Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review
Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Pathology Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353924000026 |
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| author | Masoud Tafavvoghi Lars Ailo Bongo Nikita Shvetsov Lill-Tove Rasmussen Busund Kajsa Møllersen |
| author_facet | Masoud Tafavvoghi Lars Ailo Bongo Nikita Shvetsov Lill-Tove Rasmussen Busund Kajsa Møllersen |
| author_sort | Masoud Tafavvoghi |
| collection | DOAJ |
| description | Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata. |
| format | Article |
| id | doaj-art-1b42556ffece42f8b0e1ca0cb0bafd24 |
| institution | Kabale University |
| issn | 2153-3539 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-1b42556ffece42f8b0e1ca0cb0bafd242024-12-15T06:15:10ZengElsevierJournal of Pathology Informatics2153-35392024-12-0115100363Publicly available datasets of breast histopathology H&E whole-slide images: A scoping reviewMasoud Tafavvoghi0Lars Ailo Bongo1Nikita Shvetsov2Lill-Tove Rasmussen Busund3Kajsa Møllersen4Department 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 Computer Science, Uit The Arctic University of Norway, Tromsø, NorwayDepartment of Medical Biology, Uit The Arctic University of Norway, Tromsø, NorwayDepartment of Community Medicine, Uit The Arctic University of Norway, Tromsø, NorwayAdvancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.http://www.sciencedirect.com/science/article/pii/S2153353924000026Breast cancerComputational pathologyDeep learningWhole-slide imagesPublicly available datasets |
| spellingShingle | Masoud Tafavvoghi Lars Ailo Bongo Nikita Shvetsov Lill-Tove Rasmussen Busund Kajsa Møllersen Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review Journal of Pathology Informatics Breast cancer Computational pathology Deep learning Whole-slide images Publicly available datasets |
| title | Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review |
| title_full | Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review |
| title_fullStr | Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review |
| title_full_unstemmed | Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review |
| title_short | Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review |
| title_sort | publicly available datasets of breast histopathology h e whole slide images a scoping review |
| topic | Breast cancer Computational pathology Deep learning Whole-slide images Publicly available datasets |
| url | http://www.sciencedirect.com/science/article/pii/S2153353924000026 |
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