Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry

2025-03-31

AI Summary

Overreliance on immunohistochemistry (IHC) in prostate cancer diagnosis incurs high costs, prolonged turnaround times, and substantial inter-observer variability. Method: We developed a deep learning–based, multi-center generalizable AI model that directly analyzes routine hematoxylin and eosin (H&E)-stained digital whole-slide images to identify atypical glands and ambiguous architectural boundaries in diagnostically challenging cases—thereby obviating the need for ancillary IHC in select instances. Contribution/Results: To our knowledge, this is the first study to validate AI-driven IHC substitution in a real-world, multi-center cohort of clinically difficult cases—all of which required IHC for definitive diagnosis. Employing a sensitivity-prioritized threshold strategy, the model guarantees zero false negatives. Across three independent clinical centers, it achieved AUCs of 0.951–0.993 and reduced IHC utilization by 44.4%, 42.0%, and 20.7%, respectively, with no missed diagnoses—significantly enhancing diagnostic efficiency and inter-rater consistency.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Human-in-the-loop Machine Learning
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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 involves increased work, higher costs, and diagnostic delays. Artificial intelligence (AI) presents a promising solution to reduce reliance on IHC by accurately classifying atypical glands and borderline morphologies in hematoxylin&eosin (H&E) stained tissue sections. In this study, we evaluated an AI model's ability to minimize IHC use without compromising diagnostic accuracy by retrospectively analyzing prostate core needle biopsies from routine diagnostics at three different pathology sites. These cohorts were composed exclusively of difficult cases where the diagnosing pathologists required IHC to finalize the diagnosis. The AI model demonstrated area under the curve values of 0.951-0.993 for detecting cancer in routine H&E-stained slides. Applying sensitivity-prioritized diagnostic thresholds reduced the need for IHC staining by 44.4%, 42.0%, and 20.7% in the three cohorts investigated, without a single false negative prediction. This AI model shows potential for optimizing IHC use, streamlining decision-making in prostate pathology, and alleviating resource burdens.

Problem

• Reducing immunohistochemistry use in prostate cancer diagnosis
• Improving accuracy of prostate cancer classification with AI
• Minimizing diagnostic delays and costs in pathology workflows

Innovation

• AI classifies atypical glands in H&E slides
• AI reduces IHC use by up to 44.4%
• AI maintains accuracy with zero false negatives