59
Mayson Aburaya Hepatitis-2015 Orlando, USA July 20 - 22 2015
| Date post: | 18-Jan-2016 |
| Category: |
Documents |
| Upload: | adele-nicholson |
| View: | 218 times |
| Download: | 0 times |
Mayson Aburaya
Hepatitis-2015Orlando, USA
July 20 - 22 2015
Dr. Maison Abu Raya Rambam Health Care Campus, Haifa, Israel.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
Histomorphometric Findings May Help Predict Response To Antiviral Therapy At An Early Fibrosis Grade In Patients With
Chronic HCV Infection
Presenter: Mayson Abu Raya, MD Coauthors: Amir Klein ,MD
Tarek Saadi, MD Edmond Sabo, MD
Mentor: Prof. Yaacov Baruch, MD
Liver Unit, Department of Gastroenterology, Department of Pathology, Rambam Health Care Campus, Haifa, Israel.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
Overview
Background
Objectives
Methods
Results
Conclusion
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction
Background HCV
Worldwide, an estimated 180 million people have a chronic infection with hepatitis C virus (HCV).
HCV is a leading cause of cirrhosis and hepatocellular carcinoma and is the leading indication for liver transplantation in the United States (1).
In the United States, genotype 1 is the most predominant, especially in HIV-HCV co-infected and the African-American population (2).
The current treatment for HCV infection is peginterferon alpha (PEG-IFN) combined with ribavirin (with/without protease inhibitors).
Several viral and host factors related to viral response have been reported.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction
Background HCV
Worldwide, an estimated 180 million people have a chronic infection with hepatitis C virus (HCV).
HCV is a leading cause of cirrhosis and hepatocellular carcinoma and is the leading indication for liver transplantation in the United States (1).
In the United States, genotype 1 is the most predominant, especially in HIV-HCV co-infected and the African-American population (2).
The current treatment for HCV infection is peginterferon alpha (PEG-IFN) combined with ribavirin (with/without protease inhibitors).
Several viral and host factors related to viral response have been reported.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction
Background HCV
Worldwide, an estimated 180 million people have a chronic infection with hepatitis C virus (HCV).
HCV is a leading cause of cirrhosis and hepatocellular carcinoma and is the leading indication for liver transplantation in the United States (1).
In the United States, genotype 1 is the most predominant, especially in HIV-HCV co-infected and the African-American population (2).
The current treatment for HCV infection is peginterferon alpha (PEG-IFN) combined with ribavirin (with/without protease inhibitors).
Several viral and host factors related to viral response have been reported.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction
Background HCV
Worldwide, an estimated 180 million people have a chronic infection with hepatitis C virus (HCV).
HCV is a leading cause of cirrhosis and hepatocellular carcinoma and is the leading indication for liver transplantation in the United States (1).
In the United States, genotype 1 is the most predominant, especially in HIV-HCV co-infected and the African-American population (2).
The current treatment for HCV infection is peginterferon alpha (PEG-IFN) combined with ribavirin (with/without protease inhibitors).
Several viral and host factors related to viral response have been reported.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction
Background HCV
Worldwide, an estimated 180 million people have a chronic infection with hepatitis C virus (HCV).
HCV is a leading cause of cirrhosis and hepatocellular carcinoma and is the leading indication for liver transplantation in the United States (1).
In the United States, genotype 1 is the most predominant, especially in HIV-HCV co-infected and the African-American population (2).
The current treatment for HCV infection is peginterferon alpha (PEG-IFN) combined with ribavirin (with/without protease inhibitors).
Several viral and host factors related to viral response have been reported.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction BackgroundMorphometry
Morphometry is a field that investigates changes in shape, size and orientation of objects.
Several methods exist for the extraction of morphological parameters of an object.
These include length, angles, perimeter shape and distribution in the space.
Morphometry allows for the quantification of these parameters, which can highlight areas with significant differences.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction BackgroundMorphometry
Morphometry is a field that investigates changes in shape, size and orientation of objects.
Several methods exist for the extraction of morphological parameters of an object.
These include length, angles, perimeter shape and distribution in the space.
