Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules.

Abstract

Authors

• Long Jiang
  Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai xxxx, China.
• Yang Zhou
  State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China.
• Wang Miao
  Department of Thoracic Surgery, The Third People's Hospital of Zhengzhou, Zhengzhou, China.
• Hongda Zhu
  Department of Thoracic Surgical Oncology, Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
• Ningyuan Zou
  Department of Thoracic Surgical Oncology, Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
• Yu Tian
  Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
• Hanbo Pan
  Department of Thoracic Surgical Oncology, Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
• Weiqiu Jin
  School of Medicine, Shanghai Jiao Tong University, 200025, Shanghai, PR China.
• Jia Huang
  Shanghai Lung Tumor Clinical Medical Center, Shanghai Chest Hospital, Shanghai Jiao Tong University (SJTU), Shanghai 200030, China.
• Qingquan Luo
  Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai xxxx, China.

Keywords

External Resources