Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images
Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Prev...
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Elsevier
2025-01-01
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| Series: | Journal of Pathology Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S215335392400049X |
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| author | Masoud Tafavvoghi Anders Sildnes Mehrdad Rakaee Nikita Shvetsov Lars Ailo Bongo Lill-Tove Rasmussen Busund Kajsa Møllersen |
| author_facet | Masoud Tafavvoghi Anders Sildnes Mehrdad Rakaee Nikita Shvetsov Lars Ailo Bongo Lill-Tove Rasmussen Busund Kajsa Møllersen |
| author_sort | Masoud Tafavvoghi |
| collection | DOAJ |
| description | Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting model. The pipeline was tested on 221 hold-out WSIs, achieving an F1 score of 0.95 for tumor vs non-tumor classification and a macro F1 score of 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development. |
| format | Article |
| id | doaj-art-93005a4b95ea4af69bc6c5805c67938c |
| institution | DOAJ |
| issn | 2153-3539 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-93005a4b95ea4af69bc6c5805c67938c2025-08-20T03:04:51ZengElsevierJournal of Pathology Informatics2153-35392025-01-011610041010.1016/j.jpi.2024.100410Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide imagesMasoud Tafavvoghi0Anders Sildnes1Mehrdad Rakaee2Nikita Shvetsov3Lars Ailo Bongo4Lill-Tove Rasmussen Busund5Kajsa Møllersen6Department of Community Medicine, Uit The Arctic University of Norway, Tromsø, Norway; Corresponding author.Department of Computer Science, Uit The Arctic University of Norway, Tromsø, NorwayDepartment of Medical Biology, Uit The Arctic University of Norway, Tromsø, Norway; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Cancer Genetics, Oslo University Hospital, Oslo, NorwayDepartment of Computer Science, Uit The Arctic University of Norway, Tromsø, NorwayDepartment of Computer Science, Uit The Arctic University of Norway, Tromsø, NorwayDepartment of Medical Biology, Uit The Arctic University of Norway, Tromsø, Norway; Department of Clinical Pathology, University Hospital of North Norway, Tromsø, NorwayDepartment of Community Medicine, Uit The Arctic University of Norway, Tromsø, NorwayClassifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting model. The pipeline was tested on 221 hold-out WSIs, achieving an F1 score of 0.95 for tumor vs non-tumor classification and a macro F1 score of 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development.http://www.sciencedirect.com/science/article/pii/S215335392400049XBreast cancerClassificationDeep learningMolecular subtypesWhole-slide images |
| spellingShingle | Masoud Tafavvoghi Anders Sildnes Mehrdad Rakaee Nikita Shvetsov Lars Ailo Bongo Lill-Tove Rasmussen Busund Kajsa Møllersen Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images Journal of Pathology Informatics Breast cancer Classification Deep learning Molecular subtypes Whole-slide images |
| title | Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images |
| title_full | Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images |
| title_fullStr | Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images |
| title_full_unstemmed | Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images |
| title_short | Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images |
| title_sort | deep learning based classification of breast cancer molecular subtypes from h e whole slide images |
| topic | Breast cancer Classification Deep learning Molecular subtypes Whole-slide images |
| url | http://www.sciencedirect.com/science/article/pii/S215335392400049X |
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