Comparison of preoperative two-dimensional shear wave elastography, indocyanine green clearance test and biomarkers for post hepatectomy liver failure prediction in patients with hepatocellular carcinoma

Application of ultrasound elastography and splenic size in predicting post-hepatectomy liver failure: Unveiling new clinical perspectives

In this article, we discuss the study by Cheng et al, published in the World Journal of Gastroenterology, focusing on predictive methods for post-hepatectomy liver failure (PHLF). PHLF is a common and serious complication, and accurate prediction is critical for clinical management. The study examines the potential of ultrasound elastography and splenic size in predicting PHLF. Ultrasound elastography reflects liver functional reserve, while splenic size provides additional predictive value. By integrating these factors with serological markers, we developed a comprehensive prediction model that effectively stratifies patient risk and supports personalized clinical decisions. This approach offers new insights into predicting PHLF. These methods not only assist clinicians in identifying high-risk patients earlier but also provide scientific support for personalized treatment strategies. Future research will aim to validate the model's accuracy with larger sample sizes, further enhancing the clinical application of these non-invasive indicators.

Nomogram based on liver stiffness and spleen area with ultrasound for posthepatectomy liver failure: A multicenter study

BACKGROUND

Liver stiffness (LS) measurement with two-dimensional shear wave elastography (2D-SWE) correlates with the degree of liver fibrosis and thus indirectly reflects liver function reserve. The size of the spleen increases due to tissue proliferation, fibrosis, and portal vein congestion, which can indirectly reflect the situation of liver fibrosis/cirrhosis. It was reported that the size of the spleen was related to posthepatectomy liver failure (PHLF). So far, there has been no study combining 2D-SWE measurements of LS with spleen size to predict PHLF. This prospective study aimed to investigate the utility of 2D-SWE assessing LS and spleen area (SPA) for the prediction of PHLF in hepatocellular carcinoma (HCC) patients and to develop a risk prediction model.

AIM

To investigate the utility of 2D-SWE assessing LS and SPA for the prediction of PHLF in HCC patients and to develop a risk prediction model.

METHODS

This was a multicenter observational study prospectively analyzing patients who underwent hepatectomy from October 2020 to March 2022. Within 1 wk before partial hepatectomy, ultrasound examination was performed to measure LS and SPA, and blood was drawn to evaluate the patient's liver function and other conditions. Least absolute shrinkage and selection operator logistic regression and multivariate logistic regression analysis was applied to identify independent predictors of PHLF and develop a nomogram. Nomogram performance was validated further. The diagnostic performance of the nomogram was evaluated with receiver operating characteristic curve compared with the conventional models, including the model for end-stage liver disease (MELD) score and the albumin-bilirubin (ALBI) score.

RESULTS

A total of 562 HCC patients undergoing hepatectomy (500 in the training cohort and 62 in the validation cohort) were enrolled in this study. The independent predictors of PHLF were LS, SPA, range of resection, blood loss, international normalized ratio, and total bilirubin. Better diagnostic performance of the nomogram was obtained in the training [area under receiver operating characteristic curve (AUC): 0.833; 95% confidence interval (95%CI): 0.792-0.873; sensitivity: 83.1%; specificity: 73.5%] and validation (AUC: 0.802; 95%CI: 0.684-0.920; sensitivity: 95.5%; specificity: 52.5%) cohorts compared with the MELD score and the ALBI score.

CONCLUSION

This PHLF nomogram, mainly based on LS by 2D-SWE and SPA, was useful in predicting PHLF in HCC patients and presented better than MELD score and ALBI score.

Current status of preoperative risk assessment for posthepatectomy liver failure in patients with hepatocellular carcinoma

Liver resection is an effective therapeutic option for patients with hepatocellular carcinoma. However, posthepatectomy liver failure (PHLF) remains a major cause of hepatectomy-related mortality, and the accurate prediction of PHLF based on preoperative assessment of liver functional reserve is a critical issue. The definition of PHLF proposed by the International Study Group for Liver Surgery has gained acceptance as a standard grading criterion. Liver function can be estimated using a variety of parameters, including routine blood biochemical examinations, clinical scoring systems, dynamic liver function tests, liver stiffness and fibrosis markers, and imaging studies. The Child-Pugh score and model for end-stage liver disease scores are conventionally used for estimating liver decompensation, although the alternatively developed albumin-bilirubin score shows superior performance for predicting hepatic dysfunction. Indocyanine green clearance, a dynamic liver function test mostly used in Japan and other Asian countries, serves as a quantitative estimation of liver function reserve and helps determine indications for surgical procedures according to the estimated risk of PHLF. In an attempt to improve predictive accuracy, specific evaluation of liver fibrosis and portal hypertension has gained popularity, including liver stiffness measurements using ultrasonography or magnetic resonance elastography, as well as noninvasive fibrosis markers. Imaging modalities, including Tc-99m-labeled galactosyl serum albumin scintigraphy and gadolinium-enhanced magnetic resonance imaging, are used for preoperative evaluation in combination with liver volume. This review aims to provide an overview of the usefulness of current options for the preoperative assessment of liver function in predicting PHLF.

