Prognostic model for predicting recurrence in hepatocellular carcinoma patients with high systemic immune-inflammation index based on machine learning in a multicenter study.

Journal: Frontiers in immunology
PMID: 39315112

Abstract

INTRODUCTION: This study aims to use machine learning to conduct in-depth analysis of key factors affecting the recurrence of HCC patients with high preoperative systemic immune-inflammation index (SII) levels after receiving ablation treatment, and based on this, construct a nomogram model for predicting recurrence-free survival (RFS) of patients.

Authors

  • Ningning Lu
    Interventional Therapy Center for Oncology, Beijing You'an Hospital, Capital Medical University, Beijing, China.
  • Shugui Sheng
    Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Yiqi Xiong
    Interventional Therapy Center for Oncology, Beijing You'an Hospital, Capital Medical University, Beijing, China.
  • Chuanren Zhao
    Department of Cancer Center, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Wenying Qiao
    Department of Cancer Center, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Xiaoyan Ding
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
  • Jinglong Chen
    State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China. Electronic address: jlstrive2008@mail.xjtu.edu.cn.
  • Yonghong Zhang
    Interventional Therapy Center for Oncology, Beijing You'an Hospital, Capital Medical University, Beijing, China.

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