Evaluating the clinical value of MRI multi-model diffusion-weighted imaging on liver fibrosis in chronic hepatitis B patients

Author(s):  
Hongwei Ren ◽  
Yuan Liu ◽  
Jing Lu ◽  
Weimin An ◽  
Weidi Wang ◽  
...  
2005 ◽  
Vol 51 (2) ◽  
pp. 328-335 ◽  
Author(s):  
Terence CW Poon ◽  
Alex Y Hui ◽  
Henry LY Chan ◽  
Irene Ling Ang ◽  
Shuk Man Chow ◽  
...  

Abstract Background: Most noninvasive predictive models of liver fibrosis are complicated and have suboptimal sensitivity. This study was designed to identify serum proteomic signatures associated with liver fibrosis and to develop a proteome-based fingerprinting model for prediction of liver fibrosis. Methods: Serum proteins from 46 patients with chronic hepatitis B (CHB) were profiled quantitatively on surface-enhanced laser desorption/ionization (SELDI) ProteinChip arrays. The identified liver fibrosis-associated proteomic fingerprint was used to construct an artificial neural network (ANN) model that produced a fibrosis index with a range of 0–6. The clinical value of this index was evaluated by leave-one-out cross-validation. Results: Thirty SELDI proteomic features were significantly associated with the degree of fibrosis. Cross-validation showed that the ANN fibrosis indices derived from the proteomic fingerprint strongly correlated with Ishak scores (r = 0.831) and were significantly different among stages of fibrosis. ROC curve areas in predicting significant fibrosis (Ishak score ≥3) and cirrhosis (Ishak score ≥5) were 0.906 and 0.921, respectively. At 89% specificity, the sensitivity of the ANN fibrosis index in predicting fibrosis was 89%. The sensitivity for prediction increased with degree of fibrosis, achieving 100% for patients with Ishak scores >4. The accuracy for prediction of cirrhosis was also 89%. Inclusion of International Normalized Ratio, total protein, bilirubin, alanine transaminase, and hemoglobin in the ANN model improved the predictive power, giving accuracies >90% for the prediction of fibrosis and cirrhosis. Conclusions: A unique serum proteomic fingerprint is present in the sera of patients with fibrosis. An ANN fibrosis index derived from this fingerprint could differentiate between different stages of fibrosis and predict fibrosis and cirrhosis in CHB infection.


Gut and Liver ◽  
2017 ◽  
Vol 11 (3) ◽  
pp. 401-408 ◽  
Author(s):  
Tiffany P. Hennedige ◽  
Gang Wang ◽  
Fiona P. Leung ◽  
Hind S. Alsaif ◽  
Lynette LS Teo ◽  
...  

Antioxidants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 77
Author(s):  
Jing-Hua Wang ◽  
Sung-Bae Lee ◽  
Dong-Soo Lee ◽  
Chang-Gue Son

Oxidative stress plays a pivotal role in the progression of chronic hepatitis B; however, it is unclear whether the status of blood oxidative stress and antioxidant components differs depending on the degree of hepatic fibrosis. To explore the relationship between oxidative stress/antioxidant capacity and the extent of hepatic fibrosis, fifty-four subjects with liver fibrosis (5.5 ≤ liver stiffness measurement (LSM) score ≤ 16.0 kPa) by chronic hepatitis B virus (HBV) were analyzed. From the analysis of eight kinds of serum oxidative stress/antioxidant profiles and liver fibrosis degrees, the level of total antioxidant capacity (TAC) reflected a negative correlation with the severity of hepatic fibrosis (Pearson correlation, r = −0.35, p = 0.01). Moreover, TAC showed higher sensitivity (73.91%) than the aspartate transaminase (AST) to platelet ratio index (APRI, 56.52%) in the receiver operating characteristic (ROC) curves. Interestingly, the TAC level finely reflected the fibrosis degree in inactive carriers (HBV DNA < 2000 IU/mL), while the APRI did in active carriers (HBV DNA > 2000 IU/mL). In conclusion, TAC is a promising biomarker for evaluating the progression of liver fibrosis in patients with HBV, and this finding may indicate the involvement of TAC-composing factors in the pathogenesis of hepatic fibrosis in chronic HBV carriers.


Hepatology ◽  
2015 ◽  
Vol 61 (4) ◽  
pp. 1261-1268 ◽  
Author(s):  
Beomseok Suh ◽  
Sehhoon Park ◽  
Dong Wook Shin ◽  
Jae Moon Yun ◽  
Hyung-Kook Yang ◽  
...  

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