Morphometry allows for the quantification of these parameters, which can highlight areas with significant differences.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction BackgroundMorphometry
Morphometry is a field that investigates changes in shape, size and orientation of objects.
Several methods exist for the extraction of morphological parameters of an object.
These include length, angles, perimeter shape and distribution in the space.
Morphometry allows for the quantification of these parameters, which can highlight areas with significant differences.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction BackgroundMorphometry
Morphometry is a field that investigates changes in shape, size and orientation of objects.
Several methods exist for the extraction of morphological parameters of an object.
These include length, angles, perimeter shape and distribution in the space.
Morphometry allows for the quantification of these parameters, which can highlight areas with significant differences.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
BackgroundMorphometry
In recent years, morphometry has been used to better predict disease phenotype and prognosis in several fields.
Various studies used morphometry in liver diseases.One study found that the evaluation of the amount of liver fibrosis by computer-assisted digital image analysis (DIA) was better correlated to the amount of pressure differentials of the hepatic veins (HVPG) (15).
Another study showed that morphometry is a good method to follow the progress of liver fibrosis in patients with chronic HCV (16).
Introduction
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
BackgroundMorphometry
In recent years, morphometry has been used to better predict disease phenotype and prognosis in several fields.
Various studies used morphometry in liver diseases.One study found that the evaluation of the amount of liver fibrosis by computer-assisted digital image analysis (DIA) was better correlated to the amount of pressure differentials of the hepatic veins (HVPG) (15).
Another study showed that morphometry is a good method to follow the progress of liver fibrosis in patients with chronic HCV (16).
Introduction
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
BackgroundMorphometry
In recent years, morphometry has been used to better predict disease phenotype and prognosis in several fields.
Various studies used morphometry in liver diseases.One study found that the evaluation of the amount of liver fibrosis by computer-assisted digital image analysis (DIA) was better correlated to the amount of pressure differentials of the hepatic veins (HVPG) (15).
Another study showed that morphometry is a good method to follow the progress of liver fibrosis in patients with chronic HCV (16).
Introduction
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
BackgroundMorphometry
Morphometric analysis in other fields:
In a recent study, morphometric analysis of biopsies taken from the colon of patients with colitis due to Crohn's Disease was used to classify and predict the clinical phenotype by retrospective (20).
Morphometric analysis of cancerous cells from squamous carcinoma of the vulva and kidney carcinoma allowed the prediction of lymph node involvement and illness prognosis (12, 13).
Introduction
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Hypothesis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
At the same level of inflammation or fibrosis according to the METAVIR method, there are morphometric differences in regard to inflammation and fibrosis and differences in the texture of liver tissue in different patients.
These differences maybe related to the response to anti-viral treatment.
It is possible that these data would be early predictive factors to the response of HCV virus to anti-viral treatment.
Hypothesis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
At the same level of inflammation or fibrosis according to the METAVIR method, there are morphometric differences in regard to inflammation and fibrosis and differences in the texture of liver tissue in different patients.
These differences maybe related to the response to anti-viral treatment.
It is possible that these data would be early predictive factors to the response of HCV virus to anti-viral treatment.
Hypothesis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
At the same level of inflammation or fibrosis according to the METAVIR method, there are morphometric differences in regard to inflammation and fibrosis and differences in the texture of liver tissue in different patients.
These differences maybe related to the response to anti-viral treatment.
It is possible that these data would be early predictive factors to the response of HCV virus to anti-viral treatment.
Aims
1. Quantification of histological findings from patients with chronic HCV using computerized morphometrics.
2. Prediction of response to medical treatment of chronic HCV using baseline histomorphometric findings.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Methods
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Methods- Study design
A Retrospective study
All clinical data was blinded to patient identification.
Histolomorphometric analysis has been blinded to patient identification or previous histological information.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Inclusion criteria
Chronic infection with HCV genotype 1.
Patients naïve to anti-viral treatment,
Viremia level above 400,000 IU/ml prior to the treatment.Treatment of HCV was by combination of Peg-INF and RBV.Liver biopsy at most a year before treatment with fibrosis level of F1 or F2 based on the Metavir Score.
Exclusion criteria
Patients under 18 years of age or above 65 years of age.
