Random Survival Forests to Predict Disease Control for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib - Zhong, Bin-Yan; Yan, Zhi-Ping; Sun, Jun-Hui; Zhang, Lei; Hou, Zhong-Heng; Zhu, Xiao-Li; Wen, Ling; Ni, Cai-Fang (Frontiers in Molecular Biosciences, 2021)
Objectives To use baseline variables to predict 1-year disease control for patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as initial treatment by applying a machine learning approach based on random survival forest (RF) model. Materials and Methods The multicenter retrospective study included 496 patients with HCC treated with TACE combined with sorafenib between January 2014 and December 2018. The independent risk factors associated with 1-year disease control (complete response, partial response, stable disease) were identified using RF model and their predictive importance was determined using the Gini index. Tumor response was assessed according to modified Response Evaluation Criteria in Solid Tumors. Results The median overall survival was 15.5 months. A total of 186 (37.5%) patients achieved positive 1-year disease control. The Barcelona Clinic Liver Cancer (BCLC) stage (Gini index: 20.0), tumor size (≤7 cm, >7 cm; Gini index: 9.0), number of lobes involved (unilobar, bilobar; Gini index: 6.4), alpha-fetoprotein level (≤200 ng/dl, >200 ng/dl; Gini index: 6.1), the albumin-bilirubin grade (Gini index: 5.7), and number of lesions (1, >1; Gini index: 5.3) were identified as independent risk factors, with the BCLC stage as the most important variable. The RF model achieved a higher concordance index of 0.724 compared to that for the logistic regression model (0.709). Conclusions RF model is a simple and accurate approach for prediction of 1-year disease control for patients with HCC treated with TACE combined with sorafenib.