Predictive value of machine learning models based on CT imaging features for papillary thyroid carcinoma
10.3760/cma.j.cn115807-20231018-00112
- VernacularTitle:CT影像特征机器学习预测模型对甲状腺乳头状癌的预测价值
- Author:
Hanlin ZHU
1
;
Bo FENG
;
Haifeng ZHANG
;
Meihua ZHANG
;
Min TIAN
;
Tong ZHANG
;
Peiying WEI
;
Zhijiang HAN
Author Information
1. 浙江省杭州市第九人民医院放射科，杭州 311225
- Publication Type:Journal Article
- Keywords:
Thyroid nodules;
Papillary thyroid carcinoma;
Tomography, X-ray;
SHAP;
Machine learning model
- From:
Chinese Journal of Endocrine Surgery
2025;19(1):68-73
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish three machine learning prediction models based on CT imaging characteristics of papillary thyroid carcinoma (PTC) , and use SHAP (shapley additive explanations) analysis to investigate the contribution of each CT image features in the best model.Methods:CT imaging features in 426 cases of 440 PTCs confirmed pathologically from Jan. 2016 to Jan. 2021 at the affiliated Hangzhou First People’s Hospital of Westlake University Medical School were retrospectively analyzed. compared with 467 cases of 528 nodular goiter (NG) , evaluating the distribution of four CT characteristics: cookie bite sign, enhanced range of narrowing/blur (ERNB) , microcalcifications, and irregular shape. We split the data into 8∶2 ratio for training and testing sets, then constructed three machine learning models using XGBoost, RF, and SVM. Based on AUC, accuracy, F1 score, and other metrics, we selected the best model. Lastly, we used SHAP values to assess each CT feature’s contribution and positive/negative effects on the model.Results:Among 440 PTC and 528 NG nodules, CT features like cookie bite sign, ERNB, microcalcifications, and irregular shape occurred in 326 and 30 ( χ 2=483.05, P<0.001) , 363 and 106 ( χ 2=374.45, P<0.001) , 158 and 53 ( χ 2=94.24, P<0.001) , and 354 and 52 ( χ 2=491.34, P<0.001) nodules, respectively. The machine learning models built using XGBoost, RF, and SVM had AUC, accuracy, and F1 scores ranging from 0.884~0.925, 0.867~0.873, and 0.844~0.854 respectively on the training set. On the test set, the scores ranged from 0.869~0.923, 0.845~0.871, and 0.803~0.845. Among them, the XGBoost model demonstrated the highest diagnostic performance on the test set. Among the four CT features, irregular shape had the highest absolute SHAP value, positively contributing to PTC diagnosis. Conclusion:XGBoost model showed the highest PTC diagnostic performance. Irregular shape had the greatest positive impact on PTC diagnosis.
