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

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...

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Vydané v:	Medical & biological engineering & computing Ročník 56; číslo 10; s. 1771 - 1779
Hlavní autori:	Zhang, Li, Chen, Jiasheng, Gao, Chunming, Liu, Chuanmiao, Xu, Kuihua
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2018 Springer Nature B.V
Predmet:	Adult Aged Aged, 80 and over Algorithms Antigens Artificial Intelligence Artificial neural networks Biomarkers Biomarkers, Tumor - blood Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Carbohydrates Carcinoembryonic antigen Carcinoma, Hepatocellular - blood Carcinoma, Hepatocellular - diagnosis Carcinoma, Hepatocellular - genetics Case-Control Studies Computer Applications Diagnosis Evolutionary algorithms Female Gene expression Gene Expression Regulation, Neoplastic Hepatocellular carcinoma Human Physiology Humans Imaging Liver cancer Liver Neoplasms - blood Liver Neoplasms - diagnosis Liver Neoplasms - genetics Male Mathematical models Medical diagnosis Middle Aged Multilayer perceptrons Neural networks Original Article Problem solving Radiology ROC Curve Test sets Young Adult α-Fetoprotein γ-Glutamyltransferase Hepatocellular carcinoma Auxiliary diagnosis Serum biomarkers Gene expression programming
ISSN:	0140-0118, 1741-0444, 1741-0444
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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. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma
AbstractList	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. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma.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. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma. 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. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma 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. 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. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma.
Author	Gao, Chunming Xu, Kuihua Liu, Chuanmiao Chen, Jiasheng Zhang, Li
Author_xml	– sequence: 1 givenname: Li surname: Zhang fullname: Zhang, Li email: zl_byfy@sina.com organization: Department of Infectious Disease, The First Affiliated Hospital of Bengbu Medical College – sequence: 2 givenname: Jiasheng surname: Chen fullname: Chen, Jiasheng organization: Department of Infectious Disease, The First Affiliated Hospital of Bengbu Medical College – sequence: 3 givenname: Chunming surname: Gao fullname: Gao, Chunming organization: Department of Infectious Disease, The First Affiliated Hospital of Bengbu Medical College – sequence: 4 givenname: Chuanmiao surname: Liu fullname: Liu, Chuanmiao organization: Department of Infectious Disease, The First Affiliated Hospital of Bengbu Medical College – sequence: 5 givenname: Kuihua surname: Xu fullname: Xu, Kuihua organization: Department of Infectious Disease, The First Affiliated Hospital of Bengbu Medical College
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SubjectTerms	Adult Aged Aged, 80 and over Algorithms Antigens Artificial Intelligence Artificial neural networks Biomarkers Biomarkers, Tumor - blood Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Carbohydrates Carcinoembryonic antigen Carcinoma, Hepatocellular - blood Carcinoma, Hepatocellular - diagnosis Carcinoma, Hepatocellular - genetics Case-Control Studies Computer Applications Diagnosis Evolutionary algorithms Female Gene expression Gene Expression Regulation, Neoplastic Hepatocellular carcinoma Human Physiology Humans Imaging Liver cancer Liver Neoplasms - blood Liver Neoplasms - diagnosis Liver Neoplasms - genetics Male Mathematical models Medical diagnosis Middle Aged Multilayer perceptrons Neural networks Original Article Problem solving Radiology ROC Curve Test sets Young Adult α-Fetoprotein γ-Glutamyltransferase
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Title	An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming
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