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OACSTPCD
Research on the 3D Dose Prediction Based on Deep Learning for Rectal Cancer Volumetric Modulated Arc Therapy
目的 基于3DRes-UNet深度学习网络预测直肠癌术后容积旋转调强放射治疗(Volumetric Modulated Arc Therapy,VMAT)三维剂量精度,以指导临床放疗工作.方法 选取168例直肠癌VMAT放疗计划为研究对象,将数据集按7∶1∶2随机分为训练集120例、验证集16例和测试集32例.将训练集的CT影像和危及器官及靶区的掩码输入网络进行训练,在测试集上将预测的剂量与临床批准的放疗剂量进行对比,评价放疗剂量的预测精度.结果 临床剂量与预测值相比,靶区的D2、D98、D50、均匀性指数差异均无统计学意义(P>0.05),适形性指数差异有统计学意义(P<0.05);危及器官膀胱V50、Dmean预测剂量小于临床剂量,差异有统计学意义(P<0.05),V40差异无统计学意义(P>0.05);左股骨头V40预测剂量小于临床剂量,差异有统计学意义(P<0.05),V30、V50、Dmean差异无统计学意义(P>0.05);右股骨头Dmean预测剂量小于临床剂量,差异有统计学意义(P<0.05),V30、V40、V50差异无统计学意义(P>0.05);骨盆V45和Dmean预测剂量均小于临床剂量,差异有统计学意义(P<0.05);小肠V30、V40,Dmean、D0.1cc差异无统计学意义(P>0.05).剂量差异图显示靶区预测结果与临床结果差异很小,危及器官差异范围为-10~10 Gy.预测与临床的剂量体积直方图基本重合.结论 该3DRes-UNet模型可有效预测直肠癌术后VMAT三维空间剂量,以达到指导临床放疗工作的目的.
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.
刘润红;刘可;黄强;徐孝明;许惠
内江市第二人民医院 放疗科,四川 内江 641000自贡市第一人民医院 肿瘤科,四川 自贡 643000内江市第六人民医院 放射科,四川 内江 641000
临床医学
直肠癌容积旋转调强放疗三维剂量深度学习
rectal cancervolumetric modulated arc therapy3D dosedeep learning
《中国医疗设备》 2024 (004)
41-46,52 / 7
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