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

Background: Progesterone receptor (PR) status plays an important role in guiding hormone therapy decisions in breast cancer. In current practice, PR expression is assessed manually from immunohistochemistry (IHC) slides, which can be time-consuming and may vary between pathologists. This study aims to develop an automated and interpretable framework for PR-IHC analysis to improve consistency and efficiency. Methods: In this work, we developed an AI-assisted pipeline that combines nuclei segmentation, classification, and scoring for PR-IHC images. A fine-tuned Cellpose model was used to segment individual nuclei. The segmented nuclei were then analyzed using a DAB intensity-based approach to classify them into four categories: negative, weak, moderate, and strong. These results were further combined to generate Allred scores. The system was evaluated on 250 PR-IHC images with annotations provided by expert pathologists. Results: The framework achieved strong segmentation performance (F1-score = 0.85, IoU = 0.74) and high classification accuracy (macro F1-score = 0.95). The method also performed well when applied to ER-IHC images without additional retraining. Conclusions: The proposed framework provides a reliable and interpretable approach for automated PR-IHC scoring. It helps reduce manual effort, improves consistency in evaluation, and shows potential for practical use in digital pathology settings.