A novel nomogram for identifying high-risk patients among active surveillance candidates with papillary thyroid microcarcinoma

Frontiers in Endocrinology

Endocrinology

Li Zhang¹  Shuai Xue²  Peisong Wang²  Kaixuan Li² 

[1] Department of Nephrology, The First Hospital of Jilin University, Changchun, China;General Surgery Center, Department of Thyroid Surgery, The First Hospital of Jilin University, Changchun, China;

关键词: active surveillance;    papillary thyroid microcarcinoma;    high risk;    high-volume lymph node metastasis;    extrathyroidal invasion;    aggressive variant;    predictive model;    nomogram;   

DOI  :  10.3389/fendo.2023.1185327

 received in 2023-03-13, accepted in 2023-08-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveActive surveillance (AS) has been recommended as the first-line treatment strategy for low-risk (LR) papillary thyroid microcarcinoma (PTMC) according to the guidelines. However, preoperative imaging and fine-needle aspiration could not rule out a small group of patients with aggressive PTMC with large-volume lymph node micro-metastasis, extrathryoidal invasion to surrounding soft tissue, or high-grade malignancy from the AS candidates.MethodsAmong 2,809 PTMC patients, 2,473 patients were enrolled in this study according to the inclusion criteria. Backward stepwise multivariate logistic regression analysis was used to filter clinical characteristics and ultrasound features to identify independent predictors of high-risk (HR) patients. A nomogram was developed and validated according to selected risk factors for the identification of an HR subgroup among “LR” PTMC patients before operation.ResultsFor identifying independent risk factors, multivariable logistic regression analysis was performed using the backward stepwise method and revealed that male sex [3.91 (2.58–5.92)], older age [0.94 (0.92–0.96)], largest tumor diameter [26.7 (10.57–69.22)], bilaterality [1.44 (1.01–2.3)], and multifocality [1.14 (1.01–2.26)] were independent predictors of the HR group. Based on these independent risk factors, a nomogram model was developed for predicting the probability of HR. The C index was 0.806 (95% CI, 0.765–0.847), which indicated satisfactory accuracy of the nomogram in predicting the probability of HR.ConclusionTaken together, we developed and validated a nomogram model to predict HR of PTMC, which could be useful for patient counseling and facilitating treatment-related decision-making.

【 授权许可】

Unknown   
Copyright © 2023 Zhang, Wang, Li and Xue