| 袁炜烨,姚杰,蒋舟,肖虹,钱爱君,王彬,白江涛,高林峰.基于神经网络对甲状腺功能亢进患者放射性碘治疗给药剂量预测的可行性研究[J].中华放射医学与防护杂志,2022,42(2):130-136 |
| 基于神经网络对甲状腺功能亢进患者放射性碘治疗给药剂量预测的可行性研究 |
| Feasibility study of predicting dose of radioiodine in hyperthyroidism patients based on neural network |
| 投稿时间：2021-07-12 |
| DOI：10.3760/cma.j.cn112271-20210712-00273 |
| 中文关键词: 甲状腺机能亢进 131I治疗 神经网络 |
| 英文关键词:Hyperthyroidism 131 I treatment Neural network |
| 基金项目: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） |
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| 中文摘要: |
| 目的 构建反向传播（back propagation，BP）神经网络模型，预测甲状腺功能亢进（以下简称甲亢）患者131I核素治疗所需给药剂量，为患者计算个性化治疗剂量方案。方法 从上海多家医学院核医学科收集接受放射性碘治疗的甲亢患者完整的病例资料，包括病史、检查结果、治疗过程等。随后建立预测模型，先以小样本数据比较BP神经网络，径向基（radial basis function，RBF）神经网络，支持向量机（support vector machine，SVM）3种模型的预测结果，选择最优模型对给药剂量进行预测，最后测试模型精准度。结果 以小样本构建的BP神经网络、RBF神经网络、SVM模型预测的平均误差分别为5.53%，7.09%，9.64%，比较后选择BP神经网络建立预测模型；采用随机抽样法选取30例数据对BP神经网络进行验证计算，预测结果的平均误差为7.22%，均方误差为0.053，最小误差为0.57%，最大误差为13.78%。结论 本研究提出了一种神经网络预测方法，为需要放射性碘治疗的甲亢患者提供给药剂量参考，减少患者剂量过量所致放射性损伤的可能或剂量不足所致治疗效果不理想的情况。 |
| 英文摘要: |
| 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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