Validation of an Artificial Intelligence Model for Breast Cancer Molecular Subtyping Using Hematoxylin and Eosin-Stained Whole-Slide Images in a Population-Based Cohort
Abstract
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Immunohistochemistry
2.3. AI Model
2.3.1. Feature Extraction on the SBC Dataset
2.3.2. Retraining the Final Classifier
2.3.3. Performance Evaluation
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
AUC | Area Under the Curve |
BC | Breast Cancer |
BCNB | Breast Cancer Needle Biopsy |
CLF | Classification tasks |
CNN | Convolutional Neural Network |
CV | Cross-Validation |
ER | Estrogen Receptor |
H&E | Hematoxylin and Eosin |
HER2 | Human Epidermal Growth Factor Receptor 2 |
IHC | Immunohistochemistry |
ISH | In Situ Hybridization |
LAR | Luminal Androgen Receptor |
MAI | Mitotic Activity Index |
MIL | Multiple Instance Learning |
NBCG | Norwegian Breast cancer Group |
PAM50 | Prediction Analysis of Microarray 50 |
PR | Progesterone Receptor |
SBC | Stavanger Breast Cancer |
TILs | Tumor-Infiltrating Lymphocytes |
TME | Tumor Microenvironment |
TNBC | Triple-Negative Breast Cancer |
WSI | Whole Slide Image |
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ER | PR | HER2 | Proliferation | |
---|---|---|---|---|
Luminal A | positive | positive | negative | low |
Luminal B | positive | any | any | high |
HER2-positive | negative | negative | overexpressed | - |
TNBC | negative | negative | negative | - |
Classification Tasks | Description |
---|---|
Two-class task (2-CLF) | Non-TNBC TNBC |
Three-class task (3-CLF) | Luminals (Luminal A and B) HER2-positive TNBC |
Four-class task (4-CLF) | Luminal A Luminal B HER2-positive TNBC |
Task | AUC | Accuracy | F1 Score | Precision | Recall | Learning Rate | Weight Decay |
---|---|---|---|---|---|---|---|
2-CLF | 0.823 ± 0.040 | 0.833 ± 0.015 | 0.824 ± 0.017 | 0.823 ± 0.018 | 0.833 ± 0.015 | 0.01 | 1 × 10−6 |
3-CLF | 0.834 ± 0.037 | 0.795 ± 0.034 | 0.770 ± 0.046 | 0.761 ± 0.063 | 0.795 ± 0.034 | 0.01 | 0.01 |
4-CLF | 0.790 ± 0.026 | 0.642 ± 0.043 | 0.601 ± 0.045 | 0.587 ± 0.056 | 0.642 ± 0.043 | 0.01 | 1 × 10−6 |
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Kiraz, U.; Fernandez-Martin, C.; Rewcastle, E.; Gudlaugsson, E.G.; Skaland, I.; Naranjo, V.; Morales-Martinez, S.; Janssen, E.A.M. Validation of an Artificial Intelligence Model for Breast Cancer Molecular Subtyping Using Hematoxylin and Eosin-Stained Whole-Slide Images in a Population-Based Cohort. Cancers 2025, 17, 3234. https://doi.org/10.3390/cancers17193234
Kiraz U, Fernandez-Martin C, Rewcastle E, Gudlaugsson EG, Skaland I, Naranjo V, Morales-Martinez S, Janssen EAM. Validation of an Artificial Intelligence Model for Breast Cancer Molecular Subtyping Using Hematoxylin and Eosin-Stained Whole-Slide Images in a Population-Based Cohort. Cancers. 2025; 17(19):3234. https://doi.org/10.3390/cancers17193234
Chicago/Turabian StyleKiraz, Umay, Claudio Fernandez-Martin, Emma Rewcastle, Einar G. Gudlaugsson, Ivar Skaland, Valery Naranjo, Sandra Morales-Martinez, and Emiel A. M. Janssen. 2025. "Validation of an Artificial Intelligence Model for Breast Cancer Molecular Subtyping Using Hematoxylin and Eosin-Stained Whole-Slide Images in a Population-Based Cohort" Cancers 17, no. 19: 3234. https://doi.org/10.3390/cancers17193234
APA StyleKiraz, U., Fernandez-Martin, C., Rewcastle, E., Gudlaugsson, E. G., Skaland, I., Naranjo, V., Morales-Martinez, S., & Janssen, E. A. M. (2025). Validation of an Artificial Intelligence Model for Breast Cancer Molecular Subtyping Using Hematoxylin and Eosin-Stained Whole-Slide Images in a Population-Based Cohort. Cancers, 17(19), 3234. https://doi.org/10.3390/cancers17193234