Clinical evaluation of deep learning-based clinical target volume auto-segmentation algorithm for cervical cancer
10.3760/cma.j.cn113030-20191112-00475
- VernacularTitle:基于深度学习宫颈癌靶区自动分割勾画临床研究
- Author:
Chenying MA
1
;
Juying ZHOU
;
Xiaoting XU
;
Jian GUO
;
Miaofei HAN
;
Yaozong GAO
;
Zhanglong WANG
;
Jingjie ZHOU
Author Information
1. 苏州大学附属第一医院放疗科 215006
- From:
Chinese Journal of Radiation Oncology
2020;29(10):859-865
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To validate the feasibility of a deep learning-based clinical target volume (CTV) auto-segmentation algorithm for cervical cancer in clinical settings.Methods:CT data sets from 535 cervical cancer patients were collected. CTVs were delineated according to RTOG and JCOG guidelines, reviewed by experts, and then used as reference contours for training (definitive 177, post-operative 302) and test (definitive 23, post-operative 33). Four definitive and 6 post-operative cases were randomly selected from the testing cohort to be manually delineated by junior, intermediate, senior doctors, respectively. Dice coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD) were used for test and comparison between auto-segmentation and RO delineation. Meantime, auto-segmentation time and manual delineation time were recorded.Results:Auto-segmentation models of dCTV 1, dCTV 2 and pCTV 1 were trained with VB-Net and showed good agreement with reference contours in the testing cohorts (DSC, 0.88, 0.70, 0.86 mm; MSD, 1.32, 2.42, 1.15 mm; HD, 21.6, 22.4, 20.8 mm). For dCTV 1, the difference between auto-segmentation and all three groups of doctors was not significant ( P>0.05). For dCTV 2 and pCTV 1, auto-segmentation was better than the junior and intermediate doctors (both P<0.05). Auto-segmentation time consumption was considerably shorter than that of manual delineation. Conclusions:Deep learning-based CTV auto-segmentation algorithm for cervical cancer achieves comparable accuracy to manual delineation of senior doctors. Clinical application of the algorithm can contribute to shortening doctors′ manual delineation time and improving clinical efficiency. Furthermore, it may serve as a guide for junior doctors to improve the consistency and accuracy of cervical cancer CTV delineation in clinical practice.
