Computer vision detects subtle histological effects of dutasteride on benign prostate. (25th March 2018)

• Record Type: Journal Article
• Authors: Sethi, Amit; Sha, Lingdao; Kumar, Neeraj; Macias, Virgilia; Deaton, Ryan J.; Gann, Peter H.
• Is Part Of: BJU international. Volume 122:Number 1(2018)
• Page Start: 143
• Page End: 151
• Publication Date: 2018-03-25
• Subjects: dutasteride -- computer vision -- digital image analysis -- benign prostate; Genitourinary organs -- Diseases -- Periodicals; Genitourinary organs -- Surgery -- Periodicals; Urology -- Periodicals; 616.6
• DOI: 10.1111/bju.14172
• Languages: English
• ISSNs: 1464-4096

Abstract

Abstract : Objective: To determine whether a computer vision‐based approach applied to haematoxylin and eosin (H&E) prostate biopsy images can distinguish dutasteride‐treated tissue from placebo, and identify features associated with degree of responsiveness to 5α‐reductase inhibitor (5ARI) therapy. Subjects and Methods: Our study population comprised 100 treatment‐adherent men without prostate cancer assigned to dutasteride or placebo in the REDUCE trial, who had slides available from mandatory year‐4 biopsies. Half of the men also provided slides from a year‐2 biopsy. We obtained 20× whole‐slide images and used specialized software to generate a library of 1 300 epithelial and stromal features from objects comprising superpixels and several types of nuclei, including spatial relations among objects between and within each hierarchical level. We used penalized logistic regression and fivefold cross‐validation to find optimal combinations of histological features in the year‐4 biopsies. Feature data from the year‐2 biopsies were fitted to a final model for independent validation. Two pathologists, blinded to treatment, scored each image for focal atrophy and histological features previously linked to 5AR1 treatment. Results: Consensus classification by pathologists obtained a discrimination accuracy equivalent to chance. A 21‐feature computer vision model gave a cross‐validation area under the curve of 0.97 (95% confidence interval [CI] 0.95–0.99) in the year‐4 biopsies and 0.79 (95% CI: 0.65–0.92) in the set‐aside year‐2 biopsies. Histology scores were not correlated with change in prostate‐specific antigen level, serum dihydrotestosterone level or gland volume. Key features associated with dutasteride treatment included greater shape and colour uniformity in stroma, irregular clustering of epithelial nuclei, and greater variation in lumen shape. Conclusion: The present findings show that a computer vision approach can detect subtle histological effects attributable to dutasteride, resulting in a continuous measure of responsiveness to the drug that could eventually be used to predict individual patient response in the context of BPH treatment or cancer chemoprevention.