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Enhanced Prognostication of Early Breast Cancer Outcomes Using Deep Learning on Merged Multistain and

Yifei Lin¹, Xingyu Li¹, Jelena Milovanović²

¹Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, 116 Street, Edmonton, Canada T6G 1H9.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|June 12, 2025

Summary

Deep learning accurately predicts breast cancer spread using histopathology images. Grayscale images with pan-cytokeratin staining achieved 94.4% accuracy, improving with augmented data to 100%.

Keywords:

ResNet breast cancer deep learning distant metastasis histopathology microscopy multicolor-depth multistain pan-cytokeratin prognosis

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Oncology
Computational Pathology
Medical Imaging

Background:

Accurate breast cancer prognosis is crucial for treatment selection and patient survival.
Traditional prognostic methods can be limited; novel approaches are needed.

Purpose of the Study:

To evaluate the efficacy of deep learning models combined with histopathology images for predicting breast cancer metastasis.
To compare the prognostic utility of different image formats (color, grayscale, binary) and staining techniques.

Main Methods:

Utilized deep learning (ResNet-50) on digitized tumor histopathology images.
Optimized immunostaining with AE1/AE3 pan-cytokeratin and hematoxylin and eosin (H&E).
Compared prognostic performance across color, grayscale, and binary image representations.

Main Results:

Grayscale images demonstrated superior prognostic accuracy compared to color or binary formats.
Grayscale pan-cytokeratin images achieved 94.4% accuracy (AUC=0.982); grayscale H&E images reached 85.7% accuracy (AUC=0.992).
Augmenting data and training with ResNet-50 resulted in 100% accuracy (AUC=1.0), with no data contamination between sets.

Conclusions:

Deep learning applied to histopathology images offers exceptional accuracy for breast cancer prognosis.
Grayscale image representation and specific staining protocols enhance predictive performance.
This approach shows robust generalization and potential for clinical application in early-stage breast cancer.