Early Detection of Ovarian Cancer.


Journal

Cold Spring Harbor perspectives in medicine
ISSN: 2157-1422
Titre abrégé: Cold Spring Harb Perspect Med
Pays: United States
ID NLM: 101571139

Informations de publication

Date de publication:
01 Nov 2023
Historique:
pmc-release: 01 11 2025
medline: 3 11 2023
pubmed: 22 8 2023
entrez: 21 8 2023
Statut: epublish

Résumé

The risk of death from ovarian cancer is highly associated with the clinical stage at diagnosis. Efforts to implement screening for ovarian cancer have been largely unsuccessful, due to the low prevalence of the disease in the general population and the heterogeneity of the various cancer types that fall under the ovarian cancer designation. A practical test for early detection will require both high sensitivity and high specificity to balance reducing the number of cancer deaths with minimizing surgical interventions for false positive screens. The technology must be cost-effective to deliver at scale, widely accessible, and relatively noninvasive. Most importantly, a successful early detection test must be effective not only at diagnosing ovarian cancer but also in reducing ovarian cancer deaths. Stepwise or multimodal approaches among the various areas under investigation will likely be required to make early detection a reality.

Identifiants

pubmed: 37604591
pii: cshperspect.a041337
doi: 10.1101/cshperspect.a041337
pmc: PMC10626263
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 Cold Spring Harbor Laboratory Press; all rights reserved.

Auteurs

Naoko Sasamoto (N)

Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.

Kevin M Elias (KM)

Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA kelias@bwh.harvard.edu.

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Classifications MeSH