Evidence map›Paper›PMID 27122667›Full record

ArticleWorld journal of gastroenterology2016

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

Abdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Mohamed Abouelhoda, Abeer A Bahnassy

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of gastroenterology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Abdel-Rahman Nabawy ZekriAbdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt.
Amira Salah El-Din YoussefAbdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt.
Yasser Mabrouk BakrAbdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt.
Reham Mohamed GabrAbdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt.
Ola Sayed AhmedAbdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt.
Mostafa Hamed ElberryAbdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt.
Ahmed Mahmoud MaylaAbdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt.
Mohamed AbouelhodaAbdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt.
Abeer A BahnassyAbdel-Rahman Nabawy Zekri, Amira Salah El-Din Youssef, Yasser Mabrouk Bakr, Reham Mohamed Gabr, Ola Sayed Ahmed, Mostafa Hamed Elberry, Ahmed Mahmoud Mayla, Molecular Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Cairo 11976, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo develop a mathematical model for the early detection of hepatocellular carcinoma (HCC) with a panel of serum proteins in combination with α-fetoprotein (AFP).

methodsSerum 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.

resultsWe 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.

conclusionOur proposed mathematical model may be a useful method for the early detection of different statuses of liver disease co-occurring with HCV infection.

Indexed as

Early Detection of CancerModels, StatisticalAdultAgedAged, 80 and overalpha-FetoproteinsArea Under Curvebeta CateninBiomarkers, TumorCarcinoma, HepatocellularChi-Square DistributionDiagnosis, DifferentialEgyptFemaleHepatitis CHumansAFP protein, humanalpha-Fetoproteinsbeta CateninBiomarkers, TumorCTNNB1 protein, humanICAM1 protein, humanIntercellular Adhesion Molecule-1Proteasome Endopeptidase ComplexHepatocellular carcinomaInterleukin-8Mathematical modelProteasomeSoluble intercellular adhesion molecule-1Soluble tumor necrosis factor receptor IIα-fetoproteinβ-catenin

Identifiers

PMID27122667
PMCPMC4837434

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