Utilizing machine learning to tailor radiotherapy and chemoradiotherapy for low-grade glioma patients.

Journal: PloS one
PMID: 39163387

Abstract

BACKGROUND: There is ongoing uncertainty about the effectiveness of various adjuvant treatments for low-grade gliomas (LGGs). Machine learning (ML) models that predict individual treatment effects (ITE) and provide treatment recommendations could help tailor treatments to each patient's needs.

Authors

  • Enzhao Zhu
    School of Medicine, Tongji University, Shanghai, China.
  • Jiayi Wang
    Department of Statistics, Texas A&M University.
  • Weizhong Shi
    Shanghai Hospital Development Center, Shanghai, China.
  • Zhihao Chen
    Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
  • Min Zhu
    Department of Infectious Diseases, Affiliated Taizhou Hospital of Wenzhou Medical University, No.50 Ximeng Road, Taizhou, 317000, China.
  • Ziqin Xu
    Department of Biobehavioral Sciences, Columbia University, New York, NY, United States of America.
  • Linlin Li
    Department of Clinical Pharmacy, School of Pharmacy, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, Shandong, 271016, China.
  • Dan Shan
    Department of Biobehavioral Sciences, Columbia University, New York, NY, United States of America.

Keywords

External Resources

Popular Topics
Recent Journals