Comparative study on the accuracies of customized and universal models for organs-at-risk segmentation in cervical cancer
10.3969/j.issn.1005-202X.2024.11.003
- VernacularTitle:定制化与通用模型在宫颈癌危及器官勾画准确性中的比较
- Author:
Xuanyu LIU
1
,
2
;
Shuying CHEN
;
Feibao GUO
;
Yanbin CHEN
;
Qing HE
;
Wenlong LÜ
;
Qi CHEN
;
Yimeng ZHANG
;
Shaobin WANG
;
Chuanshu CAI
Author Information
1. 福建医科大学附属第一医院肿瘤中心放疗科,福建 福州 350005
2. 福建医科大学附属第一医院滨海院区国家区域医疗中心,福建 福州 350212
- Keywords:
cervical cancer;
automatic segmentation;
RT-Mind;
customized model
- From:
Chinese Journal of Medical Physics
2024;41(11):1337-1342
- CountryChina
- Language:Chinese
-
Abstract:
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-at-risk 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.
