A quantitative exploration of efficacy of gland morphology in prostate cancer grading

Currently, prostate cancer diagnosis is done qualitatively by pathologists who visually analyze tissue architecture while largely ignoring gland morphology. In this study we have developed an automated image analysis scheme for grading prostate cancer by quantitatively analyzing morphological features of individual glands from digitized histological images. Following automated gland boundary segmentation via level sets, 7 boundary features are extracted. Non-linear dimensionality reduction is then applied to the set of extracted features. A Support vector machine (SVM) classifier is then used to classify tissue patches corresponding to benign epithelium, and prostate cancer grades 3 and 4 in a lower dimensional embedding space. We obtained an accuracy of 75.00% in distinguishing benign epithelium and grade 3, 85.71% between benign epithelium and grade 4, and 72.73% between grade 3 and grade 4. Our results strongly suggest that quantitative analysis of gland boundary morphology may play a significant clinical role in distinguishing different prostate cancer Gleason grades.