Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.

Journal: Academic radiology
Published Date: Aug 1, 2024

Abstract

RATIONALE AND OBJECTIVES: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set.

Authors

  • Yasen Yimit
    Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000.
  • Parhat Yasin
    Department of Spine Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China, 830054.
  • Abudouresuli Tuersun
    Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, China.
  • Jingru Wang
    School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi'an, 710072, China.
  • Xiaohong Wang
    School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China. wxhong@buaa.edu.cn.
  • Chencui Huang
    Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Saimaitikari Abudoubari
    Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000.
  • Xingzhi Chen
    Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, PR China, 100080.
  • Irshat Ibrahim
    Department of General Surgery, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000.
  • Pahatijiang Nijiati
    Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, China.
  • Yunling Wang
    Department of Imaging Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China, 830054.
  • Xiaoguang Zou
    The First People's Hospital of Kashi, Xinjiang, China.
  • Mayidili Nijiati
    The First People's Hospital of Kashi, Xinjiang, China.

Keywords

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