An investigation of machine learning methods in delta-radiomics feature analysis.

Journal: PloS one
Published Date:

Abstract

PURPOSE: This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurgery and bevacizumab, and to investigate the effectiveness of machine learning methods for delta-radiomics feature selection and building classification models.

Authors

  • Yushi Chang
    Medical Physics Graduate Program, Duke University, Durham, North Carolina, United States of America.
  • Kyle Lafata
    Department of Radiation Oncology, Duke University, Durham, North Carolina, USA.
  • Wenzheng Sun
    School of Information Science and Engineering, Shandong University, Qingdao, Shandong, 266237, People's Republic of China.
  • Chunhao Wang
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.
  • Zheng Chang
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, United States of America.
  • John P Kirkpatrick
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, United States of America.
  • Fang-Fang Yin
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.