Abstract

BackgroundCurrently, a surgical approach is the best curative treatment for those with hepatocellular carcinoma (HCC). However, this requires HCC detection and removal of the lesion at an early stage. Unfortunately, most cases of HCC are detected at an advanced stage because of the lack of accurate biomarkers that can be used in the surveillance of those at risk. It is believed that biomarkers that could detect HCC early will play an important role in the successful treatment of HCC.MethodsIn this study, we analyzed serum levels of alpha fetoprotein, Golgi protein, fucosylated alpha-1-anti-trypsin, and fucosylated kininogen from 113 patients with cirrhosis and 164 serum samples from patients with cirrhosis plus HCC. We utilized two different methods, namely, stepwise penalized logistic regression (stepPLR) and model-based classification and regression trees (mob), along with the inclusion of clinical and demographic factors such as age and gender, to determine if these improved algorithms could be used to increase the detection of cancer.Results and discussionThe performance of multiple biomarkers was found to be better than that of individual biomarkers. Using several statistical methods, we were able to detect HCC in the background of cirrhosis with an area under the receiver operating characteristic curve of at least 0.95. stepPLR and mob demonstrated better predictive performance relative to logistic regression (LR), penalized LR and classification and regression trees (CART) used in our prior study based on three-fold cross-validation and leave one out cross-validation. In addition, mob provided unparalleled intuitive interpretation of results and potential cut-points for biomarker levels. The inclusion of age and gender improved the overall performance of both methods among all models considered, while the stratified male-only subset provided the best overall performance among all methods and models considered.ConclusionsIn addition to multiple biomarkers, the incorporation of age and gender into statistical models significantly improved their predictive performance in the detection of HCC.

Highlights

  • A surgical approach is the best curative treatment for those with hepatocellular carcinoma (HCC)

  • The major etiology of hepatocellular carcinoma is infection with hepatitis B virus (HBV) and/or hepatitis C virus (HCV) [1,2,3,4,5], which can lead to liver cirrhosis, the main risk factor for HCC

  • It is evident from the results reported in our previous study [26] that univariate logistic regression (LR) models performed uniformly worse than multivariable models that utilized multiple biomarkers using any of the three methods considered in that study, namely, multivariable LR, PLR and classification and regression trees (CART)

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Summary

Introduction

A surgical approach is the best curative treatment for those with hepatocellular carcinoma (HCC). This requires HCC detection and removal of the lesion at an early stage. Surgical treatments, such as tumor ablation, resection and transplantation still offer the best hope for long term survival but work best when tumors are caught at an early stage. The use of AFP as the primary screen for HCC is questioned [11] and more specific and sensitive, serum biomarkers for HCC are urgently needed [12,13,14,15,16]

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