Abstract
Because of its multifactorial nature, predicting the presence of cancer using a single biomarker is difficult. We aimed to establish a novel machine-learning model for predicting hepatocellular carcinoma (HCC) using real-world data obtained during clinical practice. To establish a predictive model, we developed a machine-learning framework which developed optimized classifiers and their respective hyperparameter, depending on the nature of the data, using a grid-search method. We applied the current framework to 539 and 1043 patients with and without HCC to develop a predictive model for the diagnosis of HCC. Using the optimal hyperparameter, gradient boosting provided the highest predictive accuracy for the presence of HCC (87.34%) and produced an area under the curve (AUC) of 0.940. Using cut-offs of 200 ng/mL for AFP, 40 mAu/mL for DCP, and 15% for AFP-L3, the accuracies of AFP, DCP, and AFP-L3 for predicting HCC were 70.67% (AUC, 0.766), 74.91% (AUC, 0.644), and 71.05% (AUC, 0.683), respectively. A novel predictive model using a machine-learning approach reduced the misclassification rate by about half compared with a single tumor marker. The framework used in the current study can be applied to various kinds of data, thus potentially become a translational mechanism between academic research and clinical practice.
Highlights
Hepatocellular carcinoma (HCC) is one of the commonest cancers and is the leading cause of cancer-related deaths worldwide[1]
The proportions of patients with a male sex, hepatitis C virus (HCV) antibody-positivity, and hepatitis B surface (HBs) antigen-negativity were significantly higher among the hepatocellular carcinoma (HCC) patients, compared with the non-HCC patients
In addition to tumor marker levels, biomarkers of liver inflammation, liver fibrosis, liver function, and the hepatitis virus status are commonly measured in daily clinical practice
Summary
Hepatocellular carcinoma (HCC) is one of the commonest cancers and is the leading cause of cancer-related deaths worldwide[1]. In addition to information on tumor markers, data on biomarkers of liver inflammation (aspartate aminotransferase [AST] and alanine aminotransferase [ALT]), fibrosis (platelet count)[19], liver function (total bilirubin [TB] and albumin)[20], and the hepatitis virus status are commonly available in daily clinical practice. These biomarkers alter the pretest probability for a diagnosis of HCC using tumor marker and are useful for predicting the presence of HCC. Combining clinical data using this analytical tool can enable the development of a novel model for HCC prediction. Machine-learning framework to establish the most appropriate model depending on the applied data, and (2) to apply this framework to existing data from HCC patients to develop an appropriate model for HCC prediction
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