Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

Journal: Academic radiology
Published Date: Mar 7, 2024

Abstract

BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression.

Authors

  • Jiaying Ni
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
  • Hongjian Zhang
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
  • Qing Yang
    School of Nursing, Chengdu Medical College, Chengdu, China.
  • Xiao Fan
    Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China.
  • Junqing Xu
    The second Clinical Medical School, Nanjing Medical University, Nanjing 211166, China.
  • Jianing Sun
    School of Public Health, Nanjing Medical University, Nanjing 211166, China.
  • Junxia Zhang
    Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
  • Yifang Hu
    Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
  • Zheming Xiao
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
  • Yuhong Zhao
    Department of Medical Informatics, China Medical University, Shenyang, Liaoning, China.
  • Hongli Zhu
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
  • Xian Shi
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
  • Wei Feng
    Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You'anmenwai, Xitoutiao No.10, Beijing, P. R. China.
  • Junjie Wang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Cheng Wan
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Yun Liu
    Google Health, Palo Alto, CA USA.
  • Yongping You
    Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
  • Yun Yu
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China. yuyun@njmu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals