Cargando…
Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and Golgi protein 73
More than 70% of hepatocellular carcinoma (HCC) cases develop as a consequence of liver cirrhosis (LC). Here we have evaluated the diagnostic potential of four serum biomarkers, and developed models for HCC diagnosis and differentiation from LC patients. Serum levels of α-fetoprotein (AFP), AFP-L3,...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655217/ https://www.ncbi.nlm.nih.gov/pubmed/29113322 http://dx.doi.org/10.18632/oncotarget.19298 |
_version_ | 1783273489192452096 |
---|---|
author | Li, Bo Li, Boan Guo, Tongsheng Sun, Zhiqiang Li, Xiaohan Li, Xiaoxi Chen, Lin Zhao, Jing Mao, Yuanli |
author_facet | Li, Bo Li, Boan Guo, Tongsheng Sun, Zhiqiang Li, Xiaohan Li, Xiaoxi Chen, Lin Zhao, Jing Mao, Yuanli |
author_sort | Li, Bo |
collection | PubMed |
description | More than 70% of hepatocellular carcinoma (HCC) cases develop as a consequence of liver cirrhosis (LC). Here we have evaluated the diagnostic potential of four serum biomarkers, and developed models for HCC diagnosis and differentiation from LC patients. Serum levels of α-fetoprotein (AFP), AFP-L3, des-γ-carboxy prothrombin (DCP), and Golgi protein 73 (GP73) were analyzed in 114 advanced HCC patients, 81 early stage HCC patients, and 152 LC patients. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks were used to construct the diagnostic models. Using all stages, HCC diagnostic models had a higher sensitivity (>70%) than the individual serum biomarkers, whereas only early stage HCC diagnostic models had a higher specificity (>80%). The early stage HCC diagnostic models could not be used as HCC screening tools due to their low sensitivity (about 40%). These results suggest that a combination of the two models might be used as a screening tool to distinguish early stage HCC patients from LC patients, thus improving prevention and treatment of HCC. |
format | Online Article Text |
id | pubmed-5655217 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-56552172017-11-06 Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and Golgi protein 73 Li, Bo Li, Boan Guo, Tongsheng Sun, Zhiqiang Li, Xiaohan Li, Xiaoxi Chen, Lin Zhao, Jing Mao, Yuanli Oncotarget Research Paper More than 70% of hepatocellular carcinoma (HCC) cases develop as a consequence of liver cirrhosis (LC). Here we have evaluated the diagnostic potential of four serum biomarkers, and developed models for HCC diagnosis and differentiation from LC patients. Serum levels of α-fetoprotein (AFP), AFP-L3, des-γ-carboxy prothrombin (DCP), and Golgi protein 73 (GP73) were analyzed in 114 advanced HCC patients, 81 early stage HCC patients, and 152 LC patients. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks were used to construct the diagnostic models. Using all stages, HCC diagnostic models had a higher sensitivity (>70%) than the individual serum biomarkers, whereas only early stage HCC diagnostic models had a higher specificity (>80%). The early stage HCC diagnostic models could not be used as HCC screening tools due to their low sensitivity (about 40%). These results suggest that a combination of the two models might be used as a screening tool to distinguish early stage HCC patients from LC patients, thus improving prevention and treatment of HCC. Impact Journals LLC 2017-07-17 /pmc/articles/PMC5655217/ /pubmed/29113322 http://dx.doi.org/10.18632/oncotarget.19298 Text en Copyright: © 2017 Li et al. http://creativecommons.org/licenses/by/3.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Research Paper Li, Bo Li, Boan Guo, Tongsheng Sun, Zhiqiang Li, Xiaohan Li, Xiaoxi Chen, Lin Zhao, Jing Mao, Yuanli Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and Golgi protein 73 |
title | Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and Golgi protein 73 |
title_full | Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and Golgi protein 73 |
title_fullStr | Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and Golgi protein 73 |
title_full_unstemmed | Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and Golgi protein 73 |
title_short | Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and Golgi protein 73 |
title_sort | artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-l3, des-γ-carboxy prothrombin, and golgi protein 73 |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655217/ https://www.ncbi.nlm.nih.gov/pubmed/29113322 http://dx.doi.org/10.18632/oncotarget.19298 |
work_keys_str_mv | AT libo artificialneuralnetworkmodelsforearlydiagnosisofhepatocellularcarcinomausingserumlevelsofafetoproteinafetoproteinl3desgcarboxyprothrombinandgolgiprotein73 AT liboan artificialneuralnetworkmodelsforearlydiagnosisofhepatocellularcarcinomausingserumlevelsofafetoproteinafetoproteinl3desgcarboxyprothrombinandgolgiprotein73 AT guotongsheng artificialneuralnetworkmodelsforearlydiagnosisofhepatocellularcarcinomausingserumlevelsofafetoproteinafetoproteinl3desgcarboxyprothrombinandgolgiprotein73 AT sunzhiqiang artificialneuralnetworkmodelsforearlydiagnosisofhepatocellularcarcinomausingserumlevelsofafetoproteinafetoproteinl3desgcarboxyprothrombinandgolgiprotein73 AT lixiaohan artificialneuralnetworkmodelsforearlydiagnosisofhepatocellularcarcinomausingserumlevelsofafetoproteinafetoproteinl3desgcarboxyprothrombinandgolgiprotein73 AT lixiaoxi artificialneuralnetworkmodelsforearlydiagnosisofhepatocellularcarcinomausingserumlevelsofafetoproteinafetoproteinl3desgcarboxyprothrombinandgolgiprotein73 AT chenlin artificialneuralnetworkmodelsforearlydiagnosisofhepatocellularcarcinomausingserumlevelsofafetoproteinafetoproteinl3desgcarboxyprothrombinandgolgiprotein73 AT zhaojing artificialneuralnetworkmodelsforearlydiagnosisofhepatocellularcarcinomausingserumlevelsofafetoproteinafetoproteinl3desgcarboxyprothrombinandgolgiprotein73 AT maoyuanli artificialneuralnetworkmodelsforearlydiagnosisofhepatocellularcarcinomausingserumlevelsofafetoproteinafetoproteinl3desgcarboxyprothrombinandgolgiprotein73 |