中国医疗设备2024，Vol.39Issue(4)：41-46,52,7.DOI:10.3969/j.issn.1674-1633.2024.04.008
基于深度学习的直肠癌术后容积旋转调强放疗三维剂量预测研究
Research on the 3D Dose Prediction Based on Deep Learning for Rectal Cancer Volumetric Modulated Arc Therapy
刘润红 1刘可 2黄强 1徐孝明 1许惠3
作者信息
- 1. 内江市第二人民医院 放疗科,四川 内江 641000
- 2. 自贡市第一人民医院 肿瘤科,四川 自贡 643000
- 3. 内江市第六人民医院 放射科,四川 内江 641000
- 折叠
摘要
Abstract
Objective To propose a 3DRes-UNet deep learning network for predicting the 3D dose accuracy of postoperative volume modulated arc therapy(VMAT)for rectal cancer surgery,so as to guide clinical practice.Methods A total of 168 VMAT radiotherapy plans for rectal cancer was collected.The data set was randomly divided into a training set of 120 cases,a validation set of 16 cases,and a test set of 32 cases in a 7∶1∶2 ratio.The CT images of the training set and masks of organs and target volume were input into the network for training.The predicted dose was compared with clinically approved radiotherapy doses on the test set to evaluate the accuracy of radiotherapy dose prediction.Results There was no statistically significant difference in D2,D98,D50,and homogeneity index between the clinical dose and predicted values in the target volume(P＞0.05).There was a statistical difference in the conformity index(P＜0.05).The predicted doses of V50 and Dmean for organ threatening bladder were lower than clinical doses(P＜0.05),and there was no statistically significant difference in V40(P＞0.05).The predicted dose of V40 in the left femoral head was lower than the clinical dose(P＜0.05),and there was no statistically significant difference in V30,V50,and Dmean(P＞0.05).The predicted dose of Dmean in the right femoral head was lower than the clinical dose(P＜0.05),and there was no statistically significant difference in V30,V40,and V50(P＞0.05).The predicted doses of pelvic V45 and Dmean were also lower than clinical doses(P＜0.05).There was no statistically significant difference in V30,V40,Dmean,and D0.1cc in the small bowel(P＞0.05).The dose difference map showed that there was little difference between the predicted results of the target area and the clinical results,and the difference in organ endangerment was between-10-10 Gy.The predicted dose volume histogram basically coincided with the clinical dose volume histogram.Conclusion The 3DRes-UNet model can effectively predict the 3D space dose of postoperative VMAT radiotherapy plan for rectal cancer,and guide clinical radiotherapy work.关键词
直肠癌/容积旋转调强放疗/三维剂量/深度学习Key words
rectal cancer/volumetric modulated arc therapy/3D dose/deep learning分类
医药卫生引用本文复制引用
刘润红,刘可,黄强,徐孝明,许惠..基于深度学习的直肠癌术后容积旋转调强放疗三维剂量预测研究[J].中国医疗设备,2024,39(4):41-46,52,7.
