A Machine Learning Approach Yields a Multiparameter Prognostic Marker in Liver Cancer.

Journal: Cancer immunology research
PMID: 33431375

Abstract

A number of staging systems have been developed to predict clinical outcomes in hepatocellular carcinoma (HCC). However, no general consensus has been reached regarding the optimal model. New approaches such as machine learning (ML) strategies are powerful tools for incorporating risk factors from multiple platforms. We retrospectively reviewed the baseline information, including clinicopathologic characteristics, laboratory parameters, and peripheral immune features reflecting T-cell function, from three HCC cohorts. A gradient-boosting survival (GBS) classifier was trained with prognosis-related variables in the training dataset and validated in two independent cohorts. We constructed a 20-feature GBS model classifier incorporating one clinical feature, 14 laboratory parameters, and five T-cell function parameters obtained from peripheral blood mononuclear cells. The GBS model-derived risk scores demonstrated high concordance indexes (C-indexes): 0.844, 0.827, and 0.806 in the training set and validation sets 1 and 2, respectively. The GBS classifier could separate patients into high-, medium- and low-risk subgroups with respect to death in all datasets ( < 0.05 for all comparisons). A higher risk score was positively correlated with a higher clinical stage and the presence of portal vein tumor thrombus (PVTT). Subgroup analyses with respect to Child-Pugh class, Barcelona Clinic Liver Cancer stage, and PVTT status supported the prognostic relevance of the GBS-derived risk algorithm independent of the conventional tumor staging system. In summary, a multiparameter ML algorithm incorporating clinical characteristics, laboratory parameters, and peripheral immune signatures offers a different approach to identify patients with the greatest risk of HCC-related death.

Authors

  • Xiaoli Liu
    Neurology Department, Zhejiang Hospital, Zhejiang 310013, China.
  • Jilin Lu
    Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, P.R. China.
  • Guanxiong Zhang
    Genecast Precision Medicine Technology Institute, Beijing, P.R. China.
  • Junyan Han
    Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, P.R. China.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Huan Chen
    Beijing Guangqumen Middle School, Beijing, 100062, China.
  • Henghui Zhang
    Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China shenlin@bjmu.edu.cn zhhbao@ccmu.edu.cn.
  • Zhiyun Yang
    Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China. yangzhiyun66@163.com.

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