Comparison of the Value of Multiple Preoperative Objective Nutritional Indices for the Evaluation of Prognosis after Hepatectomy for Hepatocellular Carcinoma

The role of cholesterol-modified prognostic nutritional index in nutritional status assessment and predicting survival after liver resection for hepatocellular carcinoma

Malnutrition, which is often underestimated in patients with hepatocellular carcinoma (HCC), has a proven adverse effect on survival rates. The purpose of this study was to verify the effectiveness of the cholesterol-modified prognostic nutritional index (CPNI) in determining the nutritional status and predicting overall survival (OS) and recurrence-free survival (RFS) in patients with HCC by comparing it with several other nutritional indicators. This retrospective single-center study enrolled 1450 consecutive HCC patients who underwent curative liver resection from January 2015 to November 2019. We evaluated the prognostic significance of several nutritional indicators, including CPNI, the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI), by applying time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier survival analysis, and Cox proportional hazards regression analysis. Among several objective nutrition evaluations (including CPNI, CONUT, NRI, and PNI), CPNI demonstrated the greatest prognostic predictive power for predicting OS. Meanwhile, CPNI demonstrated marginally higher accuracy in predicting RFS compared to PNI, and significantly outperformed CONUT and NRI. Univariate and multivariate analyses suggested that CPNI was an independent risk factor for the OS and RFS of patients with HCC undergoing curative liver resection. In most subgroups, malnutrition as identified by CPNI demonstrates strong stratification ability in predicting both OS and RFS. CPNI serves as an accurate and stable instrument for evaluating nutritional status and forecasting survival outcomes in HCC patients following liver resection, which has the potential to markedly influence clinical decision-making processes and the management of patient care.

The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in the past decades, the overall survival (OS) of liver cancer is still disappointing. Machine learning models have several advantages over traditional cox models in prognostic prediction. This study aimed at designing an optimal panel and constructing an optimal machine learning model in predicting prognosis for HCC. A total of 941 HCC patients with completed survival data and preoperative clinical chemistry and immunology indicators from two medical centers were included. The OCC panel was designed by univariate and multivariate cox regression analysis. Subsequently, cox model and machine-learning models were established and assessed for predicting OS and PFS in discovery cohort and internal validation cohort. The best OCC model was validated in the external validation cohort and analyzed in different subgroups. In discovery, internal and external validation cohort, C-indexes of our optimal OCC model were 0.871 (95% CI, 0.863-0.878), 0.692 (95% CI, 0.667-0.717) and 0.648 (95% CI, 0.630-0.667), respectively; the 2-year AUCs of OCC model were 0.939 (95% CI, 0.920-0.959), 0.738 (95% CI, 0.667-0.809) and 0.725 (95% CI, 0.643-0.808), respectively. For subgroup analysis of HCC patients with HBV, aged less than 65, cirrhosis or resection as first therapy, C-indexes of our optimal OCC model were 0.772 (95% CI, 0.752-0.792), 0.769 (95% CI, 0.750-0.789), 0.855 (95% CI, 0.846-0.864) and 0.760 (95% CI, 0.741-0.778), respectively. In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.

Controlling Nutritional Status (CONUT) Score is Associated with Overall Survival in Patients with Hepatocellular Carcinoma Treated with Conventional Transcatheter Arterial Chemoembolization: A Propensity Score Matched Analysis

PURPOSE

This study aims to evaluate the prognostic value of controlling nutritional status (CONUT) score in determining the prognosis of patients with hepatocellular carcinoma (HCC) treated with conventional transcatheter arterial chemoembolization (cTACE).

METHODS

This study retrospectively analyzed 936 patients who underwent cTACE for HCC between January 2012 and December 2018, and divided them into two groups based on their CONUT score. To balance the bias in baseline characteristics, propensity score matched (PSM) analysis was conducted. The Kaplan-Meier method was used to establish a cumulative survival curve, and the log-rank test was employed to determine differences in overall survival (OS) and progression-free survival (PFS) among the CONUT score groups. Furthermore, the Cox proportional hazard model was employed to assess the correlation between CONUT score and OS and PFS, whereby hazard ratios (HRs) and 95% confidence intervals (95% CIs) were computed.

RESULTS

Before PSM, the median OS for the low (≤ 3) and high (≥ 4) CONUT group (558 vs. 378 patients) was 21.7 and 15.6 months, respectively, and the median PFS was 5.7 and 5 months. Following PSM, both the low and high CONUT score groups comprised 142 patients. The low CONUT score group exhibited a significantly longer OS compared to the high CONUT score group, as determined by the log-rank test (median OS 22.2 vs. 17.0 months, P = 0.014). No significant association was observed between CONUT group and PFS (median PFS 6.4 vs. 4.7 months, log-rank test, P = 0.121). Cox proportional hazard regression analysis revealed that a CONUT score of ≥ 4 was an independent risk factor for OS in patients with HCC who underwent cTACE (HR = 1.361; 95% CI: 1.047-1.771; P = 0.022). These findings were consistent across most subgroup analyses.

CONCLUSION

A high CONUT score has been found to be a prognostic factor for poorer OS in patients with HCC who underwent cTACE.

LEVEL OF EVIDENCE

Level 3, Non-randomized controlled cohort.

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy

Introduction

This study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).

Methods

The research included 881 early-stage HCC patients treated at Beijing You'an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results

The study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram's predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.

Conclusions

The established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.

Prognostic Value of Inflammation-Immunity-Nutrition Score and Inflammatory Burden Index for Hepatocellular Carcinoma Patients After Hepatectomy

PURPOSE

The study aimed to investigate the ability of inflammation-immunity-nutrition score (IINS) and inflammatory burden index (IBI), individually or in combination, to predict prognosis of hepatocellular carcinoma (HCC) patients after hepatectomy.

METHODS

A total of 701 patients who underwent HCC resection at Guangxi Medical University Cancer Hospital were enrolled in the study. An IINS ranging from 0 to 3 was defined based on preoperative C-reactive protein (CRP), lymphocyte count, and serum albumin level, while an IBI was based on CRP and neutrophil-to-lymphocyte ratio. The prognostic value of IINS and IBI was assessed using univariate and multivariate Cox regression and Kaplan-Meier survival curves. The concordance index and calibration curve were used for internal validation of models. Decision curve analysis, net reclassification index and integrated discrimination improvement were used to compare the predictive performance of the models with traditional staging systems.

RESULTS

IINS and IBI were able to predict poor prognosis in HCC patients after hepatectomy, and a nomogram based on the IINS predicted survival at 1, 3, and 5 years better than other models or traditional staging systems.

CONCLUSION

IINS may be accurate predictors of survival in HCC patients after hepatectomy, with potentially greater prognostic value than conventional markers.