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

Abstract

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 < 0.001).

Conclusions: The RSSM can stratify accurately prognostic risk for HCC patients received IAT. On the basis, an online calculator permits easy implementation of this model.

Clinical relevance statement: The risk scoring scale model could be easily implemented for physicians to stratify risk and predict prognosis quickly and accurately, thereby serving as a more favorable tool to strengthen individualized intra-arterial therapies and management in patients with unresectable hepatocellular carcinoma.

Key points: • The Categorical Gradient Boosting (CatBoost) algorithm achieved the optimal and robust predictive ability (AUC, 0.851 (95%CI, 0.833-0.868) in training datasets, 0.817 (95%CI, 0.759-0.857) in internal validation datasets, and 0.791 (95%CI, 0.748-0.834) in external validation datasets) for prediction of 5-years death of hepatocellular carcinoma (HCC) after intra-arterial therapies (IATs) among five machine learning models. • We used the SHapley Additive exPlanations algorithms to explain the CatBoost model so as to resolve the black boxes of machine learning principles. • A simpler restricted variable, risk scoring scale model (RSSM), derived by stepwise Cox regression for risk stratification after intra-arterial therapies for hepatocellular carcinoma, provides the potential forewarning to adopt combination strategies for high-risk patients.

Keywords: Hepatocellular carcinoma; Intra-arterial therapies; Machine learning; Risk scoring scale model; Risk stratification.

Fig. 1

The enrolment pathway of the patients with unresectable hepatocellular carcinoma who underwent intra-arterial therapies

Fig. 2

The correlation coefficient matrix heat map of all 34 variables. A The correlation analysis of 17 variables including AFP; B the correlation analysis of the other 17 variables including BCLC stage

Fig. 3

The performance comparison of five different ML models: A the AUC comparison in training datasets; B the AUC comparison in internal validation datasets; C the AUC comparison in external validation datasets

Fig. 4

A RSSM with nomogram format was develop and validated: A the graph shows the nomogram for predicting 1-, 2-, 3-, and 5-year OS in HCC patients underwent IAT. B–D The bootstrapped calibration curves plotted with 1-, 3-, and 5-year OS were well matched with the idealized 45° line for the nomogram in the three datasets; E–G the decision curve analysis graphically indicated that the nomogram provided a larger benefit across the range of reasonable threshold probabilities in the three datasets

Fig. 5

Overall survival (OS) of unresectable hepatocellular carcinoma patients are stratified based on RSSM. A The OS comparison between low-risk group, middle-risk group, and high-risk group in training datasets; B OS comparison between low-risk group, middle-risk group, and high-risk group in the internal validation datasets; C OS comparison between low-risk group, middle-risk group, and high-risk group in the external validation datasets