■自发性破裂出血是一种致命的肝细胞癌(HCC)并发症,并且是生存结果的重要决定因素。本研究旨在开发和验证一种新的基于人工神经网络(ANN)的经导管动脉栓塞(TAE)后自发性HCC破裂患者的生存预测模型。
■在2010年1月至2018年12月期间在我们医院接受TAE的自发性HCC破裂出血患者被纳入我们的研究。采用最小绝对收缩和选择算子(LASSO)Cox回归模型筛选与预后相关的临床变量。我们将通过LASSOCox回归确定的上述临床变量纳入ANN模型。多层感知器ANN用于在训练集中开发自发性HCC破裂出血患者的1年总生存(OS)预测模型。使用接收器工作特征曲线和决策曲线分析下的面积来比较ANN模型与现有常规预测模型的预测能力。
■全组的中位生存时间为11.8个月,1年OS率为47.5%。LASSOCox回归显示性别,肝外转移,宏观血管侵犯,肿瘤数量,乙型肝炎表面抗原,乙型肝炎e抗原,肿瘤大小,甲胎蛋白,纤维蛋白原,直接胆红素,红细胞,γ-谷氨酰转移酶是OS的危险因素。具有12个输入节点的ANN模型,七个隐藏节点,并构建了两个相应的预后结果。在训练集和验证集中,ANN模型预测自发性HCC破裂出血患者1年OS的AUC为0.923(95%CI,0.890-0.956)和0.930(95%CI,0.875-0.985),分别,均高于现有常规模型(均P<0.0001)。
■我们建立的ANN模型具有更好的生存预测性能。
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: The
ANNs model that we established has better survival prediction performance.