Prognostic Nutritional Index and Neutrophil/Lymphocyte Ratio Can Serve as Independent Predictors of the Prognosis of Hepatocellular Carcinoma Patients Receiving Targeted Therapy

Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma

BACKGROUND/AIMS

The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups.

METHODS

The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort.

RESULTS

In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores.

CONCLUSION

Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Prognostic Nutritional Index as a Prognostic Factor for Very Early-Stage Hepatocellular Carcinoma

INTRODUCTION

Field factors play more important roles in predicting the outcomes of patients compared with tumor factors in early-stage hepatocellular carcinoma (HCC). However, the prognostic ability of noninvasive serum marker scores for hepatic fibrosis and liver functional reserve on very early-stage HCC is still not yet determined. We aimed to investigate the performance of these serum marker scores in predicting the prognoses of patients with very early-stage HCC.

METHODS

A total of 446 patients with very early-stage HCC from 2012 to 2022 were retrospectively enrolled. Serum biomarkers and prognostic scores determining overall survival (OS) were analyzed by Cox proportional hazards model. We compared the Akaike information criterion among the prognostic nutritional index (PNI), aspartate aminotransferase-to-platelet ratio index, albumin-bilirubin (ALBI) score, EZ (easy)-ALBI score, modified ALBI score, fibrosis-4 score, and lymphocyte-to-monocyte ratio to determine the predictability on the OS.

RESULTS

After a median follow-up of 41.0 months (interquartile range 36.9-45.1 months), 81 patients died, with a 5-year OS rate of 71.0%. Among the noninvasive serum marker scores, PNI had the best performance in predicting the OS with the lowest Akaike information criterion (846.407) compared with other scores. Moreover, we stratified the patients into high-risk (PNI <45) and low-risk (PNI ≥45) groups. It showed that the 5-year OS rates were 83.4% and 60.8% in the low-risk and high-risk PNI groups, respectively ( P < 0.001).

DISCUSSION

PNI had the best performance in predicting the OS for patients with very early-stage HCC.

Prognostic significance of psoas muscle index in male hepatocellular carcinoma patients treated with immune checkpoint inhibitors and tyrosine kinase inhibitors

Currently, the relationship between nutritional indices and the prognosis of hepatocellular carcinoma (HCC) patients treated with immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs) remains unclear. This study aims to investigate the prognostic value of psoas muscle index (PMI), prognostic nutritional index (PNI), body mass index (BMI), and geriatric nutritional risk index (GNRI) in HCC patients treated with ICIs combined with TKIs. A total of 124 male patients with HCC were included in this study. PNI, PMI, BMI, and GNRI were calculated at the beginning of treatment. The Cox proportional hazards model was used to analyze the effect of various variables. In the univariate analysis, PMI, PNI, GNRI, and ALB were found to impact the outcomes of the patients at different follow-up times. However, the predictive value of these nutritional indices was eliminated when established risk factors were considered. In the multivariate analysis that only included nutrition-related indicators, PMI emerged as an independent prognostic factor for 1-year treatment outcomes. The group with low PMI (≤5.5409 cm2/m2) was found to have a higher risk of death at one year and at the end of the follow-up period.

Blood-based biomarkers for immune-based therapy in advanced HCC: Promising but a long way to go

The introduction of immune checkpoint inhibitors (ICIs) represents a key shift in the management strategy for patients with hepatocellular carcinoma (HCC). However, there is a paucity of predictive biomarkers that facilitate the identification of patients that would respond to ICI therapy. Although several researchers have attempted to resolve the issue, the data is insufficient to alter daily clinical practice. The use of minimally invasive procedures to obtain patient-derived specimen, such as using blood-based samples, is increasingly preferred. Circulating tumor DNA (ctDNA) can be isolated from the blood of cancer patients, and liquid biopsies can provide sufficient material to enable ongoing monitoring of HCC. This is particularly significant for patients for whom surgery is not indicated, including those with advanced HCC. In this review, we summarize the current state of understanding of blood-based biomarkers for ICI-based therapy in advanced HCC, which is promising despite there is still a long way to go.