Open Access<p>Artificial Neural Network Model for Liver Cirrhosis Diagnosis in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma</p>
Author(s) -
Rongyun Mai,
Jie Zeng,
Yi-shuai Mo,
Rong Liang,
Yan Lin,
Susu Wu,
Xuemin Piao,
Xing Gao,
Guobin Wu,
LeQun Li,
Jiazhou Ye
Publication year - 2020
Publication title -
therapeutics and clinical risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 55
eISSN - 1178-203X
pISSN - 1176-6336
DOI - 10.2147/tcrm.s257218
Subject(s) - medicine , hepatocellular carcinoma , cirrhosis , logistic regression , hepatitis b virus , gastroenterology , prothrombin time , receiver operating characteristic , liver cancer , multivariate analysis , oncology , virus , immunology
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.