A neural network-based model for predicting thyroid tumor recurrence risk
10.3969/j.issn.1005-202X.2025.07.020
- VernacularTitle:基于神经网络的甲状腺肿瘤复发风险评估模型
- Author:
Aijing LUO
1
;
Zhexuan WANG
;
Wenzhao XIE
;
Dehua HU
;
Qian XU
;
Yongbo SHU
Author Information
1. 中南大学湘雅二医院,湖南 长沙 410011;中南大学生命科学学院,湖南 长沙 410013;医学信息研究湖南省普通高等学校重点实验室(中南大学),湖南 长沙 410013;湖南省心血管智能医疗临床医学研究中心,湖南 长沙 410011
- Publication Type:Journal Article
- Keywords:
thyroid tumor;
postoperative recurrence;
machine learning;
artificial neural network
- From:
Chinese Journal of Medical Physics
2025;42(7):974-980
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a neural network-based deep learning model for predicting postoperative recurrence in thyroid tumor patients and validate the model with external datasets for providing clinicians with a reliable decision support tool.Methods An artificial neural network structure was adopted in the study,with thyroid tumor data from the SEER database serving as the training set.External validation was conducted with open-source data from the University of California,Irvine(UCIrvine),and the data from 100 patients at a general tertiary hospital in Hunan province.The model's accuracy and reliability in predicting recurrence were evaluated through multiple performance metrics.Results Experimental results showed that the model outperformed Logistic model in recurrence prediction,with accuracy,recall rate,precision and F1 score reaching 0.915 3,0.981 8,0.921 1 and 0.947 4 in internal validation.Moreover,the model achieved accuracies,recall rates,precisions,F1 scores and ROC_AUC values of 0.832 9,0.945 5,0.841 4,0.890 4 and 0.78 on the UCIrvine validation set,while 0.870 0,0.880 0,0.862 7,0.871 3 and 0.80 on the local validation set.Conclusion This neural network-based predictive model exhibits excellent performance in thyroid tumor recurrence prediction,providing clinicians with a valuable decision support tool that can help optimize postoperative treatment plans and improve patient prognosis management.
