A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign.

Journal: European radiology
Published Date: Mar 1, 2024

Abstract

OBJECTIVES: With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN management algorithm avoiding "one-size fits all" approach. One critical problem is how to learn the discriminative multi-view characteristics and the unique context of each SCPN.

Authors

  • Xiongwen Yang
    Department of Thoracic Surgery, Guizhou Provincial People's Hospital, No. 83, Zhongshan East Road, Guiyang, , Guizhou, China. yangxiongwen@gz5055.com.
  • Xiang-Peng Chu
    School of Medicine, South China University of Technology, Guangzhou, China.
  • Shaohong Huang
    Department of Cardio-Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Yi Xiao
    Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.
  • Dantong Li
    Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
  • Xiaoyang Su
    Department of Thoracic Surgery, Maoming City People's Hospital, Maoming, China.
  • Yi-Fan Qi
    School of Medicine, South China University of Technology, Guangzhou, China.
  • Zhen-Bin Qiu
    School of Medicine, South China University of Technology, Guangzhou, China.
  • Yanqing Wang
    Clinical Research Management Center, Livzon Pharmaceutical Group Inc., Zhuhai, Guangdong, China.
  • Wen-Fang Tang
    Department of Cardio-Thoracic Surgery, Zhongshan City People's Hospital, Zhongshan, China.
  • Yi-Long Wu
    Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China. Electronic address: syylwu@live.cn.
  • Qikui Zhu
  • Huiying Liang
    Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
  • Wen-Zhao Zhong
    Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China 13609777314@163.com syylwu@live.cn.

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