基于全卷积网络U-Net宫颈癌近距离治疗三维剂量分布预测研究

Study of three-dimensional dose distribution prediction in cervical cancer brachytherapy based on U-Net fully convolutional network

目的基于全卷积网络U-Net预测宫颈癌近距离治疗(BT)感兴趣区(ROI)三维剂量分布,并评估其预测精度。方法首先选取100例宫颈癌腔内结合组织间插植病例作为整个研究数据集,并将其划分为训练集(72例)、验证集(8例)、测试集(20例);然后利用U-Net建立模型,将是否包含宫腔管及插针作为区分因素训练两个模型;最后对20例测试集病例进行预测,并进行对比分析。模型的性能通过ΔD90%、ΔD_(2cm)^(3)以及平均绝对离差共同评估。结果包含与未包宫腔管与插植针的模型相比,直肠的ΔD_(2cm)^(3)上升了(16.83±1.82)cGy(P<0.05),其余ROI的ΔD90%或ΔD_(2cm)^(3)均相近(均P>0.05);高危靶区、直肠、乙状结肠、小肠、膀胱平均绝对离差分别上升了(11.96±3.78)、(11.43±0.54)、(24.08±1.65)、(17.04±7.17)、(9.52±4.35)cGy(均P<0.05);中危靶区的下降了(120.85±29.78)cGy(P<0.05);6个ROI的平均绝对离差的平均值下降了(7.8±53)cGy(P<0.05),更接近实际计划。结论利用全卷积网络U-Net可以实现宫颈癌患者BT的三维剂量分布预测;结合宫腔管与插植针作为输入参数,比单一使用ROI结构作为输入能得到更准确的预测结果。

Objective Topredict the three-dimensional dose distribution of regions of interest(ROI)with brachytherapy for cervical cancer based on U-Net fully convolutional network,and evaluate the accuracy of prediction model.Methods First,100 cases of cervical cancer intracavity combined with interstitial implantation were selected as the entire research data set,and divided into the training set(n=72),validation set(n=8),and test set(n=20).Then the U-Net was used to construct two models based on whether the uterine tandem and the implantation needles were included as the distinguishing factors.Finally,dose distribution of 20 cases in the test set were predicted using the trained model,and comparative analysis was performed.The performance of the model was jointly evaluated byΔD90%,ΔD_(2cm)^(3)and the mean absolute deviation(MAD).Results Compared with the model without the uterine tandem and the implantation needles,theΔD_(2cm)^(3)of the rectum was increased by(16.83±1.82)cGy(P<0.05),andtheΔD90%orΔD_(2cm)^(3)of the other ROI were not different significantly(all P>0.05).The MAD of the high-risk clinical target volume,rectum,sigmoid,small bowel,and bladder was increased by(11.96±3.78)cGy,(11.43±0.54)cGy,(24.08±1.65)cGy,(17.04±7.17)cGy and(9.52±4.35)cGy,respectively(all P<0.05).The MAD of the intermediate-risk clinical target volume was decreased by(120.85±29.78)cGy(P<0.05).The mean value of MAD for all ROI was decreased by(7.8±53)cGy(P<0.05),which was closer to the actual plan.Conclusions U-Net fully convolutional network can be used to predict three-dimensional dose distribution of patients with cervical cancer undergoing brachytherapy.Combining the uterine tube with the implantation needles as the input parameters yields more accurate predictions than a single use of the ROI structure as the input.