Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review

• URL: http://arxiv.org/abs/2306.01546v2
• Date: Wed, 6 Dec 2023 09:43:41 GMT
• Title: Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review
• Authors: Masoud Tafavvoghi (1), Lars Ailo Bongo (2), Nikita Shvetsov (2), Lill-Tove Rasmussen Busund (3), Kajsa M{\o}llersen (1) ((1) Department of Community Medicine, UiT The Arctic University of Norway, Troms{\o}, Norway, (2) Department of Computer Science, UiT The Arctic University of Norway, Troms{\o}, Norway, (3) Department of Medical Biology, UiT The Arctic University of Norway, Troms{\o}, Norway)
• Abstract summary: We identified the publicly available datasets of breast H&E stained whole-slide images (WSI) that can be used to develop deep learning algorithms. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 (WSI) that can be used to develop deep learning algorithms. We systematically searched nine scientific literature databases and nine research data repositories and found 17 publicly available datasets containing 10385 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 two 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.

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