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]
