Computer vision detects subtle histological effects of dutasteride on benign prostate
Amit Sethi1,2, Lingdao Sha1,3, Neeraj Kumar1
1Department of Pathology, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
BJU International
|February 21, 2018
Summary
Computer vision accurately distinguishes dutasteride-treated prostate tissue from placebo by analyzing H&E images. This approach identifies histological features linked to 5α-reductase inhibitor (5ARI) therapy response.
Area of Science:
- Digital pathology
- Computational analysis of histological images
- Oncology and Urology research
Background:
- Distinguishing dutasteride-treated prostate tissue from placebo is crucial for understanding 5α-reductase inhibitor (5ARI) efficacy.
- Traditional histological assessment may lack the sensitivity to detect subtle treatment-related changes.
Purpose of the Study:
- To evaluate a computer vision approach for differentiating dutasteride-treated prostate biopsies from placebo.
- To identify specific histological features associated with varying degrees of responsiveness to 5ARI therapy.
Main Methods:
- Analysis of whole-slide images from 100 men in the REDUCE trial (dutasteride vs. placebo).
- Generation of 1,300 epithelial and stromal features using specialized software.
- Penalized logistic regression and cross-validation for feature selection in year-4 and year-2 biopsies.
Main Results:
- Pathologist consensus achieved chance-level discrimination.
- A 21-feature computer vision model demonstrated high accuracy (AUC 0.97 in year-4, 0.79 in year-2 biopsies).
- Key dutasteride-associated features included stromal uniformity, irregular epithelial nuclei clustering, and lumen shape variation.
Conclusions:
- Computer vision effectively detects subtle histological effects of dutasteride.
- This method provides a continuous measure of drug responsiveness.
- Potential applications include predicting patient response in BPH treatment and cancer chemoprevention.
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