Are inflammation-based markers useful in patients with hepatocellular carcinoma and clinically significant portal hypertension after liver resection?

Tumor burden score combined with AFP and PIVKA-II (TAP score) to predict the prognosis of hepatocellular carcinoma patients after radical liver resection

BACKGROUND

Our study aimed to combine the morphological behavior (tumor burden score, TBS) and the biological behavior (AFP and PIVKA-II) to predict the prognosis of HCC patients after radical liver resection.

METHODS

A total of 1766 HCC patients were divided into the training cohort (n = 1079) and the validation cohort (n = 687) with a ratio of 6:4. The Kaplan-Meier method was used to analyze the recurrence-free (RFS) and overall survival (OS). The multivariable Cox regression model was established based on the variables screened by the least absolute shrinkage and selection operator (LASSO) regression to identify variables independently associated with recurrence-free survival (RFS) and overall survival (OS). Constructing our prognostic score (TBS-LN(AFP + PIVKA-II) score, TAP score) based on regression coefficients and the predictive ability of the TAP score was compared with Barcelona Clinic Liver Cancer (BCLC) stage.

RESULTS

The TAP score had good performance in stratifying RFS (p < 0.001) and OS (p < 0.001) in the training cohort and the validation cohort. There still existed significant differences in the intergroup comparisons among three TAP score groups for RFS and OS in the training cohort and the validation cohort. In our LASSO-Cox regression model, the TAP score was independently associated with RFS and OS. The TAP score also outperformed the BCLC stage in predicting RFS (1, 2 and 3 years) and OS (1, 3 and 5 years).

CONCLUSIONS

The TAP score had good performance in predicting the prognosis of HCC patients after radical liver resection and was superior to the BCLC stage.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

BACKGROUND

Surgical resection is the primary treatment for hepatocellular carcinoma (HCC). However, studies indicate that nearly 70% of patients experience HCC recurrence within five years following hepatectomy. The earlier the recurrence, the worse the prognosis. Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data, which are lagging. Hence, developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.

AIM

To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.

METHODS

The demographic and clinical data of 371 HCC patients were collected for this retrospective study. These data were randomly divided into training and test sets at a ratio of 8:2. The training set was analyzed, and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice.

RESULTS

Following machine learning analysis, eight key feature variables (age, intratumoral arteries, alpha-fetoprotein, pre-operative blood glucose, number of tumors, glucose-to-lymphocyte ratio, liver cirrhosis, and pre-operative platelets) were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 (95% confidence interval: 0.982-1.000), 0.734 (0.601-0.867), and 0.706 (0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.

CONCLUSION

The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.

Combining local regional therapy and systemic therapy: Expected changes in the treatment landscape of recurrent hepatocellular carcinoma

Improvements in early screening, new diagnostic techniques, and surgical treatment have led to continuous downward trends in hepatocellular carcinoma (HCC) morbidity and mortality rates. However, high recurrence and refractory cancer after hepatectomy remain important factors affecting the long-term prognosis of HCC. The clinical characteristics and prognosis of recurrent HCC are heterogeneous, and guidelines on treatment strategies for recurrent HCC are lacking. Therapies such as surgical resection, radiofrequency ablation, and transhepatic arterial chemoembolization are effective for tumors confined to the liver, and targeted therapy is a very important treatment for unresectable recurrent HCC with systemic metastasis. With the deepening of the understanding of the immune microenvironment of HCC, blocking immune checkpoints to enhance the antitumor immune response has become a new direction for the treatment of HCC. In addition, improvements in the tumor immune microenvironment caused by local treatment may provide an opportunity to improve the therapeutic effect of HCC treatment. Ongoing and future clinical trial data of combined therapy may develop the new treatment scheme for recurrent HCC. This paper reviews the pattern of recurrent HCC and the characteristics of the immune microenvironment, demonstrates the basis for combining local treatment and systemic treatment, and reports current evidence to better understand current progress and future approaches in the treatment of recurrent HCC.