JOURNAL ARTICLE

Breast cancer characteristics and pathological prognostic determinants in indigenous Australians: Retrospective cohort study in the Northern Territory.

  • Published In: Asia Pacific Journal of Clinical Oncology, 2024, v. 20, n. 5. P. 597 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Mencel, Justin; Hong, Hooi Wen; Charakidis, Michail; Pokorny, Adrian; Aldridge, Emily; Karanth, Narayan 3 of 3

Abstract

Background: There is a disparity in health outcomes between indigenous and nonindigenous Australians, with higher chronic disease burden and shorter life expectancy in this minority population. Although rates of breast cancer among indigenous women are lower than nonindigenous women, they face a higher breast cancer‐associated mortality, which may not entirely be explained by socio‐economic disadvantage. Methods: This retrospective cohort study investigated previously described pathologic prognostic factors in indigenous Australians in the Northern Territory. Results: Data analyzed confirmed that indigenous women were more likely to have poorer prognostic disease features, including ER/PR negative and human epidermal growth factor receptor 2 amplified tumors, larger tumors, and higher stage disease. Conclusion: These pathologic features portend to a poor prognosis, raising the possibility these factors contribute to the disparity in health outcomes between indigenous and nonindigenous women with breast cancer, in addition to known socio‐economic factors. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Asia Pacific Journal of Clinical Oncology. 2024/10, Vol. 20, Issue 5, p597
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:1743-7555
  • DOI:10.1111/ajco.13956
  • Accession Number:180925936
  • Copyright Statement:Copyright of Asia Pacific Journal of Clinical Oncology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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