Early detection of hepatocellular carcinoma co-occurring with hepatitis C virus infection: A mathematical model

• Published in: World journal of gastroenterology : WJG 2016-04, Vol.22 (16), p.4168-4182
• Main Authors: Zekri, Abdel-Rahman Nabawy, Youssef, Amira Salah El-Din, Bakr, Yasser Mabrouk, Gabr, Reham Mohamed, Ahmed, Ola Sayed, Elberry, Mostafa Hamed, Mayla, Ahmed Mahmoud, Abouelhoda, Mohamed, Bahnassy, Abeer A
• Format: Article
• Language: English
• Subjects: adhesion; Adult; Aged; Aged, 80 and over; alpha-Fetoproteins - analysis; Area Under Curve; beta Catenin - blood; Biomarkers, Tumor - blood; Carcinoma, Hepatocellular - blood; Carcinoma, Hepatocellular - diagnosis; Carcinoma, Hepatocellular - virology; carcinoma; α-fetoprotein; Soluble; Case Control Study; Chi-Square Distribution; Diagnosis, Differential; Early Detection of Cancer; Egypt; Female; Hepatitis C - blood; Hepatitis C - complications; Hepatitis C - diagnosis; Humans; intercellular; Intercellular Adhesion Molecule-1 - blood; Linear Models; Liver Cirrhosis - blood; Liver Cirrhosis - diagnosis; Liver Cirrhosis - virology; Liver Neoplasms - blood; Liver Neoplasms - diagnosis; Liver Neoplasms - virology; Logistic Models; Male; Mathematical; Middle Aged; model; Hepatocellular; Models, Statistical; molecule-1; Predictive Value of Tests; Proteasome Endopeptidase Complex - blood; Reproducibility of Results; Retrospective Studies; ROC Curve; Young Adult
• ISSN: 1007-9327; 2219-2840
• DOI: 10.3748/wjg.v22.i16.4168

AIM: To develop a mathematical model for the early detection of hepatocellular carcinoma(HCC) with a panel of serum proteins in combination with α-fetoprotein(AFP).METHODS: Serum levels of interleukin(IL)-8, soluble intercellular adhesion molecule-1(s ICAM-1), soluble tumor necrosis factor receptor Ⅱ(s TNF-RⅡ), proteasome, and β-catenin were measured in 479 subjects categorized into four groups:(1) HCC concurrent with hepatitis C virus(HCV) infection(n = 192);(2) HCV related liver cirrhosis(LC)(n = 96);(3) Chronic hepatitis C(CHC)(n = 96); and(4) Healthy controls(n = 95). The R package and different modules for binary and multi-class classifiers based on generalized linear models were used to model the data. Predictive power was used to evaluate the performance of the model. Receiver operating characteristic curve analysis over pairs of groups was used to identify the best cutoffs differentiating the different groups. RESULTS: We revealed mathematical models, based on a binary classifier, made up of a unique panel of serum proteins that improved the individual performance of AFP in discriminating HCC patients from patients with chronic liver disease either with or without cirrhosis. We discriminated the HCC group from the cirrhotic liver group using a mathematical model(-11.3 + 7.38 × Prot + 0.00108 × s ICAM + 0.2574 ×β-catenin + 0.01597 × AFP) with a cutoff of 0.6552, which achieved 98.8% specificity and 89.1% sensitivity. For the discrimination of the HCC group from the CHC group, we used a mathematical model [-10.40 + 1.416 × proteasome + 0.002024 × IL + 0.004096 × s ICAM-1 +(4.251 × 10-4) × s TNF + 0.02567 ×β-catenin + 0.02442 × AFP] with a cutoff 0.744 and achieved 96.8% specificity and 89.7% sensitivity. Additionally, we derived an algorithm, based on a binary classifier, for resolving the multi-class classification problem by using three successive mathematical model predictions of liver disease status. CONCLUSION: Our proposed mathematical model may be a useful method for the early detection of different statuses of liver disease co-occurring with HCV infection.