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Peer-reviewedCC BY 4.0Scientific Data

Ovarian Stainology: Database of evidence-based immunohistochemical antigen expression in ovarian tumors

  • Jan H. Gruszczyński1,2
  • Kacper Trębacz3
  • Emilian Małek4
  • Michał J. Dopierała5
  • Aleksander Gostyński-Glina6
  • Patryk Kraiński7
  • Hanna Pelant2
  • Miłosz Kadziński1
7 affiliations
  1. 1Institute of Computing Science, Poznan University of Technology, Poznan, Poland
  2. 2Faculty of Medicine, Poznan University of Medical Sciences, Poznan, Poland
  3. 3Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
  4. 4Department of Pathology and Wildlife Diseases, Swedish Veterinary Agency, Uppsala, Sweden
  5. 5Department of Oncological Pathology, University Clinical Hospital, Poznań University of Medical Sciences, Poznan, Poland
  6. 6Faculty of Computing and Telecommunications, Poznan University of Technology, Poznan, Poland
  7. 7Department of Clinical Pathology and Immunology, Poznan University of Medical Sciences, Poznan, Poland
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Abstract

Immunohistochemical (IHC) profiling is essential for ovarian tumor subtyping, but comprehensive data on antigen and protein expression frequencies is fragmented across the literature. We developed a structured, harmonized database of IHC expression profiles in ovarian tumors extracted from published studies. The dataset was constructed using large language models to identify and extract relevant data from abstracts and tables from systematically retrieved publications. Screening of 5,961 studies identified 100,149 raw tumor-stain pairs, which were processed through a multi-step curation pipeline to filter, standardize, and harmonize the records. The final dataset comprises 12,212 tumor–stain frequency records derived from 1,450 studies, with all tumor nomenclature standardized to the 2020 WHO Classification of tumors. Records include details such as expression type, total sample counts, expression percentages, subcellular localization and expression distribution. The resource provides a consolidated, evidence-based reference to support research, analyses, and the development of computational models in diagnostic pathology.

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Cite

BibTeXRIS
BibTeX
@article{gruszczynski2026ovarian,
  title = {{Ovarian Stainology: Database of evidence-based immunohistochemical antigen expression in ovarian tumors}},
  author = {Jan H. Gruszczyński and Kacper Trębacz and Emilian Małek and Michał J. Dopierała and Aleksander Gostyński-Glina and Patryk Kraiński and Hanna Pelant and Miłosz Kadziński},
  journal = {Scientific Data},
  year = {2026},
  month = jun,
  doi = {10.1038/s41597-026-07776-1},
  url = {https://doi.org/10.1038/s41597-026-07776-1},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Ovarian Stainology: Database of evidence-based immunohistochemical antigen expression in ovarian tumors
AU  - Jan H. Gruszczyński
AU  - Kacper Trębacz
AU  - Emilian Małek
AU  - Michał J. Dopierała
AU  - Aleksander Gostyński-Glina
AU  - Patryk Kraiński
AU  - Hanna Pelant
AU  - Miłosz Kadziński
PY  - 2026
DA  - 2026/06/30
JO  - Scientific Data
DO  - 10.1038/s41597-026-07776-1
UR  - https://doi.org/10.1038/s41597-026-07776-1
AB  - Immunohistochemical (IHC) profiling is essential for ovarian tumor subtyping, but comprehensive data on antigen and protein expression frequencies is fragmented across the literature. We developed a structured, harmonized database of IHC expression profiles in ovarian tumors extracted from published studies. The dataset was constructed using large language models to identify and extract relevant data from abstracts and tables from systematically retrieved publications. Screening of 5,961 studies identified 100,149 raw tumor-stain pairs, which were processed through a multi-step curation pipeline to filter, standardize, and harmonize the records. The final dataset comprises 12,212 tumor–stain frequency records derived from 1,450 studies, with all tumor nomenclature standardized to the 2020 WHO Classification of tumors. Records include details such as expression type, total sample counts, expression percentages, subcellular localization and expression distribution. The resource provides a consolidated, evidence-based reference to support research, analyses, and the development of computational models in diagnostic pathology.
SN  - 2052-4463
ER  -