Correlations Between Interstitial Stromal Fibrillary Network And Disease Progression In Hepatitis C

Introduction/ Background Since the virus description in the 80s [1] , [2], the infection with hepatitis virus C (HVC) became a real health problem because 50-80% of acute HVC infections evolve to chronic hepatitis, from which 4–20% of patients develop liver cirrhosis within 20 years and, finally, the risk of developing HCC of patients with liver cirrhosis is 1–5% per year [3]. The principal stage of pathological processes is the interstitial space and mainly the portal areas. Fibrillary network, one of the important components of the interstitial space, undergoes dramatic and highly variable changes, fibrosis representing one of the elements of the morphologic triad of the pathologic conflict within the liver parenchyma. Aims The aim of the study is to assess quantitatively the liver fibrosis on biopsy fragments of patients with virus C hepatitis (VCH) and to compare it with the fibrosis score described qualitatively in METAVIR system. Methods The studied material was represented by liver biopsies from 87 patients with VCH. Tissue fragments were processed following classical histological techniques (formalin fixation and paraffin embedment) and serial sections were stained, for each case, with Hematoxylin Eosin and Mason Trichrome. Tissue fragments images were acquired with a dedicated optical system, using   the X10 objective and the portal and periportal areas were acquired using X20 objective. The fibrosis was firstly assessed using METAVIR qualitative score for fibrosis (MV-F1, MV-F2 and MV-F3). There were no cases with no fibrosis or with cirrhosis. The quantitative parameters determined were: total area of examined hepatic parenchyma, portal spaces area, total area of fibrosis and area of portal fibrosis. The quantitative parameters calculated were: the percentage of the total parenchymal area represented by fibrosis (TF/TA-HP), the percentage of the parenchymal area represented by the portal spaces (PS-A/TA-HP), the percentage of the portal spaces represented by the portal fibrosis (PS-F/PS-A). The measurements were made with two dedicated software programs, after preceding software calibration. For numerical parameters minimum (MIN), maximum (MAX) mean (AV) values and standard deviation (STDEV) were calculated. For comparison with METAVIR fibrosis score grades, the values of quantitative parameters calculated were stratified in classes. For statistical analysis of the correlation between the quantitative and qualitative assessment of fibrosis, t-test (2-sample, unequal variance), One-Way ANOVA test and χ2 test were used. Results TF/TA-HP and PS-A/TA-HP correlated with the METAVIR degrees of fibrosis (Figure  1). Both correlations were statistically validated at very high significance level. (Figure 2). In turn, PS-F/PS-A didn’t correlate with the METAVIR degrees of fibrosis, as statistical tests revealed (Figures 1 and 2).So, in VCH, one of the main morphological aspects is the constant enlargement of portal spaces but with a reduced extension of the destructive and reparatory processes towards the lobule center. Collagen fibers production is not an accelerated process, being in a relative equilibrium with the reactive inflammatory cellular population as demonstrated by the relatively constant percentage of the portal spaces represented by the fibrillary structures. Figure 1.  Figure 2

Fractal Behavior Of Gleason And Srigley Grading Systems

INTRODUCTION / BACKGROUND: Prostate cancer remains one of the major malignancies of modern society. The need of grading this malignancy is still in dispute. Two major grading systems have emerged and are world-wide adapted: Gleason grading system [1] and Srigley grading system [2]. Both systems use optical subjective descriptions of different architec- tural growth patterns of prostate adenocarcinoma. The fractal dimension (FD) is used in the medical field as an objective feature for describing a given image rather than showing a precise value for a known fractal. The FD can be an objective measurement for different patterns description. AIMS: The aim of our study is to assess the fractal behavior of images labeled according to Gleason and Srigley grading systems both in terms of in-class and inter-class variation. METHODS: 299 Gömöri stained microscopic digital images of prostate adenocarcinoma were labeled independently according to Gleason and Srigley patterns. Each image was firstly transformed to grayscale then a maximum cropped square of the image was resized to a standard 256x256 pixel image. For the resulted images the fractal dimension was approximated with two different algorithms: a standard box-counting algorithm (applied to the binary image obtained with Roberts’s method for edge detection) and a novel algorithm that is applied to the grayscale version of the image consisting in the ratio between image’s volume and area (R-VA) at different scales [3]. In-class variation was assessed as the average standard deviation (SD).Lower SDmeans better discrimination. For the inter-class variation assessment each class was compared with all other classes using a two-tail, Student’s t-test. The resulted value was defined as the ratio between the statistically different means and the total number of comparisons. The maximum possible value for Gleason grading system was 28, be- cause there were no images labeled as Gleason pattern 1, while for the Srigley grading system the maximum possible value was 6. RESULTS: In-class variation was 0.045 using the box-counting algorithm and 0.048 using the R-VA algorithm for Gleason grading system and 0.161 using the box-counting algorithm and 0.178 using the R-VA algorithm for Srigley grading system. Inter-class variation was, for Gleason grading system 13/28 using the box-counting algorithm and 20/28 using the R-VA algorithm while for the Srigley grading system was 3/6 using the box-counting algorithm and 5/6 using the R-VA algorithm respectively. Srigley grading system seems to perform better than Gleason’s on inter-class variation, but has lower performance on in-class variation. Nevertheless, we must note that there is a large difference between the two systems regarding the number of classes. The FD computed with the R-VA algorithm has better discrimination results than the one computed with the box-counting algorithm in both grading systems, thus proving once again the R-VA’s performance [3]