Nomogram prediction for the involution of the ablation zone after radiofrequency ablation treatment in patients with low-risk papillary thyroid carcinoma

Abstract

Objectives

To construct a prognostic nomogram to predict the involution of the ablation zone for patients with low-risk papillary thyroid carcinoma (PTC) who underwent radiofrequency ablation (RFA) treatment.

Methods

Data from 204 patients with low-risk PTC without extrathyroidal extension or cervical lymph node or distant metastasis who underwent RFA treatment were collected from January 2018 to January 2019. Clinicopathological and imaging characteristics were analyzed. The prognostic factors associated with the involution of the ablation zone within 12 months after RFA were identified by logistic analysis, and the nomogram was established. Calibration curve and decision curve analysis were used to evaluate the nomogram performance.

Results

Of the 204 patients included in this study, the ablation zone in 78 (38%) patients did not completely disappear in the 12 months after RFA. Four variables, including sex (odds ratio [OR], 3.303; 95% confidence interval [CI], 1.418–8.418; p = 0.008), age (OR, 1.045; 95% CI, 1.012–1.081; p = 0.009), calcification size (OR, 1.666; 95% CI, 1.041–2.701; p = 0.035), and RFA energy (OR, 2.902; 95% CI, 1.333–6.683; p = 0.009), were found to be closely associated with ablation zone non-disappearance at 12 months after RFA by multivariate analysis. A nomogram model was constructed, and its accuracy was well validated (C-index = 0.787).

Conclusions

This study constructed and validated a risk model that could accurately predict the involution of the ablation zone after RFA for patients with PTC. This could provide clinicians with useful resource to guide patient counseling regarding tumor prognosis after RFA.

Keywords:

• Papillary thyroid carcinoma
• radiofrequency ablation
• nomogram
• prognosis
• risk factors

Ethical approval

This study was conducted with approval from the Ethics Committee of the Chinese People’s Liberation Army General Hospital. The scientific guarantor of this publication is Yukun Luo.

Disclosure statement

No potential conflict of interest was reported by the author(s). All authors declare that they have no relationship with any companies whose products or services may be related to the subject matter of the study.

Data availability statement

The raw data supporting the conclusions of this study are not publicly available but are available from the corresponding author with reasonable request.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [Grant no. 81771834] and the Medical Big Data and AI R&D Project of Chinese PLA General Hospital [2019MBD-040].