Non-naïve patients (patients given anti-viral treatment in the past).Patients who stopped the anti-viral treatment due to side effects.If the liver biopsy was performed over a year before treatment.Fibrosis level according to Metavir score below F1 or above F2.
Viremia level below 400,000 IU/ml.
HCV genotype other than 1.
Patients with background of another liver disease,
Alcoholic patients or patients with HBV or HIV.
Methods-Study Population
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
60 chronic HCV patients with genotype 1
30 patients SVR
Clinical data
Pre treatment histologic biopsy -
Histolomorphometric analysis
Textural analysis
30 patients – NON SVR
Clinical data
Pre treatment histologic biopsy-
Histolomorphometric analysis
Textural analysis
Methods- Study design
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Slides were scanned using the dot slide virtual microscopy (Olympus) system.
The entire slide was manually scanned, 3-4 representative images were recorded from each slide.
Each biopsy contained 6-8 representative portal spaces in average.
The Imagepro plus 7.0 (Mediacybernetics USA) program has been used to analyze and quantify collagen fibers, inflammatory cells and liver architecture.
MATLAB (Mathworks USA) program has been used to analyze fractal and lacunar dimension, giving an indication of the architectural distortion in the liver parenchyma.
Methods- Histomorphometric analysis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Histomorphometric analysis
BA
Figure 1 – Quantification of inflammatory cells in the hepatic portal space: A – image of hepatic portal space magnified x10 scanned in light microscope with TRICHROME staining. B- red marking of inflammatory cells within the hepatic portal space (border in green).
Methods- Histomorphometric analysis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
BA
Figure 2 – fibrosis measurement in the hepatic portal space compared to the area: image of hepatic portal space magnified x10 scanned in light microscope. A – collagen fibers in the liver tissue are stained with TRICHROME staining and appear in blue. B – the hepatic portal space border is shown in green and the collagen fibers in red.
Methods- Histomorphometric analysis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
• Figure 3 – convolution MASK: A – parenchymal tissue magnified x10 scanned in light microscope. B- MASK image, C – image processed by MATLAB software.
BA C
Methods- Textural analysis analysis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Figure 4 – image processed by the GLCM method: A- parenchymal tissue magnified x10 scanned in light microscope
B- Grey white scale image C- image processing by GLCM (Parameters: homogeneity; contrast; correlation and
entropy)
A B C
Methods- Textural analysis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Dependent variable
Independent variables
Demographic and clinical variables•Age, sex, ethnicity, height, weight, BMI, background illnesses, habits – alcohol, smoking
•type of interferon given to the patient: PEG-INF-alpha 2a or PEG-INF-alpha 2b and duration of treatment,
Laboratory variables: •Liver enzyme level,
•blood count•albumin•INR levels
Histomorphometric variables: * Amount of inflammation and fibrosis in the hepatic portal space * parenchyma texture in liver biopsy
Textural analysis variables:
Lacunarity; Fractal
analysis GLCM
analysis- Entropy Correlation
Hemogeneity; Contrast
Response to anti-viral treatment (SVR) Or
NON Response to treatment (NON SVR).
