Journal of Otolaryngology and Ophthalmology of Shandong University ›› 2020, Vol. 34 ›› Issue (3): 40-45.doi: 10.6040/j.issn.1673-3770.1.2020.031

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Prediction of cervical lymph node metastasis in papillary thyroid cancer based on XGBoost artificial intelligence and enhanced computed tomography

CHEN Haibing1, WEI Ya'nan1, XU Xiaoquan2, CHEN Xi1   

  1. 1. Department of Otorhinolaryngology;
    2. Department of Radiology, The First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, Jiangsu, China
  • Online:2020-05-20 Published:2020-06-29

Abstract: Objective Incorporating eXtreme Gradient Boosting(XGBoost)artificial intelligence, we aimed to build a predictive model using pre-operative enhanced computed tomography(CT)of cervical lymph node metastasis in patients with thyroid cancer, to provide a reference for pre-operative planning. Methods The clinical data of 38 patients with thyroid papillary carcinoma from October 2017 to May 2019 were retrospectively analyzed. A total of 135 lymph nodes were included. Using XGBoost artificial intelligence, the lymph node metastasis prediction model was established, and the accuracy of the prediction model was tested. Results The average accuracy of the XGBoost model was 87.41%, which was higher than that of the SVM model(79.2%). Important CT characteristics that are indicative of lymph node metastasis include degree and distribution of enhancement, location, and capsule invasion. Conclusion The predictive model of cervical lymph node metastasis in patients with thyroid cancer exhibits high accuracy and could help in the pre-operative evaluation of cervical lymph node metastasis, tumor staging, and surgical procedures.

Key words: Thyroid cancer, Lymph node metastasis, XGBoost, Artificial intelligence, Prediction model

CLC Number: 

  • R736
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