Development and Validation of Artificial Neural Networks for Survival Prediction Model for Patients with Spontaneous Hepatocellular Carcinoma Rupture After Transcatheter Arterial Embolization

Abstract

Background Spontaneous rupture bleeding is a fatal hepatocellular carcinoma (HCC) complication and a significant determinant of survival outcomes. This study aimed to develop and validate a novel artificial neural network (ANN)-based survival prediction model for patients with spontaneous HCC rupture after transcatheter arterial embolization (TAE). Methods Patients with spontaneous HCC rupture bleeding who underwent TAE at our hospital between January 2010 and December 2018 were included in our study. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to screen clinical variables related to prognosis. We incorporated the above clinical variables identified by LASSO Cox regression into the ANNs model. Multilayer perceptron ANNs were used to develop the 1-year overall survival (OS) prediction model for patients with spontaneous HCC ruptured bleeding in the training set. The area under the receiver operating characteristic curve and decision curve analysis were used to compare the predictive capability of the ANNs model with that of existing conventional prediction models. Results The median survival time for the whole set was 11.8 months, and the 1-year OS rate was 47.5%. LASSO Cox regression revealed that sex, extrahepatic metastasis, macroscopic vascular invasion, tumor number, hepatitis B surface antigen, hepatitis B e antigen, tumor size, alpha-fetoprotein, fibrinogen, direct bilirubin, red blood cell, and γ-glutamyltransferase were risk factors for OS. An ANNs model with 12 input nodes, seven hidden nodes, and two corresponding prognostic outcomes was constructed. In the training set and the validation set, AUCs for the ability of the ANNs model to predict the 1-year OS of patients with spontaneous HCC rupture bleeding were 0.923 (95% CI, 0.890–0.956) and 0.930 (95% CI, 0.875–0.985), respectively, which were higher than that of the existing conventional models (all P < 0.0001). Conclusion The ANNs model that we established has better survival prediction performance.

Selective Arterial Embolization of Ruptured Hepatocellular Carcinoma with N-Butyl Cyanoacrylate and Lipiodol: Safety, Efficacy, and Short-Term Outcomes

“…The very high technical success rate of 94% is in line with previous studies that used other embolic agents [14,15,[38][39][40]. Rebleeding was rare, also in keeping with earlier data [16].…”

Nomogram for Estimation of Acute Liver Failure Risk in Spontaneous Ruptured Hepatocellular Carcinoma

“…Qiu et al used machine learning algorithms to find that AFP value is the most important prognostic factor for predicting the short-term survival of SRHCC patients. 33 Our study also found that high serum AFP levels are associated with the occurrence of ALF. Bilirubin and albumin, which still have high evaluation functions for liver function, have been confirmed by multiple studies, 4 , 34 , 35 but we excluded them after multivariate regression analysis, suggesting that their role in predicting the occurrence of ALF in SRHCC is limited.…”

Myosteatosis can Predict Unfavorable Outcomes in Advanced Hepatocellular Carcinoma Patients Treated With Hepatic Artery Infusion Chemotherapy and Anti-PD-1 Immunotherapy

“…In addition, the modest differentiation efficiency of our model was also attributed to the introduction of several clinical and radiological parameters, including RBC, HGB, Scr, and VFmean, whose potential prognostic roles were investigated in previous studies. For example, RBC ( 26 ) and HGB ( 27 ) have been shown to be critical risk factors for poor prognosis in HCC patients. Relatively reduced Scr level in the Non-OR group when compared to that in the OR group suggested a significant loss of skeleton muscle mass (sarcopenia), which was clearly visible in our cohort and was reported to be associated with the poor prognosis in HCC.…”