Automated Detection and Quantification of Prostate Cancer in Needle Biopsies by Digital Image Analysis

Abstract

Introduction: Triple immunohistochemical (IHC) stains including antibodies specific for alpha-methylacyl-CoA-racemase and basal cell markers have been a valuable aid in accurate identification of prostate carcinoma. However, accurate quantification of minuscule areas of prostate carcinoma in biopsy specimens can often be a challenge. Here we assessed the diagnostic value and quantitative use of automated digital image analysis on triple IHC stained prostate needle biopsies. Methods: Twelve cases of prostate needle biopsy material including 75 needle cores were stained with triple-antibody cocktail (P504S + 34βE12 + p63). Slides were digitally scanned with the APERIO digital image analyzer and evaluated with the GENIE pattern and color recognition digital image analysis that we developed. A slide with known areas of adenocarcinoma, high grade prostatic intraepithelial neoplasia (PIN), benign glands and stroma was used as a training set for the automated digital image analysis platform. Results: Among 75 needle biopsy cores, 19 (25.33%) contained adenocarcinoma by histology. Digital image analysis recognized adenocarcinoma in 95% of these needle biopsies. The average area of the needle biopsy was 7.63 mm2 and overall the average area of tumor was 0.196 mm2. The smallest area of tumor recognized by the program was 0.0022 mm2 (0.0363% of the core) and the largest was 0.62 mm2 (8.17% of the core) among needle core biopsies. False positives resulted from areas of high grade PIN with patchy basal cells. The false negative was caused by uneven AMACR staining in one area of adenocarcinoma. Digital recognition of areas of interest was improved by three successive image analysis training which increased the sensitivity and specificity from 83% and 89% to 90% and 93%, respectively. Conclusions: Digital image analysis in concert with IHC triple staining may be useful for accurate detection and quantitative analysis of small foci of prostatic adenocarcinoma. Defining methods to increase the sensitivity and specificity of quantitative automated digital image analysis will likely evolve as an area of investigation. Future automated digital scanning and innovative pattern and color recognition technologies may open avenues for classifying a variety of prostate lesions.