Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study.

Abstract

Authors

• Yan Kong
  Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China.
• Mingming Su
  Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
• Yan Zhu
  Department of Chemistry, Xixi Campus, Zhejiang University, Hangzhou, 310028, China. Electronic address: zhuyan@zju.edu.cn.
• Xuan Li
  College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, China.
• Jinmeng Zhang
  Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
• Wenchao Gu
  Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan.
• Fei Yang
  Hunan Province Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, University of South China, Hengyang 421001, China.
• Jialiang Zhou
  Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
• Jianjiao Ni
  Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China.
• Xi Yang
  Department of Health Outcomes and Biomedical Informatics.
• Zhengfei Zhu
  Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China. fuscczzf@163.com.
• Jianfeng Huang
  Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China. 9862019007@jiangnan.edu.cn.

Keywords

External Resources