Novel machine-learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis.

Journal: Cancer medicine
PMID: 39123313

Abstract

BACKGROUND: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS.

Authors

  • Xiong-Gang Yang
    Department of Orthopedics, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.
  • Shan-Shan Yang
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150000, PR China. Electronic address: shanshanyang@hit.edu.cn.
  • Yi Bao
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Qi-Yang Wang
    Department of Orthopedics, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.
  • Zhi Peng
    Department of Gastrointestinal Oncology, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China.
  • Sheng Lu
    Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, PR China. Electronic address: lusheng@vip.126.com.

Keywords

External Resources

Popular Topics
Recent Journals