Model development to predict central lymph node metastasis in cN0 papillary thyroid microcarcinoma by machine learning

Yaocheng Yu; Zhiwei Yu; Mengxuan Li; Yidi Wang; Changjiao Yan; Jing Fan; Fei Xu; Huimin Meng; Jing Kong; Songpeng Li; Rui Ling; Ting Wang

doi:10.21037/atm-22-3594

Model development to predict central lymph node metastasis in cN0 papillary thyroid microcarcinoma by machine learning

Ann Transl Med. 2022 Aug;10(16):892. doi: 10.21037/atm-22-3594.

Authors

Yaocheng Yu^#¹, Zhiwei Yu^#¹, Mengxuan Li¹, Yidi Wang¹, Changjiao Yan¹, Jing Fan¹, Fei Xu¹, Huimin Meng¹, Jing Kong¹, Songpeng Li¹, Rui Ling¹, Ting Wang¹

Affiliation

¹ Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.

^# Contributed equally.

Abstract

Background: Whether prophylactic central lymph node dissection is necessary for cN0 papillary thyroid microcarcinoma (PTMC) patients remains highly debatable. Surgeons desperately need a way to help with surgical decision-making. While traditional predictive models can better explain changes in variables, machine learning (ML) models may have better predictive performance. This study aims to develop models for predicting the risk of central lymph node metastasis (CLNM) by utilizing ML algorithms.

Methods: The clinical records of 1,121 patients with cN0 PTMC who underwent initial thyroid resection at our hospital between January 2014 and December 2018 were retrospectively retrieved. Univariate and multivariate analyses were performed to examine risk factors associated with CLNM. Six ML algorithms for predicting CLNM were established and internally validated. Indices including the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to test the performance of the model.

Results: The results showed 33.5% (376 out of 1,121) of patients had CLNM. In multivariate logistic regression (LR) analyses, gender, age, tumor size, multifocal lesions, and extrathyroidal extension (ETE) were all independent predictors of CLNM. The AUROC predictive values of the six ML algorithms were between 0.664 and 0.794, with the random forest (RF) model performing the best with an AUROC of 0.794. Therefore, we used the RF model and uploaded the results to a web-based risk calculator to predict an individual's probability of CLNM (https://xijing-thyroid.shinyapps.io/ptmc_clnm).

Conclusions: Developing predictive models of CLNM in cN0 PTMC patients using the ML algorithm is a feasible method. Our online risk calculator based on the RF model may be a useful tool for surgical decisions.

Keywords: Thyroid cancer; central lymph node metastasis (CLNM); machine learning (ML); microcarcinoma; model.