Computer-aided Detection of Prostate Cancer on Tissue Sections

We report an automated computer technique for detection of prostate cancer in prostate tissue sections processed with immunohistochemistry. Two sets of color optical images were acquired from prostate tissue sections stained with a double-chromogen triple-antibody cocktail combining alpha-methylacyl-CoA racetnase, p63, and high-molecular-weight cytokeratin. The first set of images consisted of 20 training images (10 malignant) used for developing the computer technique and 15 test images (7 malignant) used for testing and optimizing the technique. The second set of images consisted of 299 images (114 malignant) used for evaluation of the performance of the computer technique. The computer technique identified image segments of alpha-methylacyl-CoA racemase-labeled malignant epithelial cells (red), p63, and high-molecular-weight cytokeratin-labeled benign basal cells (brown), and secretory and stromal cells (blue) for identifying prostate cancer automatically. The sensitivity and specificity of the computer technique were 94% (16/17) and 94% (17/18), respectively, on the first (training and test) set of images, and 88% (79/90) and 97% (136/140), respectively, on the second (validation) set of images. If high-grade prostatic intraepithelial neoplasia, which is a precursor of cancer, and atypical cases were included, the sensitivity and specificity were 85% (97/114) and 89% (165/185), respectively. These results show that the novel automated computer technique can accurately identify prostatic adenocarcinoma in the triple-antibody cocktail-stained prostate sections.