Different machine learning models for predicting cervical lymph node metastasis of papillary thyroid carcinoma
10.13929/j.issn.1672-8475.2025.03.003
- VernacularTitle:不同机器学习模型预测甲状腺乳头状癌颈部淋巴结转移
- Author:
Beibei HU
1
;
Yingxia ZHANG
1
;
Wei DENG
1
Author Information
1. 内蒙古医科大学附属医院超声诊断科,内蒙古 呼和浩特 010050
- Publication Type:Journal Article
- Keywords:
thyroid neoplasms;
carcinoma,papillary;
neoplasm metastasis;
machine learning;
ultrasonography
- From:
Chinese Journal of Interventional Imaging and Therapy
2025;22(3):164-168
- CountryChina
- Language:Chinese
-
Abstract:
Objective To comparatively observe the value of different machine learning(ML)models for predicting cervical lymph node metastasis(CLNM)of papillary thyroid carcinoma(PTC).Methods Totally 207 patients with pathologically diagnosed PTC were enrolled and divided into metastasis group(n=103)and non-metastasis group(n=104)according to lymph nodes pathology findings after surgical resection,also divided into training set(n=144)and validation set(n=63)with a ratio of 7∶3.Random forest(RF),decision tree(DT),K-nearest neighbor(KNN),logistic regression(LR)and support vector machine(SVM)models were constructed through combining clinical information and ultrasonic manifestations of lymph nodes.Receiver operating characteristic curves were drawn,and the areas under the curve(AUC)were calculated to evaluate the efficacy of these ML models for predicting PTC CLNM.Results Patients'age,transverse diameter/longitudinal diameter ratio of lymph node≥0.5,lymph nodes with cystic change,microcalcification,disappearance of lymphatic gate,hypoechoic mass and unclear border were all impact factors of PTC CLNM,among which microcalcification had the highest contribution to the models.In training set,AUC of DT and RF models were both 0.987,with accuracy reached 93.06%.In validation set,AUC of DT and RF models was 0.817 and 0.895,respectively,all higher than those of other models.The accuracy,specificity and positive predictive value of RF model in validation set was 84.13%,93.10%and 92.86%,respectively,and RF model was the best one among all 5 ML models.Conclusion Among different ML models,RF model was the best one for predicting PTC CLNM.
