AUTHOR=Shen Keyu , Xiao Siqi , Wu Xianji , Zhang Guang 

TITLE=Preoperative prognostic risk stratification model for papillary thyroid carcinoma based on clinical and ultrasound characteristics

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.1025739

DOI=10.3389/fendo.2022.1025739

ISSN=1664-2392

ABSTRACT=Background: The preoperative risk stratification for patients with papillary thyroid carcinoma (PTC) plays a crucial role in guiding individualized treatment. We aim to construct a predictive model that aids in distinguishing between low-risk and high-risk patients with PTC based on preoperative clinical and ultrasound characteristics.
Materials and Methods: Patients who underwent open surgery and were diagnosed with PTC via a postoperative pathological report between January 2020 and December 2020 were retrospectively reviewed. The data including basic information, preoperative ultrasound characteristics, thyroid function, and postoperative pathology characteristics were obtained. Univariate logistic analysis and the Least absolute shrinkage and selection operator regression analysis were performed to screen candidate variables. Finally, the preoperative predictive model for PTC was established based on the results of the multivariate logistic analysis.
Results: A total of 1875 patients with PTC were enrolled. Eight variables (sex, age, number of foci, maximum tumor diameter on ultrasound, calcification, capsule, lymph node status on ultrasound, and TPO antibody) significantly associated with risk stratification were included in the predictive model. A nomogram was constructed for clinical utility. The model showed good discrimination and the area under the curve was 0.777 (95% confidence interval (CI): 0.752–0.803) and 0.769 (95%CI: 0.729–0.809) in the training set and validation set, respectively. The calibration curve exhibited a rather good consistency with the perfect prediction. Furthermore, decision curve analysis and clinical impact curve showed that the model had good efficacy in predicting the prognostic risk of PTC.
Conclusions: The nomogram model based on preoperative indicators for predicting the prognostic stratification of PTC showed a good predictive value. This could aid surgeons in deciding on individualized precision treatments.