Methods- Variables
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Kolmogorov Smirnov test
Pearson’s Chi Square test
Spearman’s test
Chi-Square test
Discriminant Analysis
Neural network (NNET)
ROC Analysis Curves
Methods- Statistical methods
Data distribution
Correlation between continuous variables
Categorical variables
Relations between binary variables
Prediction level
A model to discriminate and predict a response to treatment based on non-parametric data.To reach the cut-off points showing the best prediction for response to treatment. A P-value of 5% or less was considered to be statistically significant.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Results
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
TABLE-1 DESCRIPTIVE TABLE
Group 1 -SVR (n=29) % or mean (SD)
Group 2 -non SVR ( n=29) % or mean (SD)
Sociodemographic characteristics
Gender
Male 60% 53%
Female 40% 47%Age (yr) 42 (11) 47 (8.9)BMI Kg\m2 25 (3.38) 26 (3.7)ORIGIN
UKRAINE 20% 16%
RUSSIA 67% 70%
ISREAL 7% 7%
RUMANIA 7% 0%
KAZAHISTAN 0% 7%Habits * Alcohol 50% 13% Smoking 43% 40%
Most participants in the study are of Russian origin: 67% in the SVR group and 70% in the NON SVR group
Results- Descriptive Data
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Metavir Fibrosis score
Group 1 -SVR (n=29) % or mean (SD)
Group 2 -non SVR ( n=29) % or mean
(SD)
F1 67% 53% F2 27% 30% F1-2 6% 17%Inflammation A1 20% 20% A2 44% 36% A3 6% 6% A1-2 20% 14% A2-3 10% 24%Treatment COPEGUS+ PEGSYS 24w 3% 12%COPEGUS+ PEGSYS 48w 70% 46%COPEGUS+ PEGSYS 72w 10% 3%PEGINTERON + RIBAVIRIN 24w
3% 3%
PEGINTERON + RIBAVIRIN 48w
14% 23%
PEGINTERON + RIBAVIRIN 72w 0% 3%
Results- Descriptive Data
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Laboratory data Group 1 -SVR (n=29) % or mean
(SD)
Group 2 -non SVR ( n=29) % or mean
(SD)
*ALT (UNL=60 U\L) 75.3(61) 71( 33)
*ALK. PHOS. (UNL=120 U\L) 73 (18) 66.7 (24)
Albumin (LNL=3.2 gr\dl) 4.38 (0.46) 4.27 (0.3)
Billirubin (UNL=1.2 mg\dl) 0.73 (0.25) 0.68 (0.23)
White blood count (LNL=4000\ µ L)
6968( 1912) 5790 ( 1693)
Hemoglobin (LNL=11.5 g\dl) 14.6 (1.49) 13.6 (1.49)
*INR (UNL=1.1) 1.07 (0.18) 0.98 (0.05)
Platelets count (LNL=150000/µ L)
221655 (57000) 213439 (61000)
Genotype
1A 20% 0%
1B 80% 100%Viral Load ( before treatment) IU\ml
2887520 3874280
Results- Univariate analysis
Table 2- Influence of demographic and laboratory data on patients' response to medication according to Univariate analysisThis table shows the correlation between patients' demographic and laboratory characteristics and belonging to the NON-SVR group compared to the SVR group.
TABLE 2- UNIVARIATE ANALYSIS DEMOGRAPHIC AND LABORATORY CHARECTERISTICS P-valueSocio - demographic characteristics
Gender
Male 0.635
Female 0.225
Age (yr) 0.05
BMI Kg\m2 0.63
Laboratory data
ALT (UNL=60 U\L) 0.7
ALK. PHOS. (UNL=120 U\L) 0.1
Albumin (LNL=3.2 gr\dl) 0.1
Billirubin (UNL=1.2 mg\dl) 0.7
White blood count (LNL=4000\ µ L) 0.026
Hemoglobin (LNL=11.5 g\dl) 0.048
INR (UNL=1.1) 0.7
Platelets count (LNL=150000/µ L) 0.968
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Figure 3 – Average age in the two study groups (P-Value= 0.05)average age of patients in the SVR group was lower compared to the non-SVR group (42 years vs. 47 years).
Figure 4 – Leukocyte average in the two study groups prior to treatment (P-Value= 0.026)
Figure 5 – Average Hemoglobin level in the two study groups (P-Value 0.048)
The leukocyte and hemoglobin levels in peripheral blood in the SVR group patients were higher compared to the NON-SVR group as seen in figures 4 and 5.
