Hepatitis-2015
Orlando, USA
July 20 - 22 2015
Mayson Aburaya
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 (PEGIFN) 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 (PEGIFN) 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 (PEGIFN) 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 (PEGIFN) 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 (PEGIFN) 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
Morphometry
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
Background
Morphometry
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
Background
Morphometry
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
Background
Morphometry
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
Background
Morphometry
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).
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction
Background
Morphometry
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).
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction
Background
Morphometry
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).
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Introduction
Background
Morphometry 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).
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Hypothesis
It is possible that these data would be
early predictive factors to the response
of HCV virus to anti-viral treatment.
These differences maybe related to the response to
anti-viral treatment.
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Hypothesis
It is possible that these data would be
early predictive factors to the response
of HCV virus to anti-viral treatment.
These differences maybe related to the response to
anti-viral treatment.
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Hypothesis
It is possible that these data would be
early predictive factors to the response
of HCV virus to anti-viral treatment.
These differences maybe related to the response to
anti-viral treatment.
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
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
Methods-Study Population
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Methods- Study design
Clinical data
30 patients SVR
Pre treatment
histologic biopsy Histolomorphometric
analysis
Textural analysis
60 chronic HCV
patients with
genotype 1
Clinical data
30 patients – NON
SVR
Pre treatment
histologic biopsyHistolomorphometric
analysis
Textural analysis
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Methods- Histomorphometric analysis
Histomorphometric analysis
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
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
A
B
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).
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Methods- Histomorphometric analysis
A
B
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Methods- Textural analysis analysis
A
B
C
• Figure 3 – convolution MASK: A – parenchymal tissue magnified x10
scanned in light microscope. B- MASK image, C – image processed by
MATLAB software.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Methods- Textural analysis
A
B
C
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)
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Methods- Variables
Dependent
variable
Response to anti-viral treatment (SVR)
Or
NON Response to treatment (NON SVR).
Demographic and clinical
variables
Independent
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
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Textural analysis
variables:
Lacunarity;
Fractal analysis
GLCM analysisEntropy
Correlation
Hemogeneity;
Contrast
Methods- Statistical methods
Kolmogorov Smirnov test
Data distribution
Pearson’s Chi Square test
Correlation between continuous variables
Spearman’s test
Categorical variables
Chi-Square test
Relations between binary variables
Discriminant Analysis
Prediction level
Neural network (NNET)
ROC Analysis Curves
A model to discriminate and predict a response to treatment based on nonparametric 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
Results- Descriptive Data
Most participants in the study are of
Russian origin: 67% in the SVR group
and 70% in the NON SVR group
TABLE-1 DESCRIPTIVE
TABLE
Sociodemographic
characteristics
Gender
Male
Female
Age (yr)
BMI Kg\m2
ORIGIN
UKRAINE
RUSSIA
ISREAL
RUMANIA
KAZAHISTAN
Habits
* Alcohol
Smoking
Group 1 -SVR (n=29)
% or mean (SD)
Group 2 -non SVR ( n=29)
% or mean (SD)
60%
40%
42 (11)
25 (3.38)
53%
47%
47 (8.9)
26 (3.7)
20%
67%
7%
7%
0%
16%
70%
7%
0%
7%
50%
43%
13%
40%
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Results- Descriptive Data
Laboratory data
Group 1 -SVR (n=29) Group 2 -non SVR ( n=29)
% or mean (SD)
% 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\
µL)
(1912)6968
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)
221655 (57000)
213439 (61000)
1A
20%
0%
1B
80%
100%
2887520
3874280
Platelets count
(LNL=150000/ µL)
Genotype
Viral Load ( before treatment)
IU\ml
Metavir Fibrosis score
F1
F2
F1-2
Inflammation
A1
A2
A3
A1-2
A2-3
Treatment
COPEGUS+ PEGSYS 24w
COPEGUS+ PEGSYS 48w
COPEGUS+ PEGSYS 72w
PEGINTERON + RIBAVIRIN
24w
PEGINTERON + RIBAVIRIN
48w
PEGINTERON + RIBAVIRIN
72w
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Group 1 -SVR (n=29)
% or mean (SD)
Group 2 -non SVR ( n=29)
% or mean (SD)
67%
27%
6%
53%
30%
17%
20%
44%
6%
20%
10%
20%
36%
6%
14%
24%
3%
70%
10%
3%
12%
46%
3%
3%
14%
23%
0%
3%
Results- Univariate analysis
TABLE 2- UNIVARIATE ANALYSIS DEMOGRAPHIC
AND LABORATORY CHARECTERISTICS
P-value
Socio - demographic characteristics
Gender
Table 2- Influence of demographic and
laboratory data on patients' response
to medication according to Univariate
analysis
This table shows the correlation between patients'
demographic and laboratory characteristics and
belonging to the NON-SVR group compared to the
SVR group.
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)
Platelets count (LNL=150000/ µL)
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
0.7
0.968
Results- Univariate analysis
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Table 3 - Univariate Analysis of
Histomorphometric parameters
Results
Histomorphometric parameters
Table 3- Univariate Analysis of
Histomorphometric parameters:
Fibrosis analysis parameters
STD of Density of collagen fibers in portal
space
Maximal Density of collagen fibers in
portal space
Inflammation parameter
Absolute number of inflammation cells in
portal space
Portal space Area
Number of inflammation Cells\mm²
Architectural parameters
Entropy
Contrast
Homogeniety
Correlation
Architectural parameters ( matlab
analysis)
Lacunarity
Slope Average
Slope SD
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
P-value
<0.001
0.04
0.05
0.14
<0.001
0.04
0.02
0.04
0.15
0.001
0.15
0.11
Results- Discriminant Analysis
Table 4 – Clinical and histomorphometric
variables distinguishing between the two
treatment groups:
Table 4- DISCRIMINANT
P-value
ANALYSIS
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)
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
<0.001
Results
 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
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 nonresponse to treatment.
 We also calculated the relative weight
and sensitivity for each cutoff point based
on the figure below.
Area= accuracy
Area under the curve (AUC)= 0.773
Specificity: 100%
Sensitivity:93%
cut off- -15.7
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Results
Based on ROC ANALYSIS:
DS equation >- 15.7
DS equation < -15.7
predicts response to anti-viral treatment while
predicts the failure of anti-viral treatment
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Results- Summary

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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Conclusion
Conclusion
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Conclusion
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Conclusion
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Conclusion
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.
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Conclusion
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
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Conclusion
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
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
Discussion
It is possible that these data
would be early predictive
factors to the response of HCV
virus to anti-viral treatment.
Our study findings is promising and
fortifying our hypothesis
These differences maybe
related to the response to
anti-viral treatment.
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.
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.
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
Patients given
anti-viral
medication in
the past
Morphometry may be used to predict the response to the antiviral treatment( Peg- INF and RBV) in patients before treatment
beginning
That may reduce the side effects and monetary of other
treatments.
Peg- INF and RBV
Naïve patients who cannot
be treated with protease
inhibitor
Patients who cannot be treated
with protease inhibitors due to
ineligibility for government
subsidy ( F1 or genotype other
than 1)
Study limitations
It is a retrospective study.
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.
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).
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: 64-68
24. Udayakumar N, Nyingi K, Guy Ne. Predicting the probable outcome of treatment in HCV patients. Therapuetic Advances in Gastroenterology, 2009; 2: 287-302
25. 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: 705–716
41. 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.
Dr. Maison Abu Raya MD.
Rappaport faculty of medicine
Technion institute of technology
Haifa; Israel
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• Email: md.aburaya@gmail.com
The Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology
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