Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
Published Date: Oct 31, 2022

Abstract

BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature.

Authors

  • Zhichao Zuo
    School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, China.
  • Peng Wang
    Neuroengineering Laboratory, School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China.
  • Weihua Zeng
    Department of Radiology, 117752Xiangtan Central Hospital, Xiangtan, PR China.
  • Wanyin Qi
    Department of Radiology, The Affiliated Hospital of Southwest Medical University, Taiping Street, Luzhou, 646000, Sichuan, China.
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.

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