A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics

Philippe Weitz; Masi Valkonen; Leslie Solorzano; Circe Carr; Kimmo Kartasalo; Constance Boissin; Sonja Koivukoski; Aino Kuusela; Dusan Rasic; Yanbo Feng; Sandra Sinius Pouplier; Abhinav Sharma; Kajsa Ledesma Eriksson; Leena Latonen; Anne-Vibeke Laenkholm; Johan Hartman; Pekka Ruusuvuori; Mattias Rantalainen

doi:10.1038/s41597-023-02422-6

A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics

Philippe Weitz^*, Masi Valkonen, Leslie Solorzano, Circe Carr, Kimmo Kartasalo, Constance Boissin, Sonja Koivukoski, Aino Kuusela, Dusan Rasic, Yanbo Feng, Sandra Sinius Pouplier, Abhinav Sharma, Kajsa Ledesma Eriksson, Leena Latonen, Anne-Vibeke Laenkholm, Johan Hartman, Pekka Ruusuvuori, Mattias Rantalainen

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

5 Citationer (Scopus)

11 Downloads (Pure)

Abstract

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections from the same tumour. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients.

Originalsprog	Engelsk
Artikelnummer	562
Tidsskrift	Scientific Data
Vol/bind	10
Antal sider	6
ISSN	2052-4463
DOI	https://doi.org/10.1038/s41597-023-02422-6
Status	Udgivet - 2023

Bibliografisk note

Funding Information:
We acknowledge support from Stratipath and Karolinska Institutet sponsoring the ACROBAT challenge prize; MICCAI society for hosting the ACROBAT challenge, and Nguyen Thuy Duong Tran for support with digitising histopathology slides. We acknowledge funding from: Vetenskapsrådet (Swedish Research Council), Cancerfonden (Swedish Cancer Society), ERA PerMed (ERAPERMED2019-224-ABCAP), MedTechLabs, Swedish e-science Research Centre (SeRC), VINNOVA, SweLife, Academy of Finland (#341967, #334782, #335976, #334774), Cancer Foundation Finland, University of Turku Graduate School, Turku University Foundation, Oskar Huttunen Foundation, David and Astrid Hägelén Foundation.

Publisher Copyright:
© 2023, Springer Nature Limited.

Adgang til dokumentet

10.1038/s41597-023-02422-6Licens: CC BY

FulltextForlagets udgivne version, 1,13 MBLicens: CC BY

Citationsformater

Weitz, P., Valkonen, M., Solorzano, L., Carr, C., Kartasalo, K., Boissin, C., Koivukoski, S., Kuusela, A., Rasic, D., Feng, Y., Pouplier, S. S., Sharma, A., Ledesma Eriksson, K., Latonen, L., Laenkholm, A.-V., Hartman, J., Ruusuvuori, P., & Rantalainen, M. (2023). A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics. Scientific Data, 10, Artikel 562. https://doi.org/10.1038/s41597-023-02422-6

Weitz, P, Valkonen, M, Solorzano, L, Carr, C, Kartasalo, K, Boissin, C, Koivukoski, S, Kuusela, A, Rasic, D, Feng, Y, Pouplier, SS, Sharma, A, Ledesma Eriksson, K, Latonen, L, Laenkholm, A-V, Hartman, J, Ruusuvuori, P & Rantalainen, M 2023, 'A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics', Scientific Data, bind 10, 562. https://doi.org/10.1038/s41597-023-02422-6

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title = "A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics",

abstract = "The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections from the same tumour. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients.",

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AU - Weitz, Philippe

AU - Valkonen, Masi

AU - Solorzano, Leslie

AU - Carr, Circe

AU - Kartasalo, Kimmo

AU - Boissin, Constance

AU - Koivukoski, Sonja

AU - Kuusela, Aino

AU - Rasic, Dusan

AU - Feng, Yanbo

AU - Pouplier, Sandra Sinius

AU - Sharma, Abhinav

AU - Ledesma Eriksson, Kajsa

AU - Latonen, Leena

AU - Laenkholm, Anne-Vibeke

AU - Hartman, Johan

AU - Ruusuvuori, Pekka

AU - Rantalainen, Mattias

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AB - The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections from the same tumour. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients.

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