| Yuan Weiye,Yao Jie,Jiang Zhou,et al.Feasibility study of predicting dose of radioiodine in hyperthyroidism patients based on neural network[J].Chinese Journal of Radiological Medicine and Protection,2022,42(2):130-136 |
| Feasibility study of predicting dose of radioiodine in hyperthyroidism patients based on neural network |
| Received:July 12, 2021 |
| DOI：10.3760/cma.j.cn112271-20210712-00273 |
| KeyWords:Hyperthyroidism 131 I treatment Neural network |
| FundProject:国家自然科学基金（11775145）；上海市公共卫生重点学科建设计划项目（GWV-10.1-XK10）；上海市公共卫生体系建设三年行动计划优秀学科带头人计划（GWV-10.2-XD16） |
| Author Name | Affiliation | | Yuan Weiye | Shanghai Center for Disease Control and Prevention, Shanghai 200030, China | | Yao Jie | Shanghai Center for Disease Control and Prevention, Shanghai 200030, China | | Jiang Zhou | Shanghai Center for Disease Control and Prevention, Shanghai 200030, China | | Xiao Hong | Shanghai Center for Disease Control and Prevention, Shanghai 200030, China | | Qian Aijun | Shanghai Center for Disease Control and Prevention, Shanghai 200030, China | | Wang Bin | Shanghai Center for Disease Control and Prevention, Shanghai 200030, China | | Bai Jiangtao | Shanghai Center for Disease Control and Prevention, Shanghai 200030, China | | Gao Linfeng | Shanghai Center for Disease Control and Prevention, Shanghai 200030, China |
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| Abstract:: |
| Objective To construct back propagation (BP) neural network model to predict the dose required for 131I therapy for hyperthyroidism and to calculate the personalized dose plan for patients. Methods A complete set of data of patients treated for hyperthyroidism radioaiodine was collected from the nuclear medicine departments of several medical colleges in Shanghai, including history, examination result, treatment course, etc. As a result, a prediction model was established. The predicated result for BP neural network, radial basis function (RBF) neural network and Support Vector Machine (SVM) were compared by means of small sample data. The optimal model was selected to predict administrated dose and to finally test the accuracy of the model. Results The average errors in BP neural network, RBF neural network and SVM model based on small samples were 5.53%, 7.09% and 9.64%, respectively. After comparison, BP neural network was selected to build the prediction model. 30 cases of data were selected by random sampling to verify the BP neural network. The mean error, mean square error,minimum error and maximum error of the prediction result were 7.22%, 0.053, 0.57% and 13.78%, respectively. Conclusions In this study, a neural network prediction method was proposed to provide a more accurate dose for patients in need of radioiodine therap for hyperthyroidism, and to reduce the possibility of radiation damage or the unsatisfactory therapeutic effect caused by insufficient dose. It has clinical practical significance in providing the reference for clinicians to evaluate the administrated dose. |
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