Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI: A Multicenter Study.

Journal: Academic radiology
PMID: 39322536

Abstract

RATIONALE AND OBJECTIVES: Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pathological prediction outcomes by combining habitat analysis with deep learning.

Authors

  • Yunyang Zhu
    Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China (Y.Z., J.W., T.L.).
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Chen Xue
    Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China (C.X., C.X.).
  • Xiaoyang Zhai
    The First Affiliated Hospital of Xinxiang University, Xinxiang, China (X.Z.).
  • Chaoyong Xiao
    Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China (C.X., C.X.).
  • Ting Lu
    West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.

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