Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: May 26, 2024

Abstract

BACKGROUND: Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable.

Authors

  • Ting-Wei Wang
    Department of Medical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
  • Jia-Sheng Hong
    Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.
  • Jing-Wen Huang
    Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan.
  • Chien-Yi Liao
    Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Chia-Feng Lu
    Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.
  • Yu-Te Wu
    Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan. ytwu@ym.edu.tw.

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