Enhanced prediction of 5-year postoperative recurrence in hepatocellular carcinoma by incorporating LASSO regression and random forest models.

Abstract

Background: Tumor recurrence post-operation of hepatocellular carcinoma (HCC) impacts patient prognosis. Identifying and predicting 5-year HCC recurrence following surgery remains a substantial challenge.

Methods: We included 338 patients diagnosed with HCC who underwent surgery from January 2013 to December 2018. Traditional logistic regression, random forest (RF), and LASSO regression methods were used to develop a predictive model for 5-year recurrence. The findings were presented visually using nomogram. The accuracy and sensitivity of the predictive model were evaluated by receiver operating curves (ROC) and decision curve analysis (DCA).

Results: Of the 338 patients, 172 (50.9%) experienced 5 years recurrence, with a gender distribution of 79.7% males. Univariate and multivariate logistic regression analysis identified that three independent predictors of 5-year HCC recurrence (all P < 0.001). The area under the curve (AUC) value of the model (Model-1) constructed was 0.678. Then we combined LASSO regression and RF construct a predictive model including six factors: age, transarterial chemoembolization (TACE), microvascular invasion (MVI), alcohol, size, and number. The AUC of the model (Model-2) constructed was 0.733. DeLong's test results showed that Model-2 had significantly better prediction ability compared with Model-1 (P = 0.004). DCA also demonstrated that Model-2 had better predictive accuracy (P < 0.05). Then we constructed a nomogram, and Kaplan-Meier analysis showed that patients in the low-risk group had significantly better prognosis than the high (P < 0.001).

Conclusion: The predictive accuracy of our model, incorporating factors, such as age, alcohol, size, number, MVI, and TACE, significantly enhances clinical practice management by accurately forecasting 5 years HCC recurrence.