| 罗锐,刘明哲,温爱萍,晏川钧,罗静月,王培,黎杰,王先良.基于3D U-net的宫颈癌近距离治疗剂量分布预测[J].中华放射医学与防护杂志,2022,42(8):611-617 |
| 基于3D U-net的宫颈癌近距离治疗剂量分布预测 |
| Dose distribution prediction in cervical cancer brachytherapy based on 3D U-net |
| 投稿时间：2022-03-05 |
| DOI：10.3760/cma.j.cn112271-20220305-00085 |
| 中文关键词: 宫颈癌 近距离放疗 剂量预测 剂量分布 3D U-net |
| 英文关键词:Cervical cancer Brachytherapy Dose prediction Dose distribution 3D U-net |
| 基金项目:四川省重点研发项目（2022YFG0194）；成都市科技局技术创新研发项目（2021-YF05-02107-SN）；四川省科技计划资助项目（2021YFG0320）；成都市科技局重点研发支撑计划（2019-YF09-00095-SN） |
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| 中文摘要: |
| 目的 基于三维（3D） U-net深度学习模型，建立预测CT引导下宫颈癌近距离治疗计划的3D空间剂量分布。方法 2021年4-9月收集114例宫颈癌患者三维近距离放疗计划（处方剂量6 Gy）组成数据集，按84 ∶11 ∶19划分为训练集、验证集、测试集。利用3D U-net模型进行500次（epoch）训练，分别评估测试集病例体素级的平均剂量偏差（MDD）与绝对剂量偏差（MADD）、等剂量面包围体积的戴斯系数（DSC）、处方剂量适形度指数（CI）、高危临床靶区（HRCTV）的D90和平均剂量Dmean、膀胱、直肠、小肠、结肠的D1 cm3与D2 cm3剂量学参数。结果 测试集中19例患者的3D剂量矩阵MDD与MADD分别为-0.01±0.03和（0.04±0.01） Gy。50%到150%处方剂量的DSC在0.89到0.94之间，处方剂量CI为0.70±0.04。HRCTV的D90的平均偏差为2.22%，Dmean的偏差为-4.30%。膀胱、直肠、小肠、结肠的D1 cm3与D2 cm3最大偏差分别为2.46%和2.58%。模型预测平均耗时2.5 s。结论 本研究实现了一种基于3D U-net的预测宫颈癌3D剂量分布的深度学习模型，为宫颈癌近距离治疗自动化设计奠定基础。 |
| 英文摘要: |
| Objective To establish a three-dimensional (3D) U-net-based deep learning model, and to predict the 3D dose distribution in CT-guided cervical cancer brachytherapy by using the established model.Methods The brachytherapy plans of 114 cervical cancer cases with a prescription dose of 6 Gy for each case were studied. These cases were divided into training, validation, and testing groups, including 84, 11, and 19 patients, respectively. A total of 500 epochs of training were performed by using a 3D U-net model. Then, the dosimetric parameters of the testing groups were individually evaluated, including the mean dose deviation (MDD) and mean absolute dose deviation (MADD) at the voxel level, the Dice similarity coefficient (DSC) of the volumes enclosed by isodose surfaces, the conformal index (CI) of the prescription dose, the D90 and average dose Dmean delivered to high-risk clinical target volumes (HR-CTVs), and the D1cm3 and D2cm3 delivered to bladders, recta, intestines, and colons, respectively.Results The overall MDD and MADD of the 3D dose matrix from 19 cases of the testing group were (-0.01 ±0.03) and (0.04 ±0.01) Gy, respectively. The CI of the prescription dose was 0.70 ±0.04. The DSC of 50%-150% prescription dose was 0.89-0.94. The mean deviation of D90 and Dmean to HR-CTVs were 2.22% and -4.30%, respectively. The maximum deviations of the D1cm3 and D2cm3 to bladders, recta, intestines, and colons were 2.46% and 2.58%, respectively. The 3D U-net deep learning model took 2.5 s on average to predict a patient's dose.Conclusions In this study, a 3D U-net-based deep learning model for predicting 3D dose distribution in the treatment of cervical cancer was established, thus laying a foundation for the automatic design of cervical cancer brachytherapy. |
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