A Machine Learning-Based Model to Predict Survival After Transarterial Chemoembolization for BCLC Stage B Hepatocellular Carcinoma

Objective: We sought to develop and validate a novel prognostic model for predicting survival of patients with BCLC stage B hepatocellular carcinoma (HCC) using a machine learning approach based on random survival forests (RSF). Methods: We retrospectively analyzed overall survival rates of patients with BCLC stage B HCC using a training (n=602), internal validation (n=301) and external validation (n=343) groups. We extracted twenty-one clinical and biochemical parameters with established strategies for preprocessing, then adopted the RSF classifier for variable selection and model development. We evaluated model performance using the concordance index (c-index) and area under the receiver operator characteristic curves (AUROC). Results: RSF revealed that five parameters, namely size of the tumor, BCLC-B sub-classification, AFP level, ALB level and number of lesions, were strong predictors of survival. These were thereafter used for model development. The established model had a c-index of 0.69, whereas AUROC for predicting survival outcomes of the first three years reached 0.72, 0.71 and 0.73 respectively. Additionally, the model had better performance relative to other eight Cox proportional-hazards models, and excellent performance in the subgroup of BCLC-B sub-classification B I and B II stage. Conclusion: The RSF-based model, established herein, can effectively predict survival of patients with BCLC stage B HCC, with better performance than previous Cox proportional hazards models.