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An Immunohistochemical Algorithm for Ovarian Carcinoma Typing.

Martin Köbel1, Kurosh Rahimi, Peter F Rambau

  • 1Calgary Laboratory Services/Alberta Health Services (M.K., P.F.R., C.N., S.L.), Department of Pathology and Laboratory Medicine, Foothill Medical Center, University of Calgary, Calgary Department of Laboratory Medicine and Pathology (X.L., C.A.E., C.-H.L.), University of Alberta, Edmonton, AB Centre de recherche du (K.R., C.L.P., L.M., M.d.L., D.P., A.-M.M.M.), Centre hospitalier de l'Université de Montréal (CRCHUM) Departments of Pathology (K.R.) Obstetric-Gynecology (D.P.) Medicine (A.-M.M.M.), Université de Montréal Institut du cancer de Montréal (C.L.P., L.M., M.d.L., D.P., A.-M.M.M.), Montreal, QC Department of Pathology (S.L., D.H., C.B.G.), University of British Columbia, Vancouver, BC Department of Cellular and Molecular Medicine (B.V.), University of Ottawa, ON, Canada Department of Pathology (P.F.R.), Catholic University of Health and Allied Sciences-Bugando, Mwanza, Tanzania Department of Health Sciences Research (E.L.G.), Mayo Clinic, Rochester, Minnesota Department of Preventive Medicine (S.J.R.), Keck School of Medicine, USC/Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California Department of Pathology and Cytology (J.W.C.), Institution for Oncology-Pathology, Karolinska University Hospital, Stockholm, Sweden Department of Public Health Sciences (L.E.K.), College of Medicine, Medical University of South Carolina Hollings Cancer Center (L.E.K.), Medical University of South Carolina, Charleston, South Carolina.

International Journal of Gynecological Pathology : Official Journal of the International Society of Gynecological Pathologists
|March 15, 2016
PubMed
Summary

This study developed accurate immunohistochemical (IHC) algorithms to reclassify ovarian carcinoma histotypes, improving diagnostic accuracy for research and clinical practice.

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Area of Science:

  • Gynecologic Pathology
  • Oncology
  • Biomarker Development

Background:

  • Ovarian carcinoma histotyping has evolved, potentially leading to misclassification in older studies.
  • Accurate histotyping is crucial for understanding disease behavior and treatment response.

Purpose of the Study:

  • To reclassify ovarian carcinoma histotypes using immunohistochemical (IHC) biomarkers.
  • To develop and validate IHC-based algorithms for ovarian carcinoma histotyping.

Main Methods:

  • Reclassification of 1626 ovarian carcinoma samples using 8 IHC markers and logistic regression modeling.
  • Development of prediction models with varying numbers of IHC markers (4, 6, and 8).
  • Arbitration for discordant cases and validation using mutational data and outcomes.

Main Results:

  • Histologic type was confirmed in 93.5% of cases after reclassification.
  • The endometrioid type showed the highest misclassification, often reclassified as high-grade serous carcinoma.
  • IHC algorithms achieved high classification accuracy: 88% (4 markers), 91% (6 markers), and 93% (8 markers).

Conclusions:

  • Statistically validated, inexpensive IHC algorithms can accurately determine ovarian carcinoma histotype.
  • These algorithms offer versatile applications in research, clinical practice, and clinical trials.
  • Improved histotyping enhances the reliability of past and future ovarian cancer studies.