Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.

Journal: Journal of cancer research and clinical oncology
PMID: 39900688

Abstract

OBJECTIVE: The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variability. Subsequently, a prediction model of gastric cancer (GC) serosal invasion was constructed in conjunction with radiomics and deep learning features, and a nomogram was generated to explore the clinical guiding significance.

Authors

  • Hui Shang
    Shanghai Chenshan Botanical Garden, Shanghai Chenshan Plant Science Research Centre, Chinese Academy of Sciences, Shanghai, China.
  • Tao Feng
    School of Pharmacy, Anhui University of Chinese Medicine, Anhui Key Laboratory of Modern Chinese Materia Medica Hefei 230012 People's Republic of China tfeng@mail.scuec.edu.cn wanggk@ahtcm.edu.cn.
  • Dong Han
    Department of Radiology, Affiliated Hospital of Chengde Medical College, Chengde Hebei, 067000, P.R.China.
  • Fengying Liang
    Affiliated Hospital of Chengde Medical University, Chengde, 067000, Hebei Province, China.
  • Bin Zhao
    University of Michigan Medical School, Ann Arbor, MI 48109, USA.
  • Lihang Xu
    Affiliated Hospital of Chengde Medical College Department of Radiology Chengde China.
  • Zhendong Cao
    Department of Radiology, the Affiliated Hospital of Chengde Medical University, Chengde, China.

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