A radiomics feature-based machine learning models to detect brainstem infarction (RMEBI) may enable early diagnosis in non-contrast enhanced CT.

Journal: European radiology
PMID: 36169689

Abstract

OBJECTIVES: Magnetic resonance imaging has high sensitivity in detecting early brainstem infarction (EBI). However, MRI is not practical for all patients who present with possible stroke and would lead to delayed treatment. The detection rate of EBI on non-contrast computed tomography (NCCT) is currently very low. Thus, we aimed to develop and validate the radiomics feature-based machine learning models to detect EBI (RMEBIs) on NCCT.

Authors

  • Haiyan Zhang
    School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.
  • Hongyi Chen
    Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.
  • Chao Zhang
    School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
  • Aihong Cao
    Department of Radiology, the Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
  • Qingqing Lu
    Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Jun Zhang
    First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Daoying Geng
    Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai, 200040, China. GengdaoyingGDY@163.com.

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