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

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.