Results- Univariate analysis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Table 3 - Univariate Analysis of Histomorphometric parameters
Histomorphometric parameters
P-value
Fibrosis analysis parameters STD of Density of collagen fibers in portal space
<0.001
Maximal Density of collagen fibers in portal space
0.04
Inflammation parameter Absolute number of inflammation cells in portal space
0.05
Portal space Area 0.14 Number of inflammation Cells\mm² <0.001 Architectural parameters Entropy 0.04 Contrast 0.02 Homogeniety 0.04 Correlation 0.15 Architectural parameters ( matlab analysis)
Lacunarity 0.001 Slope Average 0.15 Slope SD 0.11
Table 3- Univariate Analysis of Histomorphometric parameters:
Results
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Table 4- DISCRIMINANT ANALYSIS
P-value
Demographic and clinical parameters
Hemoglobin <0.001 Fibrosis analysis parameters
STD of Density of collagen fibers in portal space
<0.001
Inflammation parameter
Number of inflammation Cells\mm²
<0.001
Architectural parameters
Contrast- max <0.001
Correlation- avg <0.001
Lacunarity (avg) <0.001
Results- Discriminant Analysis
Table 4 – Clinical and histomorphometric variables distinguishing between the two treatment groups:
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Regression coefficients provided by the model (B=slope, Constant=intercept) were used to calculate Discriminant scores in both groups based on Fisher's linear discriminant functions equation.
The formula included parameters of: Histophotometric analysis Textural analysis Lacunar analysis Clinical parameters
Results
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Results- Predictive Formula
• DS= discriminant score DS= discriminant score DS= discriminant score
DS= 205.370+(Hemoglobin*-19.079)+ ( Density\intensity (STD) max *-5.396)+( Cells\mm² -avg *0.003)+ ( Correlation- avg *-86812.696)+( Contrast- max *0.001)+( Lacunarity (avg)mn *-94.506)
This formula could be used to predict response to anti-viral treatment.
Results- Roc Analysis
Figure 6 - Receiver operating characteristics curves (ROC) of morphometry and clinical parameters on differentiating between SVR and NON SVR groups
We use ROC curves to find the best cutoff points in these DS which will be able to distinguish between response and non-response to treatment.
We also calculated the relative weight and sensitivity for each cutoff point based on the figure below.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Area= accuracyArea under the curve (AUC)= 0.773Specificity: 100%
Sensitivity:93% cut off- -15.7
Results
Based on ROC ANALYSIS:
DS equation >- 15.7 predicts response to anti-viral treatment while DS equation < -15.7 predicts the failure of anti-viral treatment
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Statistically significant parameters: Clinical parameters including: age, white blood cell count and hemoglobin concentration Histomorphometric variables including: the density of collagen fibers the number of inflammatory cells in the portal space Textural parameters
They were used together as a formula in order to predict response to treatment in HCV patients
with sensitivity of 93%, and 100% specificity.
Results- Summary
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Conclusion
Apart from predicting treatment success, this study showed that histological parameters of liver tissue have prognostic significance.
Histomorphometric and texture analysis using the histomorphomertic method is promising
Morphometry may contribute to developing an expert guided automatic system predicting response to treatment in chronic HCV patients
This method may be used at an early stage when histological changes are minimal, which may affect choosing suitable treatment for each patient.
Conclusion
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Our study indicates that:
Apart from predicting treatment success, this study showed that histological parameters of liver tissue have prognostic significance.
Histomorphometric and texture analysis using the histomorphomertic method is promising
Morphometry may contribute to developing an expert guided automatic system predicting response to treatment in chronic HCV patients
This method may be used at an early stage when histological changes are minimal, which may affect choosing suitable treatment for each patient.
Conclusion
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Our study indicates that:
Apart from predicting treatment success, this study showed that histological parameters of liver tissue have prognostic significance.
Histomorphometric and texture analysis using the histomorphomertic method is promising
Morphometry may contribute to developing an expert guided automatic system predicting response to treatment in chronic HCV patients
This method may be used at an early stage when histological changes are minimal, which may affect choosing suitable treatment for each patient.
Conclusion
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Our study indicates that:
Apart from predicting treatment success, this study showed that histological parameters of liver tissue have prognostic significance.
Histomorphometric and texture analysis using the histomorphomertic method is promising
Morphometry may contribute to developing an expert guided automatic system predicting response to treatment in chronic HCV patients
This method may be used at an early stage when histological changes are minimal, which may affect choosing suitable treatment for each patient.
