摘要
目的:对比分析定制化自动勾画模型和商品化通用勾画模型在宫颈癌危及器官勾画中的差异,探讨定制化模型的可行性。方法:回顾性选取270例宫颈癌患者,由资深临床医生勾画其膀胱、直肠、小肠、骨盆骨髓、股骨头及肾脏等器官。随机选取病例构建定制化勾画模型,其中202例作为训练集,30例作为验证集,38例作为测试集。在测试集上,使用定制化模型和通用模型进行勾画预测。对比测试集在两种模型下的结果与手动勾画的结果,评估定制化模型的性能。结果:从DSC值上分析,定制化模型/通用模型与手动勾画相近,均表现出良好的勾画效果(DSC值:0.7~0.9)。然而,在质心偏差和95%豪斯多夫距离这两个关键指标上,定制化模型优于通用模型。结论:与通用模型相比,定制化模型能更准确地勾画宫颈癌危及器官的结构。定制化模型根据特定数据集优化,为临床决策提供精准支持,展现其在宫颈癌治疗中的广阔应用前景。
Objective To compare and analyze the differences between customized models and commercial universal models in the segmentation of organs-at-risk in cervical cancer,and to investigate the feasibility of customized models.Methods A retrospective analysis was conducted on 270 cervical cancer patients.Senior clinicians manually delineated organs-at-risk,including the bladder,rectum,small intestine,pelvic bone marrow,femoral heads,and kidneys.The cases were randomly selected to develop customized models,with 202 cases allocated to the training set,38 cases to the test set,and 30 cases to the validation set.The universal and customized models were used for segmentation on the test set,and the automatic segmentation results obtained by the two models were compared with manual segmentation results to assess the performance of the customized model.Results Both customized model and universal model had comparable DSC values to manual segmentation,demonstrating satisfactory delineation outcomes(DSC values ranging from 0.7 to 0.9).However,in terms of deviation of centroid and 95% Hausdorff distance,the customized model surpassed the universal model.Conclusion Compared with the universal model,the customized model offers superior accuracy in delineating the structures of organs-atrisk in cervical cancer.As the customized model is optimized based on specific datasets,it provides precise support for clinical decision-making and holds promising applications in the treatment of cervical cancer.
作者
柳炫宇
陈舒影
郭飞宝
陈燕彬
何清
吕文龙
陈颀
张倚萌
王少彬
蔡传书
LIU Xuanyu;CHEN Shuying;GUO Feibao;CHEN Yanbin;HE Qing;LU Wenlong;CHEN Qi;ZHANG Yimeng;WANG Shaobin;CAI Chuanshu(Department of Radiotherapy,Cancer Center,the First Affiliated Hospital of Fujian Medical University,Fuzhou 350005,China;National Regional Medical Center,Binhai Branch of the First Affiliated Hospital of Fujian Medical University,Fuzhou 350212,China;Key Laboratory of Radiation Biology of Fujian Higher Education Institutions,Fuzhou 350005,China;School of Medical Imaging,Fujian Medical University,Fuzhou 350122,China;MedMind Technology Co.,Ltd.,Beijing 100083,China;Tsinghua Medicine,Tsinghua University,Beijing 100084,China)
出处
《中国医学物理学杂志》
CSCD
2024年第11期1337-1342,共6页
Chinese Journal of Medical Physics
基金
福建省科技计划项目(2021Y0101)
福建医科大学启航基金(2020QH1057)。