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

• Published in: Therapeutics and clinical risk management Vol. 16; pp. 639 - 649
• Main Authors: Mai, Rong-Yun, Zeng, Jie, Mo, Yi-Shuai, Liang, Rong, Lin, Yan, Wu, Su-Su, Piao, Xue-Min, Gao, Xing, Wu, Guo-Bin, Li, Le-Qun, Ye, Jia-Zhou
• Format: Journal Article
• Language: English
• Published: New Zealand Taylor & Francis Ltd 2020; Dove; Dove Medical Press
• Subjects: Accuracy; Algorithms; Antigens; artificial neural network; Ascites; Back propagation; Blood; Blood platelets; Deoxyribonucleic acid; Discriminant analysis; DNA; Hepatitis; Hepatitis B; hepatocellular carcinoma; Liver cancer; Liver cirrhosis; Liver diseases; Medical diagnosis; Medical research; Neural networks; Neurons; non-invasive assessment; Original Research; Propagation; serological indicators; Serology; Transplants & implants; chronic hepatitis B; artificial neural network; non-invasive assessment; serological indicators; hepatocellular carcinoma; liver cirrhosis
• ISSN: 1176-6336; 1178-203X; 1178-203X
• DOI: 10.2147/TCRM.S257218

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. 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. 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 ( < 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; < 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; < 0.05 for all) in diagnosing LC. Similar results were obtained in the validation set. 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.