On the Classification of Prostate Carcinoma With Methods from Spatial Statistics

Abstract

Gleason grading is a common method used by pathologists to determine the aggressivity of prostate cancer on the basis of histological slide preparations. The advantage of this grading system is a good correlation with the biological behavior of the tumor, while its drawback is the subjectivity underlying the judgements of pathologists. Therefore, an automation of Gleason grading would be desirable. In this paper, we examined 780 digitized grayscale images of 78 different cases, which were split into a training and a test set. We developed two methods based on combinations of morphological characteristics like area fraction, line length, and Euler number to classify into the categories "Gleason score < 7" and "Gleason score > or = 7." In particular, the distinction between these two classes has great impact on the prognosis of patients. The agreement of each method with visual diagnosis was 87.18% and 92.31% within the training set and 66.67% and 64.10% within the test set, respectively.