Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer
| Autor: | Yijun Wu, Ke Rao, Jianghao Liu, Chang Han, Liang Gong, Yuming Chong, Ziwen Liu, Xiequn Xu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male 0301 basic medicine Multivariate analysis Endocrinology Diabetes and Metabolism 030209 endocrinology & metabolism Machine learning computer.software_genre cross-validation lcsh:Diseases of the endocrine glands. Clinical endocrinology Papillary thyroid cancer 03 medical and health sciences Endocrinology 0302 clinical medicine feature selection Risk Factors medicine Humans papillary thyroid cancer Thyroid Neoplasms Thyroid cancer Original Research Retrospective Studies lcsh:RC648-665 Receiver operating characteristic business.industry Thyroid Univariate Middle Aged Prognosis medicine.disease Central lymph central lymph node metastasis 030104 developmental biology medicine.anatomical_structure machine learning ROC Curve Central Lymph Node Dissection Thyroid Cancer Papillary Lymphatic Metastasis Female Artificial intelligence business computer Algorithm Algorithms Follow-Up Studies |
| Zdroj: | Frontiers in Endocrinology, Vol 11 (2020) Frontiers in Endocrinology |
| ISSN: | 1664-2392 |
| Popis: | Background Central 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. Methods Patients 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. Results The 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. Conclusions It 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. |
| Databáze: | OpenAIRE |
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