Deep Learning-based U-Mamba Model to Predict Differentiated Gastric Cancer using Radiomics Features from Spleen Segmentation.

Journal: Current medical imaging
PMID: 39710921

Abstract

OBJECTIVE: This study aimed to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variability. Subsequently, a prediction model for gastric cancer (GC) differentiation was constructed alongside radiomics, and a nomogram was generated to investigate its clinical guiding significance.

Authors

  • Hui Shang
    Shanghai Chenshan Botanical Garden, Shanghai Chenshan Plant Science Research Centre, Chinese Academy of Sciences, Shanghai, China.
  • Ying Tong
    School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167.
  • Mingyu Li
    College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.
  • Shuangyan Xu
    Department of Radiology, Affiliated Hospital of Chengde Medical College, Chengde, China.
  • 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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