risk. Through univariate analysis and multivariate logistic regression analysis, preoperative blood Neutrophil/Lymphocyte Ratio (NLR), gender, tumor diameter, and multifocality were identified as independent risk factors that distinguished between low and intermediate to high risk patients with ≤ 1 cm PTCs. The clinical predictive model exhibited an AUC of 0.785, specificity of 70.6%, and sensitivity of 76.8%. For the independent validation group of 345 patients, the AUC was 0.813, specificity was 70.4%, and sensitivity was 83.8%. Calibration curves and clinical decision curves demonstrated the model to have good calibration capacity.
Conclusion: A dynamic clinical predictive model based on preoperative blood NLR and clinical information for patients with ≤ 1 cm PTCs was established. The model is useful for preoperative risk assessment of patients with ≤ 1 cm PTCs.