Artificial Neural Network Model for Liver Cirrhosis Diagnosis in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma.
Ther Clin Risk Manag
; 16: 639-649, 2020.
Article
en En
| MEDLINE
| ID: mdl-32764948
ABSTRACT
BACKGROUND:
Testing for the presence of liver cirrhosis (LC) is one of the most critical diagnostic and prognostic assessments for patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). More non-invasive tools are needed to diagnose LC but the predictive abilities of current models are still inconclusive. This study aimed to develop and validate a novel and non-invasive artificial neural network (ANN) model for diagnosing LC in patients with HBV-related HCC using routine laboratory serological indicators.METHODS:
A total of 1152 HBV-related HCC patients who underwent hepatectomy were included and randomly divided into the training set (n = 864, 75%) and validation set (n = 288, 25%). The ANN model was constructed from the training set using multivariate Logistic regression analysis and then verified in the validation set.RESULTS:
The morbidity of LC in the training and validation sets was 41.2% and 46.8%, respectively. Multivariate analysis showed that age, platelet count, prothrombin time and total bilirubin were independent risk factors for LC (P < 0.05). The area under the ROC curve (AUC) analyses revealed that the ANN model had higher predictive accuracy than the Logistic model (ANN 0.757 vs Logistic 0.721; P < 0.001), and other scoring systems (ANN 0.757 vs CP 0.532, MELD 0.594, ALBI 0.575, APRI 0.621, FIB-4 0.644, AAR 0.491, and GPR 0.604; P < 0.05 for all) in diagnosing LC. Similar results were obtained in the validation set.CONCLUSION:
The ANN model has better diagnostic capabilities than other commonly used models and scoring systems in assessing LC risk in patients with HBV-related HCC.
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Banco de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Año:
2020
Tipo del documento:
Article