Severity of liver fibrosis using shear wave elastography is influenced by hepatic necroinflammation in chronic hepatitis patients, but not in cirrhotic patients

Splenic Arterial Pulsatility Index to Predict Hepatic Fibrosis in Hemodialysis Patients with Chronic Hepatitis C Virus Infection

The clinical utility of the splenic arterial pulsatility index (SAPI), a duplex Doppler ultrasonographic index, to predict the stage of hepatic fibrosis in hemodialysis patients with chronic hepatitis C virus (HCV) infection remains elusive. We conducted a retrospective, cross-sectional study to include 296 hemodialysis patients with HCV who underwent SAPI assessment and liver stiffness measurements (LSMs). The levels of SAPI were significantly associated with LSMs (Pearson correlation coefficient: 0.413, p < 0.001) and different stages of hepatic fibrosis as determined using LSMs (Spearman's rank correlation coefficient: 0.529, p < 0.001). The areas under receiver operating characteristics (AUROCs) of SAPI to predict the severity of hepatic fibrosis were 0.730 (95% CI: 0.671-0.789) for ≥F1, 0.782 (95% CI: 0.730-0.834) for ≥F2, 0.838 (95% CI: 0.781-0.894) for ≥F3, and 0.851 (95% CI: 0.771-0.931) for F4. Furthermore, the AUROCs of SAPI were comparable to those of the fibrosis index based on four parameters (FIB-4) and superior to those of the aspartate transaminase (AST)-to-platelet ratio index (APRI). The positive predictive value (PPV) for ≥F1 was 79.5% when the Youden index was set at 1.04, and the negative predictive values (NPVs) for ≥F2, ≥F3, and F4 were 79.8%, 92,6%, and 96.9%, respectively, when the maximal Youden indices were set at 1.06, 1.19, and 1.30. The diagnostic accuracies of SAPI with the maximal Youden index for a fibrosis stage of ≥F1, ≥F2, ≥F3, and F4 were 69.6%, 67.2%, 75.0%, and 85.1%, respectively. In conclusion, SAPI can serve as a good noninvasive index in predicting the severity of hepatic fibrosis in hemodialysis patients with chronic HCV infection.

The synergized diagnostic value of VTQ with chemokine CXCL13 in lung tumors

Virtual Touch Tissue Quantification (VTQ) offers several advantages in the diagnosis of various lung diseases. Chemokine expression levels, such as CXCL13, play a vital role in the occurrence and development of tumors and aid in the diagnosis process. The purpose of this study was to evaluate the combined value of VTQ and changes in CXCL13 expression levels for the diagnosis of lung tumors. A total of 60 patients with thoracic nodules and pleural effusion were included, with 30 of them having malignant pleural effusion (based on pathology) and the remaining 30 having benign thoracic nodules and pleural effusion. The relative expression level of CXCL13 was measured in the collected pleural effusions using Enzyme-Linked Immunosorbent Assay (ELISA). The relationship between CXCL13 expression levels and various clinical features was analyzed. A Receiver Operating Characteristic (ROC) curve analysis was conducted on the VTQ results and relative expression levels of CXCL13, and the areas under the curve, critical values, sensitivity, and specificity were calculated. Multivariate analysis incorporating multiple indicators was performed to determine the accuracy of lung tumor diagnosis. The results showed that the expression levels of CXCL13 and VTQ were significantly higher in the lung cancer group compared to the control group (P < 0.05). In the Non-Small Cell Lung Cancer (NSCLC) group, CXCL13 expression levels increased with later TNM staging and poorer tumor differentiation. The expression level of CXCL13 in adenocarcinoma was higher than that in squamous cell carcinoma. The ROC curve analysis revealed that CXCL13 had an area under the curve (AUC) of 0.74 (0.61, 0.86) with an optimal cut-off value of 777.82 pg/ml for diagnosing lung tumors. The ROC curve analysis of VTQ showed an AUC of 0.67 (0.53, 0.82) with a sensitivity of 60.0% and a specificity of 83.3%, and an optimal diagnostic cut-off of 3.33 m/s. The combination of CXCL13 and VTQ for diagnosing thoracic tumors had an AUC of 0.842 (0.74, 0.94), which was significantly higher than either factor alone. The results of the study demonstrate the strong potential of combining VTQ results with chemokine CXCL13 expression levels for lung tumor diagnosis. Additionally, the findings suggest that elevated relative expression of CXCL13 in cases of malignant pleural effusion caused by non-small cell lung cancer may indicate a poor prognosis. This provides promising potential for using CXCL13 as a screening tool and prognostic indicator for patients with advanced lung cancer complicated by malignant pleural effusion.

Noninvasive Assessment of Liver Fibrosis and Inflammation in Chronic Hepatitis B: A Dual-task Convolutional Neural Network (DtCNN) Model Based on Ultrasound Shear Wave Elastography

BACKGROUND AND AIMS

Liver stiffness (LS) measured by shear wave elastography (SWE) is often influenced by hepatic inflammation. The aim was to develop a dual-task convolutional neural network (DtCNN) model for the simultaneous staging of liver fibrosis and inflammation activity using 2D-SWE.

METHODS

A total of 532 patients with chronic hepatitis B (CHB) were included to develop and validate the DtCNN model. An additional 180 consecutive patients between December 2019 and April 2021 were prospectively included for further validation. All patients underwent 2D-SWE examination and serum biomarker assessment. A DtCNN model containing two pathways for the staging of fibrosis and inflammation was used to improve the classification of significant fibrosis (≥F2), advanced fibrosis (≥F3) as well as cirrhosis (F4).

RESULTS

Both fibrosis and inflammation affected LS measurements by 2D-SWE. The proposed DtCNN performed the best among all the classification models for fibrosis stage [significant fibrosis AUC=0.89 (95% CI: 0.87-0.92), advanced fibrosis AUC=0.87 (95% CI: 0.84-0.90), liver cirrhosis AUC=0.85 (95% CI: 0.81-0.89)]. The DtCNN-based prediction of inflammation activity achieved AUCs of 0.82 (95% CI: 0.78-0.86) for grade ≥A1, 0.88 (95% CI: 0.85-0.90) grade ≥A2 and 0.78 (95% CI: 0.75-0.81) for grade ≥A3, which were significantly higher than the AUCs of the single-task groups. Similar findings were observed in the prospective study.

CONCLUSIONS

The proposed DtCNN improved diagnostic performance compared with existing fibrosis staging models by including inflammation in the model, which supports its potential clinical application.