Sonographic characteristics and clinical characteristics combined with nomogram for predicting the aggressiveness of papillary thyroid carcinoma coexisted with Hashimoto’s thyroiditis

Abstract

Objective

The association between Papillary Thyroid Carcinoma (PTC) and coexistent Hashimoto’s Thyroiditis (HT) was controversial. The purpose of this study was to evaluate the presence of HT exerts any influence on the aggressiveness of PTC, and to establish a nomogram for predicting the possibility of aggressiveness in PTC.

Methods

373 consecutive PTC patients with/without coexistent HT from January 2017 to December 2020 were retrospective reviewed. Patients’ clinicopathologic and sonographic characteristics were collected for univariate and multivariate analyses. A nomogram was established based on the risk factors for aggressiveness in PTC.

Results

Male (p = 0.001), tumor size >1.0 cm (p = 0.046) and lymph node metastasis (p = 0.018) were negatively associated with PTC coexisted with HT, while it was significantly positively associated with the frequence of multifocality (p = 0.010). Univariate and multivariate analyses suggested that age ≥55 years (p = 0.000), male (p = 0.027), HT (p = 0.017), tumor size >1.0 cm (p = 0.015), multifocality (p = 0.041), distance to capsular ≤0 cm (p = 0.050) and blood flow (Grade I: p = 0.044) were independent risk factors for predicting the aggressiveness in PTC. A nomogram according to these predictors was further developed and validated. The receiver operating characteristic curve (AUC = 0.734 and 0.809 for training and validation cohorts, respectively) and decision curve analyses indicated that the nomogram model was clinically useful. The calibration curve revealed that the nomogram exhibited an excellent consistency.

Conclusions

In this study, the coexistent HT might play a protective role in preventing the proliferation of PTC. Dispensable aggressive treatment may be reduced in PTC by pre-operative identification of sonographic and clinical characteristics and incorporating with the predicted nomogram model.