Abstract

Info

Title:: A neural network-based model for predicting thyroid tumor recurrence risk

Author(s):: LUO Aijing1; 3; 4; 5; WANG Zhexuan1; 3; 4; 5; XIE Wenzhao2; 3; 4; 5; HU Dehua3; XU Qian1; 3; 4; 5; SHU Yongbo1; 3; 4; 5; 1. The Second Xiangya Hospital of Central South University, Changsha 410011, China; 2. Post-Graduation Education Office, the ThirdXiangya Hospital of Central South University, Changsha 410013, China; 3. School of Life Sciences, Central South University,Changsha 410013, China; 4. Key Laboratory of Medical Information Research (Central South University), College of Hunan Province,Changsha 410013, China; 5. Clinical Research Center for Cardiovascular Intelligent Healthcare in Hunan Province, Changsha 410011,China

Keywords:: thyroid tumor; postoperative recurrence; machine learning; artificial neural network

Abstract:: Abstract: Objective To develop a neural network-based deep learning model for predicting postoperative recurrence inthyroid tumor patients and validate the model with external datasets for providing clinicians with a reliable decision supporttool. Methods An artificial neural network structure was adopted in the study, with thyroid tumor data from the SEERdatabase serving as the training set. External validation was conducted with open-source data from the University ofCalifornia, Irvine (UCIrvine), and the data from 100 patients at a general tertiary hospital in Hunan province. The model’saccuracy and reliability in predicting recurrence were evaluated through multiple performance metrics. Results Experimentalresults showed that the model outperformed Logistic model in recurrence prediction, with accuracy, recall rate, precision andF1 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 UCIrvinevalidation 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 networkbased predictive model exhibits excellent performance in thyroid tumor recurrence prediction, providing clinicians with avaluable decision support tool that can help optimize postoperative treatment plans and improve patient prognosismanagement.