ReviewJournal of personalized medicine2021
Biomarkers in Ovarian Pathology: From Screening to Diagnosis. Review of the Literature.
Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Review
- Association of the Single Nucleotide Polymorphisms rs11556218, rs4778889, rs4072111, and rs1131445 of the Interleukin-16 Gene with Ovarian Cancer.International journal of molecular sciences · 2024Article
- Diagnostic Utility of Selected Matrix Metalloproteinases (MMP-2, MMP-3, MMP-11, MMP-26), HE4, CA125 and ROMA Algorithm in Diagnosis of Ovarian Cancer.International journal of molecular sciences · 2024Article
- Clinical Applicability of Tissue Polypeptide Antigen and CA-125 in Gynecological Malignancies.Biomedicines · 2023Article
- Circulating Biomarkers for Cancer Detection: Could Salivary microRNAs Be an Opportunity for Ovarian Cancer Diagnostics?Biomedicines · 2023Review
- Molecular Management of High-Grade Serous Ovarian Carcinoma.International journal of molecular sciences · 2022Review
- Review
- Combined small and large magnetic nanoparticle extraction and concentration from biofluids for non-toxic detection of biomarkers.Sensors & diagnostics · 2022Article
- Women's awareness of ovarian cancer risk factors and symptoms in Western Iran in 2020-2021.BMC women's health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer has a low incidence, but high mortality due to a habitual diagnosis in advanced cancer stages. Currently, used biomarkers have good sensitivity, but low specificity.
aimTo determine the usefulness of the biomarkers and algorithms used up to now in the screening, diagnosis, response to treatments and identification of recurrence in patients with ovarian masses. METHODOLOGY: Systematic search of publications in English in the Medline-PubMed database with the terms: "biomarkers", "tumour", "tumour biomarkers", "marker", "tumour marker", "ovarian cancer", "ovarian", "Neoplasms", "cancer", CA-125 Antigen; Human Epididymis-specific Protein E4; Risk of Malignancy Index (RMI); Risk of Ovarian Malignancy Algorithm (ROMA); Ovarian Neoplasms. Original articles, clinical trials, reviews, systematic reviews and meta-analyses, published between January 2000 and November 2020, were selected to determine the usefulness (among others) of CA 125 and HE4 antigen in ovarian cancer.
resultsFinally, 39 transcendental publications were selected to write this article to determine the usefulness of tumour markers and algorithms in ovarian cancer.
conclusionsThe usefulness of the tumour markers antigen CA125 and antigen HE4 individually or as a basis for decision-making algorithms has low specificity; however, there is little evidence that confirms their usefulness as markers in ovarian cancer screening.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.