Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis.

Journal: Chinese medical journal
Published Date: Nov 20, 2020

Abstract

BACKGROUND: Liver fibrosis (LF) continues to develop and eventually progresses to cirrhosis. However, LF and early-stage cirrhosis (ESC) can be reversed in some cases, while advanced cirrhosis is almost impossible to cure. Advances in quantitative imaging techniques have made it possible to replace the gold standard biopsy method with non-invasive imaging, such as radiomics. Therefore, the purpose of this study is to develop a radiomics model to identify LF and ESC.

Authors

  • Qing-Tao Qiu
    Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Jing-Hao Duan
    Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China.
  • Shi-Zhang Wu
    Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China.
  • Jia-Lin Ding
    School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250358, China.
  • Yong Yin
    Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong, 250117, China.

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