已发表论文

一种预测甲状腺乳头状癌中心淋巴结转移的异质集成算法模型

 

Authors Liu W, Wang S, Xia X, Guo M 

Received 15 March 2022

Accepted for publication 29 April 2022

Published 6 May 2022 Volume 2022:15 Pages 4717—4732

DOI https://doi.org/10.2147/IJGM.S365725

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Scott Fraser

Purpose: To develop a heterogeneous ensemble algorithm model to precisely predict central lymph node metastasis (CLNM), which can provide a reference value on controversial topics of performing prophylactic central lymph node dissection for patients with papillary thyroid cancer (PTC).
Methods: The study included patients with PTC who underwent an initial thyroid resection in a single-center medical institution between January 2014 and December 2018. A total of 18 variables, including clinical features and ultrasound (US) features, were used in the univariate analysis, multivariate analysis, and feature selection and were also used to develop a heterogeneous ensemble model based on five basic machine learning models, including extreme gradient boosting, k-nearest neighbors, random forest, gradient boosting, and AdaBoost. Moreover, a partial dependent plot was used to explain the heterogeneous ensemble model.
Results: The area under the receiver operating characteristic curve of the heterogeneous ensemble algorithm model was 0.67, which is significantly better than that of the basic machine models in predicting CLNM. All machine learning models performed better than US. Based on multivariate analysis and receiver operating characteristic curve analysis, age ≤ 33 years, tumor size ≥ 0.8 cm, US-suspected CLNM, and microcalcification were risk factors for CLNM, and anti-thyroid peroxidase antibody and serum thyroglobulin levels were favorable factors for CLNM.
Conclusion: The proposed heterogeneous ensemble algorithm model may be optimal tool to predict CLNM by integrating clinical and US features.
Keywords: heterogeneous ensemble algorithm model, central lymph node metastasis, papillary thyroid cancer, ultrasound, machine learning model