A Proposed Heterogeneous Ensemble Algorithm Model for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer

Wenfei Liu,* Shoufei Wang,* Xiaotian Xia, Minggao Guo Department of Thyroid, Parathyroid, Breast and Hernia Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaotian Xia; Minggao Guo, Department of Thyroid, Parathyroid, Breast and Hernia Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, No. 600 Yishan Road, Shanghai, People’s Republic of China, Tel +8618930172917 ; +8618930172912, Email 18930172917@163.com; guominggao203@163.comPurpose: 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