Young oncologists benefit more than experts from deep learning-based organs-at-risk contouring modeling in nasopharyngeal carcinoma radiotherapy: A multi-institution clinical study exploring working experience and institute group style factor

Background: To comprehensively investigate the behaviors of oncologists with different working experiences and institute group styles in deep learning-based organs-at-risk (OAR) contouring. Methods: A deep learning-based contouring system (DLCS) was modeled from 188 CT datasets of patients with naso...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Ying Song, Junjie Hu, Qiang Wang, Chengrong Yu, Jiachong Su, Lin Chen, Xiaorui Jiang, Bo Chen, Lei Zhang, Qian Yu, Ping Li, Feng Wang, Sen Bai, Yong Luo, Zhang Yi
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2023-07-01
coleção:	Clinical and Translational Radiation Oncology
Assuntos:	Deep learning Contouring Organs-at-risk Nasopharyngeal carcinoma radiotherapy Working experience
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S2405630823000605
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!