Conclusion
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Our study indicates that:
As far as we know, this is the first study of its kind in the world which tested the relation between morphometric parameters and the chance for treatment
Further research is needed in the future both in patients with HCV and in patients with other liver diseases in order to check if there is a relation with prognosis and treatment response
Conclusion
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
As far as we know, this is the first study of its kind in the world which tested the relation between morphometric parameters and the chance for treatment
Further research is needed in the future both in patients with HCV and in patients with other liver diseases in order to check if there is a relation with prognosis and treatment response
Conclusion
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
We have hypothesized that the same level of inflammation or fibrosis according to the METAVIR method, there are morphometric differences in regard to inflammation and fibrosis.
Our study findings is promising and fortifying our hypothesis
These differences maybe related to the response to anti-viral treatment.
It may be hypothesized that interferon may accelerate the immune response of the body in different ways and in different patients, and that the morphometric test may be able to identify the patients in which the activity of interferon will be maximal.
It is possible that these data would be early predictive factors to the response of HCV virus to anti-viral treatment.
Discussion
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Discussion
Importance of our study:
The accepted treatment in Israel combination of PEG-INF, Ribavarin and a protease inhibitor
(Telaprevir or Boceprevir).
HCV genotype 1 naïve to treatment with fibrosis level F2 or higher
Naïve patients who cannot be treated with protease
inhibitor
Peg- INF and RBV
Patients given anti-viral
medication in the past
Patients who cannot be treated with protease inhibitors due to
ineligibility for government subsidy ( F1 or genotype other
than 1)
Morphometry may be used to predict the response to the anti-viral treatment( Peg- INF and RBV) in patients before treatment beginningThat may reduce the side effects and monetary of other treatments.
Study limitations
It is a retrospective study.
Recently there are new HCV treatments which are highly effective and not based on the treatment with PEG-INF. Recent studies show that the success rate in these treatments is very high (31).
These methods include fibrotest and fibroscan (32), and thus for some of the patients we lack an available liver biopsy for performing the morphometric tests.
Additionally, recently there is preference for non-invasive methods for evaluating the severity of liver damage which replace liver biopsy in some of the patients.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
References
1. McHutchison JG, Everson GT, Gordon SC, Jacobson IM, Sulkowski M, Kauffman R, McNair L, Alam J, Muir AJ; PROVE1 Study Team. Telaprevir with peginterferon and ribavirin for chronic HCV genotype 1 infection. N Engl J Med, 2009; 360:1827-1838.
2. Liu-Young G, Kozal MJ, Hepatitis C protease and polymerase inhibitors in development. AIDS Patient Care STDS 2008;22:449-455.
3. Conjeevaram HS, Fried MW, Jeffers LJ, Terrault NA, Wiley-Lucas TE, Afdhal N, Brown RS, Belle SH, Hoofnagle JH, Kleiner DE, Howell CD; Virahep-C Study Group. Peginterferon and ribavirin treatmentin African American and Caucasian American patients with hepatitisC genotype 1; Gastroenterology, 2006;131:470–477.
4. Jeffers LJ, Cassidy W, Howell CD, Hu S, Reddy KR. Peginterferon alfa-2a (40 kd) and ribavirin for black American patients with chronic HCV genotype 1. Hepatology, 2004;39:1702–1708.
5. Muir AJ, Bornstein JD, Killenberg PG; Atlantic Coast Hepatitis Treatment Group. Peginterferon alfa-2b and ribavirin for the treatment of chronic hepatitis C in blacks and non-Hispanic whites. N Engl J Med, 2004;350:2265–2271.
6. Akuta N, Suzuki F, Sezaki H, Suzuki Y, Hosaka T, Someya T, Kobayashi M, Saitoh S, Watahiki S, Sato J, Matsuda M, Kobayashi M, Arase Y, Ikeda K, Kumada H. Association of amino acid substitution pattern in core protein of hepatitis C virus genotype 1b high viral load and nonvirological response to interferon-ribavirin combination therapy. Intervirology, 2005;48:372–380.
7. Pascu M, Martus P, Hohne M, Wiedenmann B, Hopf U, Schreier E, Berg T. Sustained virological response in hepatitis C virus type 1b infected patients is predicted by the number of mutations within the NS5A-ISDR: a meta-analysis focused on geographical differences. Gut, 2004;53:1345–1351.
