Frontiers in oncology · 2026
Machine learning and Shapley Additive exPlanations to predict metastasis of lymph nodes posterior to the recurrent laryngeal nerve in cN0 papillary thyroid carcinoma.
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Zhou Jing, Li Ben, Sun Tao, Li DaXue, Huang Chun, Gao Han, Ren Jiahui, Zhuang Yuchen, Xue Song, Xiao Qian, Chun Lin, Su Xinliang
Аннотация
OBJECTIVE: Prophylactic dissection of lymph nodes posterior to the recurrent laryngeal nerve (LN-prRLN) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial due to the inability to preoperatively assess LN-prRLN metastasis.
MATERIALS AND METHODS: This study aims to construct and validate an interpretable predictive model for LN-prRLN metastasis in cN0 PTC using machine learning (ML) method. Data were collected from hospital A and divided into training and testing sets (7:3). Additional data from the hospital B were used as validation set. Nine ML models, including XGBoost, were developed. Predictive performance was evaluated using ROC curves, decision curve analysis (DCA), calibration curves, and precision-recall curves. The best model was compared to a traditional logistic regression-based nomogram using learning curves and the method of Probability-based Ranking Model Approach (PMRA). SHapley Additive exPlanations (SHAP) were used to interpret the top ten predictive features and create a web-based calculator.
RESULTS: A total of 2033 patients were included. XGBoost outperformed other models with AUCs of 0.859, and 0.885 for the testing, and validation sets, respectively, compared to the nomogram (0.814, 0.836). SHAP-based visualizations identified the top ten predictive features: ipsilateral paratracheal lymph node metastasis rate, number of total central lymph node metastases, total central lymph node metastasis rate, number of ipsilateral paratracheal lymph node metastases, pretracheal lymph node metastasis rate, ipsilateral paratracheal lymph node metastasis, unclear tumor border, size, and age ≤39 years. These features were used to develop a web-based calculator.
CONCLUSION: ML is a reliable tool for predicting LN-prRLN metastasis in cN0 PTC patients. The SHAP method provides insights into the XGBoost model, and the resultant web-based calculator is a clinically useful tool to assist in the surgical planning for LN-prRLN dissection.
MATERIALS AND METHODS: This study aims to construct and validate an interpretable predictive model for LN-prRLN metastasis in cN0 PTC using machine learning (ML) method. Data were collected from hospital A and divided into training and testing sets (7:3). Additional data from the hospital B were used as validation set. Nine ML models, including XGBoost, were developed. Predictive performance was evaluated using ROC curves, decision curve analysis (DCA), calibration curves, and precision-recall curves. The best model was compared to a traditional logistic regression-based nomogram using learning curves and the method of Probability-based Ranking Model Approach (PMRA). SHapley Additive exPlanations (SHAP) were used to interpret the top ten predictive features and create a web-based calculator.
RESULTS: A total of 2033 patients were included. XGBoost outperformed other models with AUCs of 0.859, and 0.885 for the testing, and validation sets, respectively, compared to the nomogram (0.814, 0.836). SHAP-based visualizations identified the top ten predictive features: ipsilateral paratracheal lymph node metastasis rate, number of total central lymph node metastases, total central lymph node metastasis rate, number of ipsilateral paratracheal lymph node metastases, pretracheal lymph node metastasis rate, ipsilateral paratracheal lymph node metastasis, unclear tumor border, size, and age ≤39 years. These features were used to develop a web-based calculator.
CONCLUSION: ML is a reliable tool for predicting LN-prRLN metastasis in cN0 PTC patients. The SHAP method provides insights into the XGBoost model, and the resultant web-based calculator is a clinically useful tool to assist in the surgical planning for LN-prRLN dissection.