Early detection of hepatocellular carcinoma co-occurring with hepatitis C virus infection: A mathematical model
| Autor: | Mohamed Abouelhoda, Abeer A. Bahnassy, Mostafa H. Elberry, Ola S. Ahmed, Yasser Mabrouk Bakr, Abdel-Rahman N. Zekri, Amira Salah El-Din Youssef, Ahmed Mahmoud Mayla, Reham Mohamed Gabr |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Liver Cirrhosis
Male viruses medicine.disease_cause 0302 clinical medicine Area under curve Proteasome endopeptidase complex Early Detection of Cancer beta Catenin Aged 80 and over Liver Neoplasms Gastroenterology General Medicine Hepatitis C Middle Aged Intercellular Adhesion Molecule-1 030220 oncology & carcinogenesis Hepatocellular carcinoma Area Under Curve 030211 gastroenterology & hepatology Egypt Female alpha-Fetoproteins Adult Proteasome Endopeptidase Complex Carcinoma Hepatocellular Hepatitis C virus Early detection Diagnosis Differential 03 medical and health sciences Young Adult Co occurring Predictive Value of Tests medicine Biomarkers Tumor Humans neoplasms Aged Retrospective Studies Chi-Square Distribution Models Statistical business.industry Reproducibility of Results Case Control Study medicine.disease Virology digestive system diseases Logistic Models ROC Curve Linear Models business |
| Popis: | 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).Serum levels of interleukin (IL)-8, soluble intercellular adhesion molecule-1 (sICAM-1), soluble tumor necrosis factor receptor II (sTNF-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.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 × sICAM + 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 × sICAM-1 + (4.251 × 10(-4)) × sTNF + 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.Our proposed mathematical model may be a useful method for the early detection of different statuses of liver disease co-occurring with HCV infection. |
| Databáze: | OpenAIRE |
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