前列腺癌术后放疗靶区自动勾画人工智能模型构建

Automatic-delineation model construction for prostate cancer target volume of postoperative radiotherapy based on artificial intelligence

目的探索基于卷积神经网络构建前列腺癌术后放疗临床靶区(CTV)及部分危及器官(OAR)自动勾画模型的方法,以提高临床工作效率和靶区勾画统一性。方法回顾性分析由一位放疗医师勾画的117例前列腺癌术后患者CT资料,基于3D UNet设计多分类自动勾画模型CVT-UNet,采用戴斯相似系数(DSC)、95分位豪斯多夫距离(95%HD)和平均表面距离(ASD)评估模型分割能力,比较另外两位医师对测试集自动勾画靶区评估结果的差异。再随机收集78例由其他医生治疗的患者进行模型外部验证,比较两位医师评价的差异。结果测试集前列腺瘤床区(CTV1)、盆腔淋巴结引流区(CTV2)、膀胱和直肠的平均DSC分别为0.74、0.82、0.94和0.79。临床评分显示:两位医师对CTV2和OAR的勾画达成了更多的共识;而对CTV1的勾画则共识较少,故评分差异较大。结论基于卷积神经网络构建用于术后前列腺癌CTV及相关OAR的自动勾画模型可行,但前列腺瘤床区的自动分割仍需改善。

Objective To explore the method of constructing automatic delineation model for clinical target volume(CTV)and partially organs at risk(OAR)of postoperative radiotherapy for prostate cancer based on convolutional neural network,aiming to improve the clinical work efficiency and the unity of target area delineation.Methods Postoperative CT data of 117 prostate cancer patients manually delineated by one experienced clinician were retrospectively analyzed.A multi-class auto-delineation model was designed based on 3D UNet.Dice similarity coefficient(DSC),95%Hausdorf distance(95%HD),and average surface distance(ASD)were used to evaluate the segmentation ability of the model.In addition,the segmentation results in the test set were evaluated by two senior physicians.And the CT data of 78 patients treated by other physicians were also collected for external validation of the model.The automatic segmentation of these 78 patients by CTV-UNet model was also evaluated by two physicians.Results The mean DSC for tumor bed area(CTV1),pelvic lymph node drainage area(CTV2),bladder and rectum of CVT-UNet auto-segmentation model in the test set were 0.74,0.82,0.94 and 0.79,respectively.Both physicians'scoring results of the test set and the external validation showed more consensus on the delineation of CTV2 and OAR.However,the consensus of CTV1 delineation was less.Conclusions The automatic delineation model based on convolutional neural network is feasible for CTV and related OAR of postoperative radiotherapy for prostate cancer.The automatic segmentation ability of tumor bed area still needs to be improved.