Literature DB >> 33636930

Algorithms Used in Ovarian Cancer Detection: A Minireview on Current and Future Applications.

Vishaal Gupta1, Marcus Q Bernardini2.   

Abstract

BACKGROUND: Ovarian cancer is the 5th most common cause of cancer death among women in the US. Currently, there is no screening algorithm for asymptomatic women that has been shown to lower mortality rates. Screening is currently not recommended and has been shown to increase harm. Epithelial ovarian cancer (EOC) detection is reviewed, with a focus on high-grade serous, clear-cell, and endometrioid histotypes. CONTENT: A review of current literature surrounding tools used in detection of ovarian cancer will be presented. CA 125, HE4, risk of ovarian cancer algorithm (ROCA), risk of malignancy algorithm (ROMA), risk of malignancy (RMI), OVA1, and future potential biomarkers are reviewed.
SUMMARY: Screening and early identification of EOC is currently managed as a single disease entity. However, recent evidence has shown ovarian cancer varies with relation to cellular origin, pathogenesis, molecular alterations, and prognosis, depending on histotype. There is a clear need for future studies identifying histotype-specific preclinical tumor markers to aid in detection and improvement of survival rates.
© 2018 American Association for Clinical Chemistry.

Entities:  

Year:  2018        PMID: 33636930     DOI: 10.1373/jalm.2017.025817

Source DB:  PubMed          Journal:  J Appl Lab Med        ISSN: 2475-7241


  1 in total

1.  Diagnostic potential of nanoparticle aided assays for MUC16 and MUC1 glycovariants in ovarian cancer.

Authors:  Shruti Jain; Nimrah Nadeem; Benjamin Ulfenborg; Maria Mäkelä; Shamima Afrin Ruma; Joonas Terävä; Kaisa Huhtinen; Janne Leivo; Björg Kristjansdottir; Kim Pettersson; Karin Sundfeldt; Kamlesh Gidwani
Journal:  Int J Cancer       Date:  2022-05-25       Impact factor: 7.316

  1 in total

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