Prognosis prediction and risk stratification of transarterial chemoembolization or intraarterial chemotherapy for unresectable hepatocellular carcinoma based on machine learning

Objective To develop and validate a risk scoring scale model (RSSM) for stratifying prognostic risk after intra-arterial therapies (IATs) for hepatocellular carcinoma (HCC). Methods Between February 2014 and October 2022, 2338 patients with HCC who underwent initial IATs were consecutively enrolled. These patients were divided into training datasets (TD, n = 1700), internal validation datasets (ITD, n = 428), and external validation datasets (ETD, n = 200). Five-years death was used to predict outcome. Thirty-four clinical information were input and five supervised machine learning (ML) algorithms, including eXtreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LGBT), and Random Forest (RF), were compared using the areas under the receiver operating characteristic (AUC) with DeLong test. The variables with top important ML scores were used to build the RSSM by stepwise Cox regression. Results The CatBoost model achieved the best discrimination when 12 top variables were input, with the AUC of 0.851 (95% confidence intervals (CI), 0.833–0.868) for TD, 0.817 (95%CI, 0.759–0.857) for ITD, and 0.791 (95%CI, 0.748–0.834) for ETD. The RSSM was developed based on the immune checkpoint inhibitors (ICI) (hazard ratios (HR), 0.678; 95%CI 0.549, 0.837), tyrosine kinase inhibitors (TKI) (HR, 0.702; 95%CI 0.605, 0.814), local therapy (HR, 0.104; 95%CI 0.014, 0.747), response to the first IAT (HR, 4.221; 95%CI 2.229, 7.994), tumor size (HR, 1.054; 95%CI 1.038, 1.070), and BCLC grade (HR, 2.375; 95%CI 1.950, 2.894). Kaplan–Meier analysis confirmed the role of RSSM in risk stratification ( p