Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.
J Appl Clin Med Phys
; 23(2): e13470, 2022 Feb.
Article
em En
| MEDLINE
| ID: mdl-34807501
ABSTRACT
OBJECTIVES:
Because radiotherapy is indispensible for treating cervical cancer, it is critical to accurately and efficiently delineate the radiation targets. We evaluated a deep learning (DL)-based auto-segmentation algorithm for automatic contouring of clinical target volumes (CTVs) in cervical cancers.METHODS:
Computed tomography (CT) datasets from 535 cervical cancers treated with definitive or postoperative radiotherapy were collected. A DL tool based on VB-Net was developed to delineate CTVs of the pelvic lymph drainage area (dCTV1) and parametrial area (dCTV2) in the definitive radiotherapy group. The training/validation/test number is 157/20/23. CTV of the pelvic lymph drainage area (pCTV1) was delineated in the postoperative radiotherapy group. The training/validation/test number is 272/30/33. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) were used to evaluate the contouring accuracy. Contouring times were recorded for efficiency comparison.RESULTS:
The mean DSC, MSD, and HD values for our DL-based tool were 0.88/1.32 mm/21.60 mm for dCTV1, 0.70/2.42 mm/22.44 mm for dCTV2, and 0.86/1.15 mm/20.78 mm for pCTV1. Only minor modifications were needed for 63.5% of auto-segmentations to meet the clinical requirements. The contouring accuracy of the DL-based tool was comparable to that of senior radiation oncologists and was superior to that of junior/intermediate radiation oncologists. Additionally, DL assistance improved the performance of junior radiation oncologists for dCTV2 and pCTV1 contouring (mean DSC increases 0.20 for dCTV2, 0.03 for pCTV1; mean contouring time decrease 9.8 min for dCTV2, 28.9 min for pCTV1).CONCLUSIONS:
DL-based auto-segmentation improves CTV contouring accuracy, reduces contouring time, and improves clinical efficiency for treating cervical cancer.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias do Colo do Útero
/
Aprendizado Profundo
Tipo de estudo:
Etiology_studies
Limite:
Female
/
Humans
Idioma:
En
Revista:
J Appl Clin Med Phys
Assunto da revista:
BIOFISICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China