Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.

Journal: Biomedical engineering online
PMID: 40025592

Abstract

BACKGROUND: To develop and validate a model that integrates clinical data, deep learning radiomics, and radiomic features to predict high-risk patients for cage subsidence (CS) after lumbar fusion.

Authors

  • Congying Zou
    Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China.
  • Ruiyuan Chen
    Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Baodong Wang
    College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, China.
  • Qi Fei
    Department of Orthopedics, Beijing Friendship Hospital, Capital Medical University, No 95, Yong'an Road, Xicheng District, Beijing, 100050, China.
  • Hongxing Song
    Shenzhen Hydrology and Water Quality Center, Shenzhen 518038, China.
  • Lei Zang
    Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China. zanglei@ccmu.edu.cn.

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