Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections.

Journal: Radiation oncology (London, England)
PMID: 40251610

Abstract

BACKGROUND: Delineating the internal gross tumor volume (IGTV) is crucial for the treatment of non-small cell lung cancer (NSCLC). Deep learning (DL) enables the automation of this process; however, current studies focus mainly on multiple phases of four-dimensional (4D) computed tomography (CT), which leads to indirect results. This study proposed a DL-based method for automatic IGTV delineation using maximum and average intensity projections (MIP and AIP, respectively) from 4D CT.

Authors

  • Yuling Huang
    Departments of Geriatrics, The First Hospital of China Medical University, Shenyang, Liaoning 110001, PR China.
  • Mingming Luo
    Department of Radiation Oncology, Jiangxi Cancer Hospital & Institute (The Second Affiliated Hospital of Nanchang Medical College), Nanchang, 330029, Jiangxi, PR China.
  • Zan Luo
    Department of Radiation Oncology, Jiangxi Cancer Hospital & Institute (The Second Affiliated Hospital of Nanchang Medical College), Nanchang, 330029, Jiangxi, PR China.
  • Mingzhi Liu
    Department of Radiation Oncology, Jiangxi Cancer Hospital & Institute (The Second Affiliated Hospital of Nanchang Medical College), Nanchang, 330029, Jiangxi, PR China.
  • Junyu Li
    School of Electrical and Mechanical Engineering, Hefei Technology College, Hefei, China.
  • Junming Jian
    University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • Yun Zhang
    Biology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.

Keywords

External Resources

Popular Topics
Recent Journals