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Updated: May 14, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

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Published on: August 22, 2025

AI-Driven Breast Cancer Nuclei Segmentation, Classification, and Scoring in PR-IHC Images.

Hasanul Bannah¹, Mohammad Faizal Ahmad Fauzi^2,3, Sarina Mansor³

¹Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya 63100, Malaysia.

Diagnostics (Basel, Switzerland)

|May 13, 2026

Summary

An AI framework automates progesterone receptor (PR) scoring from immunohistochemistry (IHC) slides, enhancing consistency and efficiency in breast cancer diagnosis. This digital pathology tool reduces manual workload and improves evaluation reliability.

Keywords:

Allred scoring breast carcinoma deep learning digital pathology immunohistochemistry nuclei classification nuclei segmentation progesterone receptor

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Published on: April 12, 2024

Area of Science:

Digital Pathology
Computational Biology
Oncology

Background:

Progesterone receptor (PR) status is crucial for hormone therapy decisions in breast cancer.
Manual assessment of PR expression from immunohistochemistry (IHC) slides is time-consuming and prone to inter-pathologist variability.
Developing automated methods for PR-IHC analysis is essential for improving diagnostic consistency and efficiency.

Purpose of the Study:

To develop an automated and interpretable AI framework for progesterone receptor (PR) scoring in breast cancer IHC images.
To enhance the consistency and efficiency of PR expression assessment in digital pathology workflows.
To provide a reliable tool for pathologists, reducing manual effort and potential diagnostic variability.

Main Methods:

An AI-assisted pipeline was developed, integrating nuclei segmentation, classification, and scoring for PR-IHC images.
A fine-tuned Cellpose model performed nuclei segmentation, followed by DAB intensity-based classification into negative, weak, moderate, and strong categories.
The system generated Allred scores and was validated on 250 PR-IHC images against expert pathologist annotations.

Main Results:

The AI framework demonstrated strong performance in nuclei segmentation (F1-score = 0.85, IoU = 0.74).
High classification accuracy was achieved, with a macro F1-score of 0.95 for PR expression levels.
The method showed robustness, performing well on Estrogen Receptor (ER)-IHC images without retraining.

Conclusions:

The proposed framework offers a reliable and interpretable solution for automated PR-IHC scoring.
This AI tool significantly reduces manual assessment efforts and improves the consistency of PR evaluation.
The system holds considerable potential for practical implementation in digital pathology settings for breast cancer diagnosis.