Non-invasive predictive score of fibrosis stages in chronic hepatitis C patients based on epithelial membrane antigen in the blood in combination with routine laboratory markers

Comparison of different predicting models to assist the diagnosis of spinal lesions

In neurosurgical or orthopedic clinics, the differential diagnosis of lower back pain is often time-consuming and costly. This is especially true when there are several candidate diagnoses with similar symptoms that might confuse clinic physicians. Therefore, methods for the efficient differential diagnosis can help physicians to implement the most appropriate treatment and achieve the goal of pain reduction for their patients.In this study, we applied data-mining techniques from artificial intelligence technologies, in order to implement a computer-aided auxiliary differential diagnosis for a herniated intervertebral disc, spondylolithesis, and spinal stenosis. We collected questionnaires from 361 patients and analyzed the resulting data by using a linear discriminant analysis, clustering, and artificial neural network techniques to construct a related classification model and to compare the accuracy and implementation efficiency of the different methods.Our results indicate that a linear discriminant analysis has obvious advantages for classification and diagnosis, in terms of accuracy.We concluded that the judgment results from artificial intelligence can be used as a reference for medical personnel in their clinical diagnoses. Our method is expected to facilitate the early detection of symptoms and early treatment, so as to reduce the social resource costs and the huge burden of medical expenses, and to increase the quality of medical care.

Methylation of tumour suppressor genes RUNX3, RASSF1A and E-Cadherin in HCV-related liver cirrhosis and hepatocellular carcinoma

Background: HCV infection is related to aberrant methylation of several genes. RASSF1A, E-Cadherin and RUNX3 are tumour suppressor genes that may be inactivated by hypermethylation in many tumours including hepatocellular carcinoma (HCC). We hypothesized that methylation is a diagnostic biomarker for HCC in patients with HCV-related liver cirrhosis.Methods: We recruited 207 cases of HCV-related liver cirrhosis, 193 HCC patients and 53 healthy controls. Methylation-specific polymerase chain reaction for detection of circulating hypermethylated RASSF1A, E-Cadherinand RUNX3. Alpha fetoprotein (AFP) was measured by commercial immunoassay.Results: Significant hypermethylation of the three genes was found in the HCC group compared to both cirrhosis and healthy groups (P < 0.001), whereas no significant difference in hypermethylation was found between cirrhosis and healthy groups (P = 0.17, 0.50 and 0.14, respectively). No significant links were found between hypermethylated RASSF1A, E-Cadherin and RUNX3 and stages of Barcelona Clinic of Liver Cancer score (P =0.21, 0.63 and 0.98, respectively). No significant associations were found between AFP value and hypermethylated genes in cirrhosis and HCC groups (P = 0.82) except with E-Cadherin in HCC (P = 0.02). In multiple regression analysis, RASSF1A and E-Cadherin were predictors of HCC within cirrhosis cases, but only E-Cadherin was an independent risk factor for prediction of HCC in cases with low AFP (P = 0.01).Conclusions: The presence of hypermethylated serum RASSF1A, E-Cadherin and RUNX3 is linked to HCC in patients with HCV-related cirrhosis. Only E-Cadherin is an independent risk factor for prediction of HCC with low AFP. These findings may be of diagnostic value.

Diffusion-weighted magnetic resonance imaging and micro-RNA in the diagnosis of hepatic fibrosis in chronic hepatitis C virus

BACKGROUND

Diffusion-weighted magnetic resonance imaging has shown promise in the detection and quantification of hepatic fibrosis. In addition, the liver has numerous endogenous micro-RNAs (miRs) that play important roles in the regulation of biological processes such as cell proliferation and hepatic fibrosis.

AIM

To assess diffusion-weighted magnetic resonance imaging and miRs in diagnosing and staging hepatic fibrosis in patients with chronic hepatitis C.

METHODS

This prospective study included 208 patients and 82 age- and sex-matched controls who underwent diffusion-weighted magnetic resonance imaging of the abdomen, miR profiling, and liver biopsy. Pathological scoring was classified according to the METAVIR scoring system. The apparent diffusion coefficient (ADC) and miR were calculated and correlated with pathological scoring.

RESULTS

The ADC value decreased significantly with the progression of fibrosis, from controls (F0) to patients with early fibrosis (F1 and F2) to those with late fibrosis (F3 and F4) (median 1.92, 1.53, and 1.25 × 10^-3 mm²/s, respectively) (P = 0.001). The cut-off ADC value used to differentiate patients from controls was 1.83 × 10^-3 mm²/s with an area under the curve (AUC) of 0.992. Combining ADC and miR-200b revealed the highest AUC (0.995) for differentiating patients from controls with an accuracy of 96.9%. The cut-off ADC used to differentiate early fibrosis from late fibrosis was 1.54 × 10^-3 mm²/s with an AUC of 0.866. The combination of ADC and miR-200b revealed the best AUC (0.925) for differentiating early fibrosis from late fibrosis with an accuracy of 80.2%. The ADC correlated with miR-200b (r = - 0.61, P = 0.001), miR-21 (r = - 0.62, P = 0.001), and miR-29 (r = 0.52, P = 0.001).

