Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer
- Resource Type
- article
- Authors
- Yijun Wu; Ke Rao; Jianghao Liu; Chang Han; Liang Gong; Yuming Chong; Ziwen Liu; Xiequn Xu
- Source
- Frontiers in Endocrinology, Vol 11 (2020)
- Subject
- machine learning
cross-validation
central lymph node metastasis
papillary thyroid cancer
feature selection
Diseases of the endocrine glands. Clinical endocrinology
RC648-665
- Language
- English
- ISSN
- 1664-2392
BackgroundCentral lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic central lymph node dissection is still controversial. There are no reliable models for predicting CLNM. This study aimed to develop predictive models for CLNM by machine learning (ML) algorithms.MethodsPatients with PTC who underwent initial thyroid resection at our hospital between January 2018 and December 2019 were enrolled. A total of 22 variables, including clinical characteristics and ultrasonography (US) features, were used for conventional univariate and multivariate analysis and to construct ML-based models. A 5-fold cross validation strategy was used for validation and a feature selection approach was applied to identify risk factors.ResultsThe areas under the receiver operating characteristic curve (AUC) of 7 models ranged from 0.680 to 0.731. All models performed significantly better than US (AUC=0.623) in predicting CLNM (P 1.1 cm were the most contributing predictors for CLNM.ConclusionsIt is feasible to develop predictive models of CLNM in PTC patients by incorporating clinical characteristics and US features. The ML algorithm may be a useful tool for the prediction of lymph node metastasis in thyroid cancer.