Deep Learning Improved Clinical Target Volume Contouring Quality and Efficiency for Postoperative Radiation Therapy in Non-small Cell Lung Cancer

Deep Learning Improved Clinical Target Volume Contouring Quality and Efficiency for Postoperative Radiation Therapy in Non-small Cell Lung Cancer

Purpose: To investigate whether a deep learning-assisted contour (DLAC) could provide greater accuracy, inter-observer consistency, and efficiency compared with a manual contour (MC) of the clinical target volume (CTV) for non-small cell lung cancer (NSCLC) receiving postoperative radiotherapy (PORT...

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Bibliographic Details
Main Authors: Nan Bi, Jingbo Wang, Tao Zhang, Xinyuan Chen, Wenlong Xia, Junjie Miao, Kunpeng Xu, Linfang Wu, Quanrong Fan, Luhua Wang, Yexiong Li, Zongmei Zhou, Jianrong Dai
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-11-01
Series:Frontiers in Oncology
Subjects:
non-small cell lung cancer
postoperative radiotherapy
clinical target volume
deep learning
automatic contour
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2019.01192/full

Internet

https://www.frontiersin.org/article/10.3389/fonc.2019.01192/full

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