Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 39838503

Abstract

BACKGROUND: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.

Authors

  • Qi Wan
    Department of Ophthalmology, The People's Hospital of Leshan, Leshan, 614000, China.
  • Clifford Lindsay
    Division of Nuclear Medicine, Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, USA.
  • Chenxi Zhang
    Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, 100730, China.
  • Jisoo Kim
    School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.
  • Xin Chen
    University of Nottingham, Nottingham, United Kingdom.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Raymond Y Huang
    Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts. kalpathy@nmr.mgh.harvard.edu yangli762@gmail.com ryhuang@partners.org.
  • David A Reardon
    Department of Radiology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
  • Geoffrey S Young
    Departments of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
  • Lei Qin

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