Machine Learning-Based Development of Nomogram for Hepatocellular Carcinoma to Predict Acute Liver Function Deterioration After Drug-Eluting Beads Transarterial Chemoembolization

Acute liver function deterioration (ALFD) following drug-eluting beads transarterial chemotherapy embolism (DEB-TACE) was considered a risk factor for prognosis in patients with hepatocellular carcinoma (HCC). In this study, we aimed to develop and validate a nomogram for the prediction of ALFD after DEB-TACE.A total of 288 patients with HCC from a single center were randomly divided into a training dataset (n = 201) and a validation dataset (n = 87). The univariate and multivariate logistic regression analyses were performed to determine risk factors for ALFD. The least absolute shrinkage and selection operator (LASSO) was applied to identify the key risk factors and fit a model. The performance, calibration, and clinical utility of the predictive nomogram were assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).LASSO regression analysis determined six risk factors with fibrosis index based on four factors (FIB-4) as the independent factor for the occurrence of ALFD after DEB-TACE. Gamma-glutamyltransferase, FIB-4, tumor extent, and portal vein invasion were integrated into the nomogram. In both the training and validation cohorts, the nomogram demonstrated promising discrimination with AUC of 0.762 and 0.878, respectively. The calibration curves and DCA revealed good calibration and clinical utility of the predictive nomogram.The nomogram-based risk of ALFD stratification may improve clinical decision-making and surveillance protocols for patients with a high risk of ALFD after DEB-TACE.