Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.

Journal: European radiology
PMID: 39210161

Abstract

OBJECTIVES: This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions.

Authors

  • Yuchen Wang
    College of Management, University of Massachusetts Boston, Boston, MA, USA.
  • Qinghe Han
    Radiology Department, The Second Hospital of Jilin University, Changchun.
  • Baohong Wen
    Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan, China.
  • Bingbing Yang
    Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 DongJiaoMinXiang Street, DongCheng District, Beijing, 100730, China.
  • Chen Zhang
    Department of Dermatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Yang Song
    Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia. Electronic address: yson1723@uni.sydney.edu.au.
  • Luo Zhang
    Department of Otolaryngology, Head and Neck Surgery, Beijing TongRen Hospital and Beijing Institute of Otolaryngology, Beijing, China.
  • Junfang Xian
    Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China; Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China. Electronic address: cjr.xianjunfang@vip.163.com.

Keywords

External Resources

Popular Topics
Recent Journals