Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China.

Journal: Cancer medicine
PMID:

Abstract

OBJECTIVE: We have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results.

Authors

  • Maoyu Wang
    Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
  • Zhizhou Li
    Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
  • Shuxiong Zeng
    Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
  • Ziwei Wang
    School of Information Technology and Electrical Engineering, University of Queensland, Brisbane Australia.
  • Yidie Ying
    Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
  • Wei He
    Department of Orthopaedics Surgery, First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China.
  • Zhensheng Zhang
    Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
  • Huiqing Wang
    College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
  • Chuanliang Xu
    Department of Urology, Changhai Hospital, Second Military University, Shanghai, China.