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Artificial Neural Network Model for Liver Cirrhosis Diagnosis in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma

Authors Mai R, Zeng J, Mo Y, Liang R, Lin Y, Wu S, Piao X, Gao X, Wu G, Li L, Ye J

Received 8 April 2020

Accepted for publication 22 May 2020

Published 17 July 2020 Volume 2020:16 Pages 639—649

DOI https://doi.org/10.2147/TCRM.S257218

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Professor Deyun Wang


Rong-yun Mai,1– 3,* Jie Zeng,2,3,* Yi-shuai Mo,1,3,* Rong Liang,3,4 Yan Lin,3,4 Su-su Wu,2,3 Xue-min Piao,2– 4 Xing Gao,2– 4 Guo-bin Wu,1,3 Le-qun Li,1,3 Jia-zhou Ye1,3

1Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, Nanning 530021, People’s Republic of China; 2Department of Experimental Research, Guangxi Medical University Cancer Hospital, Nanning 530021, People’s Republic of China; 3Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning 530021, People’s Republic of China; 4Department of First Chemotherapy, Guangxi Medical University Cancer Hospital, Nanning 530021, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jia-zhou Ye; Le-qun Li
Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, 71 He Di Road, Nanning 530021, People’s Republic of China
Tel +86-0771-5310045
Fax +86-0771-5312000
. Email yejiazhou2019@163.com; lequn_li001@163.com

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.

Keywords: chronic hepatitis B, hepatocellular carcinoma, liver cirrhosis, serological indicators, non-invasive assessment, artificial neural network

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