Pre-operative Detection of Liver Fibrosis in Hepatocellular Carcinoma Patients Using 2D Shear Wave Elastography: Where to Measure?

Construction of a nomogram for predicting compensated cirrhosis with Wilson's disease based on non-invasive indicators

BACKGROUND

Wilson's disease (WD) often leads to liver fibrosis and cirrhosis, and early diagnosis of WD cirrhosis is essential. Currently, there are few non-invasive prediction models for WD cirrhosis. The purpose of this study is to non-invasively predict the occurrence risk of compensated WD cirrhosis based on ultrasound imaging features and clinical characteristics.

METHODS

A retrospective analysis of the clinical characteristics and ultrasound examination data of 102 WD patients from November 2018 to November 2020 was conducted. According to the staging system for WD liver involvement, the patients were divided into a cirrhosis group (n = 43) and a non-cirrhosis group (n = 59). Multivariable logistic regression analysis was used to identify independent influencing factors for WD cirrhosis. A nomogram for predicting WD cirrhosis was constructed using R analysis software, and validation of the model's discrimination, calibration, and clinical applicability was completed. Due to the low incidence of WD and the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model.

RESULTS

Acoustic Radiation Force Impulse (ARFI), portal vein diameter (PVD), and serum albumin (ALB) are independent factors affecting WD cirrhosis. A nomogram for WD cirrhosis was constructed based on these factors. The area under the ROC curve (AUC) of the model's predictive ability is 0.927 (95% CI: 0.88-0.978). As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Clinical decision curve analysis shows that the model has high clinical practical value. ROC curve analysis of the model's rationality indicates that the model's AUC is greater than the AUC of using ALB, ARFI, and PVD alone.

CONCLUSION

The nomogram model constructed based on ARFI, PVD, and ALB can serve as a non-invasive tool to effectively predict the risk of developing WD cirrhosis.

Rethinking Liver Fibrosis Staging in Patients with Hepatocellular Carcinoma: New Insights from a Large Two-Center Cohort Study

Background

Hepatocellular carcinoma (HCC) is a prevalent and aggressive malignancy closely related to background chronic liver disease. This study aimed to explore predictive factors associated with background liver fibrosis burden in patients with HCC and sought to construct a practical predictive model for clinical use.

Methods

This large two-center retrospective cohort study evaluated data from Chinese medical centers. Uni- and multivariate ordinal logistic regression analyses were performed to identify variables associated with liver fibrosis stages. Predictive models based on variables identified by multivariate analysis were established in the Derivation Cohort and subjected to internal and external validation. Model performance was evaluated for discriminative and calibration abilities.

Results

Multivariate ordinal logistic regression analysis identified liver fibrosis severity score (LFSS), portal hypertension (PH) severity, plateletcrit (PCT) and model for end-stage liver disease-sodium (MELD-Na) as independent predictors of liver fibrosis stage in HCC patients. Nomograms that integrated these factors disclosed that the area under receiver operating characteristic curves (AUROCs) to predict S1 in the Derivation and External Validation cohorts were 0.850 and 0.919, respectively. Internal validation disclosed C-indexes of 0.823 and 0.833 in the Derivation and External Validation cohorts, respectively, indicating that the nomogram had good and excellent performance for distinguishing between S1 and non-S1 patients. Nomogram performance in the Derivation and External Validation cohorts, respectively, was fair and good to predict stage S2 (AUROCs 0.726, 0.806; C-indexes 0.713, 0.791); poor for S3 (AUROCs 0.648, 0.698; C-indexes 0.616, 0.666); good for S4 (AUROCs 0.812, 0.824; C-indexes 0.804, 0.792); and good for S3+S4 (AUROCs 0.806, 0.840; C-indexes 0.795, 0.811).

Conclusion

We propose new predictive models for the staging of background liver fibrosis in patients with HCC that can be implemented into clinical practice as important complements to hepatic imaging to inform HCC management strategy.

