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
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Bo Li1,*, Boan Li1,*, Tongsheng Guo1, Zhiqiang Sun1, Xiaohan Li1,2, Xiaoxi Li1, Lin Chen1,2, Jing Zhao1 and Yuanli Mao1,2
1Center for Clinical Laboratory, 302 Millitary Hospital, Beijing, China
2Graduate student team, Medical University of PLA, Beijing, China
*These authors contributed equally to this work
Yuanli Mao, email: firstname.lastname@example.org
Keywords: hepatocellular carcinoma, artificial neural network, serum tumor biomarker
Received: April 07, 2017 Accepted: June 02, 2017 Published: July 17, 2017
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.
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