Extreme gradient boosting model to assess risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual prediction using SHapley Additive exPlanations

作者：Ying Zou, Yan Shi, Fang Sun, Jihua Liu, Yu Guo, Huanlei Zhang, Xiudi Lu, Yan Gong, Shuang Xia · 发表于：Computer Methods and Programs in Biomedicine · 年份：2022 · DOI：10.1016/j.cmpb.2022.107038 · 被引用次数：68 · 研究领域：Thyroid Cancer Diagnosis and Treatment、AI in cancer detection、Head and Neck Cancer Studies

BACKGROUND AND OBJECTIVES: Central cervical lymph node metastasis (CLNM) is considered a risk factor for recurrence in patients with papillary thyroid carcinoma (PTC). Traditional machine learning models suffered from "black-box" problems, which could not exactly explain the interactive effects of the risk factors. We aimed to develop an eXtreme Gradient Boosting (XGBoost) model to assess CLNM, including positive and negative effects. METHODS: 1,122 patients with PTC admitted at Tianjin First Central Hospital from 2016 to 2020 were retrospectively selected. They were randomly divided into the training and test datasets with an 8:2 ratio. 108 patients with PTC admitted at Binzhou Medical University Hospital in 2020 served as the validation dataset. The XGBoost model was used to assess CLNM. The 10-fold cross-validation was utilized for model selection, and the metric used to evaluate classification performance was the average area under the curve (AUC) of 10-fold cross-validation. Interpretation and transparency of the "black-box" problem were performed. SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) were used to ensure the stability and reliability of the model. RESULTS: The XGBoost model based on ultrasound and dual-energy computed tomography images of the solitary primary lesion had an excellent performance for assessing CLNM, with average AUCs of 0.918, 0.903, and 0.881 in the training, test, and validation datasets, respecti...