Cirrhosis of Wilson's disease: High and low cutoff using acoustic radiation force impulse (ARFI) -Comparison and combination with serum fibrosis index

BACKGROUND

Acoustic Radiation Force Impulse (ARFI), Fibrosis-4(FIB-4) and Aspartate transaminase to platelet ratio index (APRI) are valuable non-invasive methods to evaluate fibrosis in hepatitis virus. Yet, they are rarely used in Wilson's disease.

OBJECTIVE

Evaluate the diagnostic efficacy of ARFI, FIB-4, APRI, combined detection in cirrhosis with WD, and speculate the optimal high, low cutoff.

METHODS

This retrospective study was authorized by hospital ethics Committee (number:2021MCZQ02). 102 patients with WD completed ARFI and laboratory examination on the same day. The intraclass correlation coeffcient (ICC) of ARFI among three sonographers was 0.896 (95%CI:0.859-0.925, p = 0.000). The stage of liver involvement was classified into 5 categories according to clinical manifestations, laboratory examination, and liver morphologic characteristics: I, normal; II, biochemical abnormal only; III, abnormal liver morphologic features without sighs of cirrhosis; IV, clinical and imaging sighs of compensateded cirrhosis (Child-Pugh A); V, decompensated cirrhosis (Child-Pugh B and C). This stage system served as the reference standard. The diagnostic efficacy was analyzed by Logistic regression, ROC curve. The optimal low cut-off with high sensitivity (SE) and low negative likelihood ratio (NLR) and high cut-off with high specificity (SP) and positive likelihood ratio (PLR) were derived.

RESULTS

The diagnostic value of ARFI (0.85, 95%CI:0.77-0.92, p = 0.000) in distiguishing cirrhosis with WD was higher than FIB-4 (0.59, 95%CI: 0.47-0.70, p = 0.127), APRI (0.70, 95%CI: 0.59-0.81, p = 0.000). The low, high cut-off of ARFI for excluding, diagnosing cirrhosis with WD was 1.47 m/s(SE: 98%, NLR:0.09), 2.11 m/s(SP:98%, PLR:27.4), 37 (36%) patients could be spared a liver biopsy. When ARFI was 1.47∼2.11 m/s, liver biopsy was recommended. After combined with ARFI, the AUROC of FIB-4, APRI were increased respectively (p <  0.001), there were not different between ARFI and combined detection(p >  0.05).

CONCLUSION

ARFI could replace some unnecessary liver biopsy according to high diagnostic efficacy for identifying cirrhosis of WD. The combined detection can also be used as an important model to predict cirrhosis in WD.

Preoperative noninvasive assessment for liver fibrosis in hepatocellular carcinoma patients with chronic hepatitis B: Comparison of two-dimensional shear-wave elastography with serum liver fibrosis models

PURPOSE

To investigate the diagnostic performance of two-dimensional shear-wave elastography (2D-SWE) in hepatocellular carcinoma patients with chronic hepatitis B adapted to hepatectomy comparing to serum liver fibrosis models.

METHOD

100 patients with chronic hepatitis B who first diagnosed with hepatocellular carcinoma and had undergone 2D-SWE measurements before the hepatectomy were included. The performance of 2D-SWE and serum models in the diagnosis of liver fibrosis was assessed using receiver operating characteristic (ROC) analyses.

RESULTS

The areas under ROC (AUCs) for 2D-SWE, Forns score, aspartate transaminase-to-platelet ratio index (APRI) and Fibrosis 4 Score (FIB-4) were 0.983, 0.757, 0.745, 0.710 in the diagnosis of significant fibrosis (F ≥ 2) respectively, and 0.896, 0.718, 0.626, 0.575 in the diagnosis of cirrhosis (F = 4) respectively. The AUCs for 2D-SWE in the diagnosis of significant fibrosis and cirrhosis were significantly higher than those for the serum fibrosis models (p < 0.05). The AUCs of Forns in the diagnosis of significant fibrosis (F ≥ 2) showed no statistical differences (p > 0.05) with those of APRI and FIB-4 while in the diagnosis of cirrhosis (F = 4), they are significantly higher (p < 0.05).

CONCLUSIONS

2D-SWE is a reliable method for preoperative noninvasive assessment of liver fibrosis in HCC patients with CHB, with notably higher diagnostic accuracy than serum liver fibrosis models.