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 for this work

Research output: Contribution to journal › Data article › peer-review

20 Citations (Scopus)

50 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.

Original language	English
Article number	562
Number of pages	6
Journal	Scientific Data
Volume	10
DOIs	https://doi.org/10.1038/s41597-023-02422-6
Publication status	Published - Aug 2023
Publication type	A1 Journal article-refereed

Funding

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.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Statistics and Probability
Information Systems
Education
Computer Science Applications
Statistics, Probability and Uncertainty
Library and Information Sciences

Access to Document

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

s41597-023-02422-6-1Final published version, 1.13 MBLicence: CC BY

https://urn.fi/URN:NBN:fi:tuni-202309158206Licence: CC BY

Cite this

Weitz, P., Valkonen, M., Solorzano, L., Carr, C., Kartasalo, K., Boissin, C., Koivukoski, S., Kuusela, A., Rasic, D., Feng, Y., Sinius Pouplier, 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, Article 562. https://doi.org/10.1038/s41597-023-02422-6

@article{0bcc305a04e84976b682da856e21fb47,

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.",

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

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: Vetenskapsr{\aa}det (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 H{\"a}gel{\'e}n Foundation. Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",

year = "2023",

month = aug,

doi = "10.1038/s41597-023-02422-6",

language = "English",

volume = "10",

journal = "Scientific Data",

issn = "2052-4463",

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}

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

TY - JOUR

T1 - A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics

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 - Sinius Pouplier, Sandra

AU - Sharma, Abhinav

AU - Ledesma Eriksson, Kajsa

AU - Latonen, Leena

AU - Laenkholm, Anne Vibeke

AU - Hartman, Johan

AU - Ruusuvuori, Pekka

AU - Rantalainen, Mattias

N1 - 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.

PY - 2023/8

Y1 - 2023/8

N2 - 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.

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.

U2 - 10.1038/s41597-023-02422-6

DO - 10.1038/s41597-023-02422-6

M3 - Data article

C2 - 37620357

AN - SCOPUS:85168702737

SN - 2052-4463

VL - 10

JO - Scientific Data

JF - Scientific Data

M1 - 562

ER -

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

Abstract

Funding

UN SDGs

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ASJC Scopus subject areas

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