CONCLUSION

Combining ADC and miRs offers an alternative surrogate non-invasive diagnostic tool for diagnosing and staging hepatic fibrosis in patients with chronic hepatitis C.

APRI test and hyaluronic acid as non-invasive diagnostic tools for post HCV liver fibrosis: Systematic review and meta-analysis

BACKGROUND AND STUDY AIMS

Hepatitis C virus (HCV) accounts for a sizable proportion of chronic liver disease cases and represents the most common indication for liver transplantation. Precise diagnosis of hepatic fibrosis stage is considered a funnel-neck in proper management and follow-up of HCV-infected patients. Given the possible complications of liver biopsy, a non-invasive method for assessing hepatic fibrosis is needed. This study aimed to evaluate the diagnostic accuracy of APRI and hyaluronic acid as non-invasive diagnostic assessment tools for post HCV liver fibrosis.

PATIENTS AND METHODS

Systematic literature searching identified studies performed on Egyptian territory to evaluate APRI and hyaluronic acid as non-invasive tests of fibrosis and using liver biopsy as the reference standard. Meta-analysis was performed for areas with an adequate number of publications. Validation of meta- analysis on APRI was done on a subset of 150 treatment-naïve post-hepatitis C patients.

RESULTS

Both APRI and hyaluronic acid have superior predictive power for hepatic cirrhosis (F4) than for significant fibrosis (F2-F3). The pooled estimate for sensitivities and specificities of APRI and hyaluronic acid to diagnose F4 were (84% and 82%) and (83% and 89%) respectively. In the subgroup of treatment naïve post-hepatitis C patients, APRI had higher diagnostic performance to diagnose liver cirrhosis with 93.8% sensitivity and 72.4% specificity (AUC; 0.908, 95%CI; 0.851-0.965, p-value; <0.001) compared to its accuracy to diagnose significant hepatic fibrosis with 65.1% sensitivity and 77.8% (AUC; 0.685, 95% CI; 0.59-0.78, p-value; 0.001).

CONCLUSION

APRI score and hyaluronic acid levels are simple and reliable non-invasive markers to detect advanced fibrosis among post-hepatitis C patients.

Combined use of Epithelial Membrane Antigen and Nuclear Matrix Protein 52 as Sensitive Biomarkers for Detection of Bladder Cancer

Background

The advent of noninvasive urine-based markers as well as other novel modalities has yielded improved diagnostic accuracy. However, the new markers failed to reach higher sensitivity and specificity. We therefore evaluated the potential role of epithelial membrane antigen (EMA) and nuclear matrix protein 52 (NMP-52) singly and combined as noninvasive biomarkers for the detection of bladder cancer (BC).

Methods

A total of 160 individuals including 66 patients with BC, 54 patients with benign urologic disorders and 40 healthy volunteers were investigated. Urinary EMA at 130 kDa and NMP at 52 kDa were identified, purified and quantified by Western blot, electroelution and enzyme-linked immunosorbent assay (ELISA). The diagnostic performance of each biomarker and their combination were compared using area under receiver operating characteristic curves (AUC).

Results

Mean urinary EMA, 2.42 µg/mL, and NMP-52, 17.85 µg/mL, were significantly elevated in patients with BC compared to controls, 1.18 and 3.44 µg/mL, respectively (p<0.0001). The combined use of these markers yielded values which were increased 4.4- and 13.7-fold in the benign and malignant disease groups, respectively, with respect to the normal group. The values of EMA and NMP-52 were significantly higher in patients with higher-grade tumors than those with lower-grade tumors (p<0.0001). Moreover, this combination could predict all BC stages and grades with 0.91 AUC, 94% sensitivity and 80% specificity.

Conclusions

EMA and NMP-52 in combination could be promising noninvasive biomarkers for BC detection.

Discrimination of fibrotic staging of chronic hepatitis C using multiple fibrotic markers

AIM

In order to evaluate and judge a fibrotic stage of patients with chronic hepatitis C, multivariate regression analysis was performed using multiple fibrotic markers.

METHODS

A total of 581 patients from eight hepatology units and institutes were diagnosed by needle biopsy as having chronic liver disease caused by hepatitis C virus. Twenty-three variables and their natural logarithmic transformation were employed in the multivariate analysis.

RESULTS

Multivariate regression analysis finally obtained the following function: z = 2.89 × ln (type IV collagen 7S) (ng/mL) - 0.011 × (platelet count) (×10(3) /mm(3) ) + 0.79 × ln (total bilirubin) (mg/dL) + 0.39 × ln (hyaluronic acid) (μg/L) - 1.87. Median values of the fibrotic score of F1 (n = 172), F2 (n = 80), F3 (n = 37) and F4 (n = 16) were calculated as 1.00, 1.45, 2.82 and 3.83, respectively. Multiple regression coefficient and coefficient of determination were 0.56 and 0.320, respectively. Validation with patient data from other institutions demonstrated good reproducibility of the fibrotic score for hepatitis C (FSC), showing 1.10 in F1 (n = 156), 2.35 in F2 (n = 73), 3.16 in F3 (n = 36) and 3.58 in F4 (n = 11).

CONCLUSION

A concise multiple regression function using four laboratory parameters successfully predicted pathological fibrotic stage of patients with hepatitis C virus infection.