A novel nomogram based on preoperative parameters to predict posthepatectomy liver failure in patients with hepatocellular carcinoma

BACKGROUND

Posthepatectomy liver failure is one of the main causes of death in patients after hepatectomy. This study intends to establish a prediction model to predict the risk of posthepatectomy liver failure and provide a scientific basis for further reducing the incidence of posthepatectomy liver failure.

METHODS

This was a retrospective analysis of 1,172 patients with hepatocellular carcinoma undergoing partial hepatectomy. Using univariate and multivariate logistic regression analyses and stepwise regression, a prediction model for posthepatectomy liver failure was established based on the independent risk factors for posthepatectomy liver failure and validated by bootstrapping with 100 resamples, and the receiver operating characteristic curve was used to evaluate the predictive value of the prediction model.

RESULTS

The incidence rate of posthepatectomy liver failure was 22.7% (266/1172). The results showed that the indocyanine green retention rate at 15 minutes (odds ratio = 1.05, P = .002), alanine transaminase (odds ratio = 1.02, P < .001), albumin rate (odds ratio = 0.92, P < .001), total bilirubin (odds ratio = 1.04, P < .001), prothrombin time (odds ratio = 2.44, P < .001), aspartate aminotransferase-neutrophil ratio (odds ratio = 0.95, P < .001), and liver fibrosis index (odds ratio = 1.35, P < .001) were associated with posthepatectomy liver failure. These 7 independent risk factors for posthepatectomy liver failure were integrated into a nomogram prediction model, the predictive efficiency for posthepatectomy liver failure (area under the curve = 0.818, 95% confidence interval 0.789-0.848) was significantly higher than in other predictive models with a liver fibrosis index (area under the curve = 0.651), indocyanine green R15 (area under the curve = 0.669), albumin-bilirubin score (area under the curve = 0.709), albumin-indocyanine green evaluation score (area under the curve = 0.706), model for end-stage liver disease score (area under the curve = 0.636), and Child‒Pugh (area under the curve = 0.551) (all P < .001). The risk of posthepatectomy liver failure in the high-risk posthepatectomy liver failure group (score ≥152) was higher than that in the posthepatectomy liver failure low-risk group (score <152).

CONCLUSION

This study developed and validated a nomogram model to predict the risk of posthepatectomy liver failure before surgery that can effectively predict the risk of posthepatectomy liver failure in patients with hepatocellular carcinoma.

Clinical application of preoperative shear wave dispersion for prediction of post liver failure in patients with hepatocellular carcinoma after hepatectomy

OBJECTIVE

The aim in this study was to determine the efficacy of shear wave dispersion (SWD) technique for the prediction of post hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma after hepatectomy and develop an SWD based risk prediction model.

METHODS & MATERIALS

We prospectively enrolled 205 consecutive patients who were scheduled to undergo hepatectomy for hepatocellular carcinoma (HCC), pre-operative SWD examination, laboratory data and some other clinicopathological tests were collected. The risk factors of PHLF were identified according to univariate and multivariate analysis, a predictive model was established based on logistic regression analyses.

RESULTS

SWD examination was successfully performed in 205 patients. PHLF occurred in 51 patients (24.9%), including 37/11/3 patients with Grade A/B/C, respectively. There was a high correlation between SWD value of liver and liver fibrosis stage (r = 0.873, p < 0.05). Patients with PHLF has a higher median SWD value of liver than patients without PHLF [17.4 vs 14.7 (m/s)/kHz, p < 0.05]. The SWD value of liver, total bilirubin (TB), international normalized ratio of prothrombin time (INR) and splenomegaly were significantly related to PHLF based on the multivariate analysis. A new prediction model (PM) for PHLF was established (PM = -12.918 + 0.183× SWD + 6.668× INR +0.100×TB+1.240×splenomegaly). The optimal cutoff value of SWD for predicting PHLF was 16.7 (m/s)/kHz. The area under the curve (AUC) of the PM for PHLF was 0.833, which was higher than that of SWD, INR, Forns, FIB4, APRI (p < 0.005, respectively).

