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

arXiv: https://arxiv.org/abs/2504.00979 · HTML: https://mlsanity.com/paper/2504.00979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology](https://mlsanity.com/paper/2408.00738/) (2024)
- [Automated Gleason Grading of Prostate Biopsies using Deep Learning](https://mlsanity.com/paper/1907.07980/) (2019)
- [Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard](https://mlsanity.com/paper/1808.05883/) (2018)
- [Foundation Models -- A Panacea for Artificial Intelligence in Pathology?](https://mlsanity.com/paper/2502.21264/) (2025)
- [Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis](https://mlsanity.com/paper/2307.05519/) (2023)
- [Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading](https://mlsanity.com/paper/2012.05197/) (2020)

## Abstract (arXiv)

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.