Artificial intelligence-assisted prostate cancer diagnosis for reduced use of immunohistochemistry.

ABSTRACT:

Background

Prostate cancer diagnosis heavily relies on histopathological evaluation, which is subject to variability. While immunohistochemical staining (IHC) assists in distinguishing benign from malignant tissue, it increases workload, costs, and leads to diagnostic delays. Artificial intelligence (AI) presents a promising solution to reduce reliance on IHC by accurately classifying atypical glands and borderline morphologies in hematoxylin and eosin (H&E) stained tissue sections.

Methods

In this study, we evaluated an AI model's ability to minimize IHC use without compromising diagnostic accuracy. We retrospectively analyzed prostate core needle biopsies from routine diagnostics at three different pathology sites. These cohorts consisted exclusively of diagnostically chal

SUBMITTER: Blilie A 

PROVIDER: S-EPMC12528698 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature