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An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming

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Abstract

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model to diagnose HCC based on gene expression programming (GEP). GEP is an evolutionary algorithm and a domain-independent problem-solving technique. Clinical data show that six serum biomarkers, including gamma-glutamyl transferase, C-reaction protein, carcinoembryonic antigen, alpha-fetoprotein, carbohydrate antigen 153, and carbohydrate antigen 199, are related to HCC characteristics. In this study, the prediction of HCC was made based on these six biomarkers (195 HCC patients and 215 non-HCC controls) by setting up optimal joint models with GEP. The GEP model discriminated 353 out of 410 subjects, representing a determination coefficient of 86.28% (283/328) and 85.37% (70/82) for training and test sets, respectively. Compared to the results from the support vector machine, the artificial neural network, and the multilayer perceptron, GEP showed a better outcome. The results suggested that GEP modeling was a promising and excellent tool in diagnosis of hepatocellular carcinoma, and it could be widely used in HCC auxiliary diagnosis.

The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma

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Funding

This work was funded by Bengbu Medical College (Grant No. BYKY1677).

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Authors and Affiliations

  1. Department of Infectious Disease, The First Affiliated Hospital of Bengbu Medical College, 287 Changhuai Road, Bengbu, Anhui, China

    Li Zhang, Jiasheng Chen, Chunming Gao, Chuanmiao Liu & Kuihua Xu

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  1. Li Zhang
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Correspondence to Li Zhang.

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Informed consent was obtained from all individual participants included in the study.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Zhang, L., Chen, J., Gao, C. et al. An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming. Med Biol Eng Comput 56, 1771–1779 (2018). https://doi.org/10.1007/s11517-018-1811-6

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  • DOI: https://doi.org/10.1007/s11517-018-1811-6

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