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

Philippe Weitz1, Masi Valkonen2, Leslie Solorzano3

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. philippe.weitz@ki.se.

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|August 24, 2023
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Summary
This summary is machine-generated.

This study introduces the largest public dataset of whole slide images (WSIs) for breast cancer, featuring matched hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains from the same tumors. This resource supports advancements in computational pathology for biomarker analysis.

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Area of Science:

  • Pathology
  • Computational Biology
  • Oncology

Background:

  • Hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining of FFPE breast cancer tissue sections are crucial for pathological assessment.
  • IHC is widely used for biomarker status (ER, PGR, HER2, KI67) in diagnostics.
  • Computational pathology shows promise for biomarker assessment using whole slide images (WSIs).

Purpose of the Study:

  • To address the scarcity of public data for computational pathology in breast cancer research.
  • To create the largest publicly available dataset of matched H&E and IHC WSIs from primary breast cancer specimens.

Main Methods:

  • Collected FFPE tissue sections from surgical resections of female primary breast cancer patients.
  • Stained sections with H&E and various IHC markers.
  • Acquired whole slide images (WSIs) for all stained sections.
  • Matched WSIs from H&E and IHC stains from the same tumor tissue.

Main Results:

  • Published the largest public dataset to date containing matched H&E and IHC WSIs.
  • The dataset comprises 4,212 WSIs from 1,153 primary breast cancer patients.
  • This dataset represents a significant resource for developing and validating computational pathology algorithms.

Conclusions:

  • The newly released dataset significantly enhances the availability of high-quality, matched imaging data for breast cancer research.
  • Facilitates the development of advanced computational tools for more accurate and efficient biomarker analysis.
  • Aims to accelerate progress in precision medicine for breast cancer through improved image analysis techniques.