AUTHOR=Zhang Huan , Zheng Xiangqian , Liu Juntian , Gao Ming , Qian Biyun 

TITLE=Development of an Active Surveillance or Surgery Model to Predict Lymph Node Metastasis in cN0 Papillary Thyroid Microcarcinoma

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.896121

DOI=10.3389/fendo.2022.896121

ISSN=1664-2392

ABSTRACT=Objective:
Involvement of multiple lymph node (LN) metastasis in papillary thyroid microcarcinoma (PTMC) may indicate a progressive disease. To assist treatment decision, we conducted a clinical study to develop and validate a prediction model for preoperative evaluation of LN metastasis involving more than 5 lymph nodes in patients with clinical N0 (cN0) PTMC.
Material and Methods:
Using data from 6337 patients with cN0 PTMCs at Tianjin Medical University Cancer Institute and Hospital from 2013 to 2017, we identified and integrated risk factors for prediction of multiple LN metastasis to build a nomogram. The predictive accuracy and discriminative ability of nomogram were evaluated by concordance index (C-index) and calibration curve. The model was validated using bootstrap resampling of training cohort and an independent temporal validation cohort at the same institution. 
Results:
In the training cohort (n=3209 patients), 6 independent risk factors were identified and included the prediction model (PTMC Active Surveillance or Surgery (ASOS) Model), including age, gender, multifocality, tumor size, calcification, and aspect ratio. The PTMC ASOS model was validated both internally and through the temporal validation cohort (n=3128 patients) from the same institute. C-index of the prediction model in the training cohort was 0.769 (95%CI, 0.698-0.838), in the internal validation and external validation cohort were 0.768 and 0.771, respectively. The area under the receiver operating characteristic curve (AUC) was 0.7068 and 0.6799. The calibration curve for probability of large LN metastasis showed good agreement between prediction by nomogram and actual observation. DCA curve were used for comparation with another model, IDI and NRI were also calculated. The cut off value of our model was obtained by ROC curve. Based on this model and cut point, a web-based dynamic nomogram was developed (https://tjmuch-thyroid.shinyapps.io/PTMCASOSM/). 
Conclusion:
We established a novel nomogram that can help to distinguish pre-operatively cN0 PTMC patients with or without metastasis of multiple lymph nodes. This clinical prediction model may be used in decision making both for active surveillance and surgery.