The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer.

Journal: Radiation oncology (London, England)

Published Date: Jun 23, 2021

PURPOSE: To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer.

Hongbo Guo

Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Jiazhou Wang

Department of radiation oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Xiang Xia

Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Yang Zhong

Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Jiayuan Peng

Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Zhen Zhang

School of Pharmacy, Jining Medical University, Rizhao, Shandong, China.
Weigang Hu

Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.

Deep Learning Humans Image Processing, Computer-Assisted Nasopharyngeal Neoplasms Organs at Risk Prognosis Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Rectal Neoplasms Tomography, X-Ray Computed

View on PubMed Access via DOI PubMed (34162410)