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Updated: Aug 24, 2025
A Personalized 3D-Printed Model for Preoperative Evaluation in Thyroid Surgery
Published on: February 17, 2023
Preoperative prognostic risk stratification model for papillary thyroid carcinoma based on clinical and ultrasound
Keyu Shen1, Siqi Xiao1, Xianji Wu1
1Department of Thyroid Surgery, The China-Japan Union Hospital of Jilin University, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, Changchun, China.
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 patients with low-risk and high-risk 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. Data including basic information, preoperative ultrasound characteristics, thyroid function, and postoperative pathology characteristics were obtained. Univariate logistic regression analysis and 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 regression analysis.
Results:
A total of 1,875 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 thyroid peroxidase (TPO) antibody level) 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.