8. Shirakawa H, Matsumoto A, Joshita S, Komatsu M, Tanaka N, Umemura T, Ichijo T, Yoshizawa K, Kiyosawa K, Tanaka E; Nagano Interferon Treatment Research Group. Pretreatment prediction of virological response to peginterferon plus ribavirin therapy in chronic hepatitis C patients using viral and host factors. Hepatology, 2008; 48: 1753–1760.
9. Walsh MJ, Jonsson JR, Richardson MM, Lipka GM, Purdie DM, Clouston AD, Powell EE. Non-response to antiviral therapy is associated with obesity and increased hepatic expression of suppressor of cytokine signalling 3 (SOCS-3) in patients with chronic hepatitis C, viral genotype 1. Gut, 2006;55:529–535.
10. Gao B, Hong F, Radaeva S. Host factors and failure of interferon alpha treatment in hepatitis C virus. Hepatology, 2004;39: 880–890.
11. Sugiyama M, Tanaka Y, Nakanishi M, Mizokami M. Novel Findings for the Development of Drug Therapy for Various Liver Diseases: Genetic Variation in IL-28B Is Associated With Response to the Therapy for Chronic Hepatitis C, J Pharm Sci, 2011; 115:263-269.
12. Lavie O, Maini I, Pilip A, Comerci G, Sabo E, Cross PA, Dawlatly B, Lopes A, Auslender R. Computerized nuclear morphometry for the prediction of inguinal lymph nodes metastases in squamous cell carcinoma of the vulva. Int J Gynecol Cancer, 2006; 16: 556-561.
13. Nativ O, Sabo E, Raviv G, Halachmi S, Moskovitz B. Value of nuclear morphometry for differentiating localized from metastatic renal cell carcinoma. Eur Urol, 1998; 33:186-189.
14. Manousou P, Dhillon AP, Isgro G, Calvaruso V, Luong TV, Tsochatzis E, Xirouchakis E, Kalambokis G, Cross TJ, Rolando N, O'Beirne J, Patch D, Thornburn D, Burroughs AK. Digital Image Analysis of Liver Collagen Predicts Clinical Outcome of Recurrent Hepatitis C Virus 1 Year After Liver Transplantation, Liver Transpl, 2011 ;17:178-188.
15. Calvaruso V, Burroughs AK, Standish R, Manousou P, Grillo F, Leandro G, Maimone S, Pleguezuelo M, Xirouchakis I, Guerrini GP, Patch D, Yu D, O'Beirne J, Dhillon AP. Computer-Assisted Image Analysis of Liver Collagen:Relationship to Ishak Scoring and Hepatic VenousPressure Gradient. Hepatology, 2009 ;49:1236-1244
16. Goodman ZD, Becker RL Jr, Pockros PJ, Afdhal NH. Progression of Fibrosis in Advanced Chronic Hepatitis C: Evaluation by Morphometric Image Analysis. Hepatology, 2007;45:886-894
17. Sabo E, Boltenko A, Sova Y, Stein A, Kleinhaus S, Resnick MB. Microscopic analysis and significance of vascular architectural complexity in renal cell carcinoma . Clin Cancer Res, 2001; 7:533-537.
18. Sabo E, Gibrat M, Sova Y, Stein A, Resnick MB. Validation of the novel indices of nuclear pleomorphism, polarity and spatial distribution in the grading of urothelial carcinoma . Anal Quant Cytol Histol, 2003. 25:53-62.
19. Sabo E, Beck AH, Montgomery EA, Bhattacharya B, Meitner P, Wang JY, Resnick MB. Computerized morphometry as an aid in determining the grade of dysplasia and progression to adenocarcinoma in Barrett's esophagus . Lab Invest, 2006; 86:1261-1271.
20. Klein A, Eliakim R, Karban A, Mazor Y, Ben-Izhak O, Chowers Y, Sabo E. Early histological findings quantified by histomorphometry allow prediction of clinical phenotypes in Crohn's colitis patients. Anal Quant Cytol Histol, 2013; 35: 95-104.
