Application of Classification Tree and Neural Network Algorithms to the Identification of Serological Liver Marker Profiles for the Diagnosis of Hepatocellular Carcinoma

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Abstract
Objective: Although many attempts have been made to identify tumour-specific α-fetoprotein (AFP) glycoforms or other serological markers for the diagnosis of hepatocellular carcinoma (HCC), none of the available markers has, so far, shown satisfactory sensitivity and specificity. Here we aimed to apply classification tree and neural network algorithms to interpret the levels of multiple serological liver markers to improve overall specificity and sensitivity, particularly with a view to discriminating between liver cirrhosis with and without HCC. Methods: We developed classification trees and neural networks that identified serological liver marker profiles comprising AFP, α1-antitrypsin (A1AT), α2-macroglobulin (A2MG), thyroxine-binding globulin (TBG), transferrin and albumin as well as sex and age, which might permit the diagnosis of HCC. Data were collected from 65 HCC patients, 51 patients with liver cirrhosis alone (LC) and 51 normal healthy subjects. Results: The generated classification trees and neural networks showed similar diagnostic values in differentiating HCC from LC. The classification trees identified AFP, A1AT and albumin as the most important classification parameters, whereas the neural networks identified A2MG, AFP, A1AT and albumin as the predominant factors. The classification logic of the classification trees indicated that more HCC cases could be identified among cases with slightly elevated AFP levels by using the serum levels of A1AT and albumin. The neural networks were also useful for the identification of the HCC cases when the AFP levels were below 500 ng/ml (p < 0.005). The neural networks could identify HCC cases with AFP levels within the normal range, but the classification trees could not. By combining the conventional AFP test and the neural networks, the overall diagnostic sensitivity for HCC was significantly increased from 60.0 to 73.8% (p < 0.05) while maintaining a high specificity (88.2%). The sensitivities for tumors of different sizes were similar. Conclusion: The neural network algorithm appeared to be more powerful than the classification tree algorithm in the identification of the distinctive serological liver marker profiles for the diagnosis of the HCC subgroup without significant elevation in serum AFP levels. By incorporating serological levels of other liver markers and including data from a large number of patients and control subjects, it should prove possible to develop a versatile neural network for early diagnosis of HCC.