CONCLUSION

SWD is a promising and reliable method for PHLF prediction in patients with HCC who were undergoing hepatectomy. Compared with SWD, Forns, APRI and FIB-4, PM demonstrate better efficacy for preoperative PHLF prediction.

Preoperative indocyanine green (ICG) clearance test: Can we really trust it to predict post hepatectomy liver failure? A systematic review of the literature and meta-analysis of diagnostic test accuracy

INTRODUCTION

Post hepatectomy liver failure (PHFL) still represents a potentially fatal complication after major liver resection. Indocyanine green (ICG) clearance test represents one of the most widely adopted examinations in the preoperative workup. Despite a copious body of evidence which has been published on this topic, the role of ICG in predicting PHLF is still a matter of debate.

METHODS

A systematic review of the literature was conducted according to PRISMA-DTA guidelines. The primary outcome was the assessment of diagnostic performance of ICG in predicting PHLF. The secondary outcome was the mean ICG_R15 and ICG_PDR in patients experiencing PHLF.

RESULTS

Seventeen studies, for a total of 4852 patients, were deemed eligible. Sensitivity ranged from 25% to 83%; Specificity ranged from 66.1% to 93.8%. ICG clearance test pooled AUC was 0.673 (95% CI: 0.632-0.713). The weighted mean ICG_R15 was 11 (95%CI: 8.3-13.7). The weighted mean ICG_PDR was 16.5 (95%CI: 13.3-19.8). High risk of bias was detected in all examined domains.

CONCLUSIONS

Preoperative ICG clearance test alone may not represent a reliable method to predict post hepatectomy liver failure. Its diagnostic significance should be framed within multiparametric models involving clinical and imaging features.

A Novel Nomogram for Predicting Postoperative Liver Failure After Major Hepatectomy for Hepatocellular Carcinoma

Background

Post-hepatectomy liver failure (PHLF) is the most common cause of mortality after major hepatectomy in hepatocellular carcinoma (HCC) patients. We aim to develop a nomogram to preoperatively predict grade B/C PHLF defined by the International Study Group on Liver Surgery Grading (ISGLS) in HCC patients undergoing major hepatectomy.

Study Design

The consecutive HCC patients who underwent major hepatectomy at the Eastern Hepatobiliary Surgery Hospital between 2008 and 2013 served as a training cohort to develop a preoperative nomogram, and patients from 2 other hospitals comprised an external validation cohort. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify preoperative predictors of grade B/C PHLF. Multivariable logistic regression was utilized to establish a nomogram model. Internal and external validations were used to verify the performance of the nomogram. The accuracy of the nomogram was also compared with the conventional scoring models, including MELD and ALBI score.

Results

A total of 880 patients who underwent major hepatectomy (668 in the training cohort and 192 in the validation cohort) were enrolled in this study. The independent risk factors of grade B/C PHLF were age, gender, prothrombin time, total bilirubin, and CSPH, which were incorporated into the nomogram. Good prediction discrimination was achieved in the training (AUROC: 0.73) and validation (AUROC: 0.72) cohorts. The calibration curve also showed good agreement in both training and validation cohorts. The nomogram has a better performance than MELD and ALBI score models.

Conclusion

The proposed nomogram showed more accurate ability to individually predict grade B/C PHLF after major hepatectomy in HCC patients than MELD and ALBI scores.

External Validation of Six Liver Functional Reserve Models to predict Posthepatectomy Liver Failure after Major Resection for Hepatocellular Carcinoma

Objective: To validate and compare the predictive ability of albumin-bilirubin model (ALBI) with other 5 liver functional reserve models (APRI, FIB4, MELD, PALBI, King's score) for posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) who underwent major hepatectomy. Methods: Data of patients undergoing major hepatectomy for HCC from 4 hospitals between January 01, 2008 and December 31, 2019 were retrospectively analyzed. PHLF was evaluated according to the definition of the 50-50 criteria. Performances of six liver functional reserve models were determined by the area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analysis. Results: A total of 745 patients with 103 (13.8%) experienced PHLF were finally included in this study. Among six liver functional reserve models, ALBI showed the highest AUC (0.64, 95% CI: 0.58-0.69) for PHLF. All models showed good calibration and greater net benefit than treating all patients at a limit range of threshold probabilities, but the ALBI demonstrated net benefit across the largest range of threshold probabilities. Subgroup analysis also showed ALBI had good predictive performance in cirrhotic (AUC=0.63) or non-cirrhotic (AUC=0.62) patients. Conclusion: Among the six models, the ALBI model shows more accurate predictive ability for PHLF in HCC patients undergoing major hepatectomy, regardless of having cirrhosis or not.