References 21. William He, Nailon Yo, Mao Ed. Texture Analysis Methods for Medical Image Characterization, Biomedical Imaging, 2010; 1:978-953.22. Geoffrey a, Geoffrey M. Henebry, Fractal signature and lacunarity in the measurement of the texture of trabecular bone in clinical CT images. Med Eng Phys,2001; 23:369–380.23. Qureshi S, Batool U, Mussarat I, Farooq U, Burki, Khan N.Pre-treatment Predictors of Response for Assessing Outcomes to Standard Treatment in Infection with HCV Genotype 3, Journal of the College of Physicians and Surgeons Pakistan, 2011; 21: 64-6824. Udayakumar N, Nyingi K, Guy Ne. Predicting the probable outcome of treatment in HCV patients. Therapuetic Advances in Gastroenterology, 2009; 2: 287-30225. Davis GL, Esteban R, Rustgi V, Hoefs J, Gordon SC, Trepo C. Interferon alfa-2b alone or in combination with ribavirin for the treatment of relapse of chronic hepatitis C. N Engl J Med,1998; 9: 339–343. 26. Invonete S, Roberto F, Feldner AN, Zarros T, Silva ED, Lucia MA. Poor response to Hepatitis C in elderly patients, Annals of Hepatology, 2013;12 : 392-398. 27. Michael JO,Norris MK,Elderiny SA, Cerda SA, Keaveny AN, Afdhal NE, Nunes D. An Assessment of Digital Image Analysis to Measure Fibrosis in Liver Biopsy Specimens of Patients with Chronic Hepatitis C. Am J Clin Pathol, 2000; 114:712-718. 28. Mirza M, Siddiqui A, Hamid S, Umar M, Shaheena B. Extent of liver inflammation in predicting response to interferon α & Ribavirin in chronic hepatitis C patients: a cohort study.Gastroenterology, 2012; 12:71. 29. Hui AY, Liew CT, Go MY, Chim AM, Chan HL, Leung NW, Sung JJ. Quantitative assessment of fibrosis in liver biopsies from patients with chronic hepatitis B. Liver International, 2004; 24: 611-8.30. Makoto Ar, Hideo Te, Kenji Ka, Tsuyoshi A, Masaru N, Akira N. Regression of Liver Fibrosis in Cases of Chronic Liver Disease Type C: Quantitative Evaluation by Using Computed Image Analysis. Intern Med, 2004; 43:902-910.40. Paul YK, Eric JL, McCone JO, Eugene R, Vierling J, Pound D, Davis M, Galati J, Stuart CG, Natarajan R, Lorenzo Ro,Frank H , Ira M , Rubin R, Kenneth Ko, Pedicone L, Clifford A, Eirum Ch, Janice A, on behalf of the SPRINT-1 investigators. Efficacy of boceprevir, an NS3 protease inhibitor, in combination with peginterferon alfa-2b and ribavirin in treatment-naive patients with genotype 1 hepatitis C infection (SPRINT-1): an open-label, randomised, multicentre phase 2 trial. Lancet, 2010; 376: 705–71641. Poynard TH, Ledinghen V, Zarski J, Stanciu C, Munteanu M, Vergniol J, France J, Trifan A, Gilles , Jean N, Vaillant Ch, Ratziu V, Charlotte F,The Fibrosis-TAGS group . Relative performances of FibroTest, Fibroscan, and biopsy for the assessment of the stage of liver fibrosis in patients with chronic hepatitis C: A step toward the truth in the absence of a gold standard. J Hepatol, 2012; 56:541-548.
Thank you Dr. Maison Abu Raya
MD.Rappaport faculty of medicine
Technion institute of technology
Haifa; Israel
• Mobile: +(972) 504281470
• Email: [email protected]
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Hepatitis– 2016 Website:
hepatitis.omicsgroup.com
Meet the eminent gathering once again at
Hepatitis-2016Dubai, UAE
October 17 - 19, 2016
![Hepatitis B virus and hepatitis C virus play different ... · alcoholic cirrhosis, hepatitis viruses, tobacco and metabolic diseases[4]. Hepatitis viruses, including hepatitis B virus](https://static.documents.pub/doc/80x56/60e46cab5bd9101a6f539e91/hepatitis-b-virus-and-hepatitis-c-virus-play-different-alcoholic-cirrhosis.jpg)