Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.

This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction model for spontaneous rupture bleeding via a machine learning algorithm, and validate and evaluate the predictive efficacy of the model. A retrospective analysis of 4209 patients with hepatocellular carcinoma (HCC) diagnosed at the Second Affiliated Hospital of Nanchang University from April 2019 to November 2023 was performed. Spontaneous rupture and bleeding occurred in 269 (6.4%) of these patients, and the clinical data of 146 patients (case group) were ultimately included, whereas the data of 144 patients without ruptured HCC (control group) were randomly chosen by matching for age, sex, and time of admission from the patients who visited our hospital during the same period. A randomly generated 70% (n = 203) was used as the training set, and the remaining 30% (n = 87) was used as the validation set. They constructed a predictive model for spontaneous rupture bleeding of hepatocellular carcinoma via 10 machine learning methods: Logistic, GBM, Neural Network, Random Forest, AdaBoost, LightGBM, CatBoost, XgBoost, KNN, and SVM models. The optimal model was screened on the basis of the area under the curve (AUC), calibration curves and confusion matrix to assess and compare the predictive performance of the models, the model was interpreted through SHAP plots, and a web-based version of the risk assessment tool for spontaneous rupture and bleeding in hepatocellular carcinoma patients was developed on the basis of the optimal machine learning predictive model. A total of 290 patients with HCC (254 males and 36 females) were included in this study. Analysis revealed that cirrhosis, neutrophil percentage, albumin levels, tumor diameter, and the presence of ascites were key predictors of spontaneous bleeding due to rupture in hepatocellular carcinoma patients. The 290 patients were randomized at a 7:3 ratio, and the training set of 203 patients and the validation set of 87 patients were simultaneously subjected to the construction of the risk prediction model. In the training set, the AUCs of the Logistic, GBM, Neural Network, Random Forest, AdaBoost, LightGBM, CatBoost, XgBoost, KNN, and SVM models are 0.911, 0.956, 0.929, 1.000, 0.919, 0.997, 0.948, 0.927, 0.984, and 0.903, respectively; in the validation set, the AUCs of the Logistic, GBM, Neural Network, Random Forest, AdaBoost, LightGBM, CatBoost, XgBoost, KNN, and SVM models are 0.940, 0.928, 0.939, 0.838, 0.897, 0.855, 0.925, 0.922, 0.888, and 0.946, respectively; among the 10 models, the SVM model has the best predictive performance. On the basis of the results of this study, a predictive model for spontaneous bleeding in hepatocellular carcinoma was presented, and a web-based version of a risk prediction assessment tool was created via SVM modeling to improve its clinical translational value.