Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.

Journal: BMC medical imaging
Published Date: Sep 13, 2024

Abstract

BACKGROUND: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.

Authors

  • Junjie Bin
    Department of Radiology, The Affilitated Huizhou Hospital, Guangzhou Medical University, Huizhou, Guangdong, China. 173043256@qq.com.
  • Mei Wu
    Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China.
  • Meiyun Huang
    The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, Guangdong, China.
  • Yuguang Liao
    Department of Radiology, The Affilitated Huizhou Hospital, Guangzhou Medical University, Huizhou, Guangdong, China.
  • Yuli Yang
    Department of Radiology, The Affilitated Huizhou Hospital, Guangzhou Medical University, Huizhou, Guangdong, China.
  • Xianqiong Shi
    Department of Radiology, The Affilitated Huizhou Hospital, Guangzhou Medical University, Huizhou, Guangdong, China.
  • Siqi Tao
    The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, Guangdong, China.

Keywords

External Resources

Popular Topics
Recent Journals