Feasibility of automatic segmentation of CTV and OARs in postoperative radiotherapy for cervical cancer using AccuLearning
10.3969/j.issn.1006-5725.2024.02.005
- VernacularTitle:AccuLearning自动勾画临床靶区和危及器官用于宫颈癌术后放疗的可行性研究
- Author:
Fei CHEN
1
;
Xiaoqin GONG
;
Yunpeng YU
;
Tao YOU
;
Xu WANG
;
Chunhua DAI
;
Jing HU
Author Information
1. 江苏大学附属医院放疗科(江苏镇江 212000)
- Keywords:
automatic segmentation;
cervical cancer;
clinical target volume;
organs at risk;
dosimetry
- From:
The Journal of Practical Medicine
2024;40(2):153-157
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the feasibility of automatic segmentation of clinical target volume(CTV)and organs at risk(OARs)for cervical cancer using AccuLearning(AL)based on geometric and dosimetric indices.Methods Seventy-five CT localization images with manual contouring data of postoperative cervical cancer were enrolled in this study.Sixty cases were randomly selected to trained to generate automatic segmentation model by AL,and the CTV and OARs of the remaining 15 cases were automatically contoured.Radiotherapy plans on the automatic segmentation contours were imported on the CT images of manual contours.The efficiency,Dice similarity coefficient(DSC),Hausdorff distance(HD)and dosimetric parameters were compared between the two methods.Results The time of automatic segmentation was significantly shorter than that of the manual contour(P<0.05).The DSC of all structures were≥0.87.The HD of bowel bag and rectum were about 10 mm,and that of the rest of OARs were less than 5 mm.CTV(D98,V90% ,V95% ,Dmean,HI),bowel bag(V50)and bladder(V50)had significant differences in dosimetric comparison(P<0.05).Conclusion The automatic segmentation model based on AL can improve the efficiency of radiotherapy.Automatic segmentation of OARs has the potential of clinical application,while that of CTV still needs to be further modified.
