The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.

Abstract

Authors

• Zhengbo Song
  Department of Clinical Trial, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, Zhejiang, China.
• Tianchi Liu
  School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore. Electronic address: tcliu@ntu.edu.sg.
• Lei Shi
• Zongyang Yu
  Department of Medical Oncology, 900th Hospital, Fuzhou, 350000, Fujian, China.
• Qing Shen
  Department of Clinical Laboratory, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, 324000, China.
• Mengdi Xu
  Diannei Technology, Shanghai, China.
• Zhangzhou Huang
  Department of Medical Oncology, Fujian Cancer Hospital, Fuzhou, 350001, China.
• Zhijian Cai
  Institute of Immunology, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
• Wenxian Wang
  Department of Clinical Trial, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, Zhejiang, China.
• Chunwei Xu
  Department of Pathology, Fujian Cancer Hospital, Fuzhou, 350001, China.
• Jingjing Sun
  School of Public Administration, Guangzhou University, Guangzhou, China.
• Ming Chen
  Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China.

Keywords

External Resources