Evidence map›Paper›PMID 39981201›Full record

ReviewUpsala journal of medical sciences2025

Novel diagnostics for improved treatment of gynecological cancer.

Ulf Gyllensten

Abstract readReview
In one paragraph

Review in Upsala journal of medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Ulf GyllenstenDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper summarizes the efforts to develop novel biomarkers for diagnosis and screening of the three main gynecological cancers, cervical, endometrial, and ovarian cancer, with an emphasis on research performed during the last 20 years in Uppsala. A cervical cancer screening program has existed in Sweden since 1966 using cytology as the primary test. Over the last two decades, research has provided the scientific base for a transition to self-sampling to improve convenience of the woman and achieve higher population coverage, and use of human papillomavirus as the primary test. Also, efficient prophylactic vaccines and more efficient treatment strategies of women with cervical dysplasia have been introduced. Together, these medical tools have the potential to eradicate cervical cancer by 2120, as envisaged by WHO. By contrast, efficient biomarkers for endometrial and ovarian cancer are still lacking. Through the use of high-throughput proteomics, we have identified novel plasma protein biomarkers to be used in the diagnosis of women with adnexal ovarian mass upon transvaginal ultrasound, and possibly also for early detection in population screening. Similarly, novel biomarkers for the diagnosis of endometrial cancer are being evaluated. To establish a population-based screening program requires careful cost-benefit analyses. One alternative would be to broaden the focus of the current cervical cancer screening program to include also the novel biomarkers for ovarian and endometrial cancer, and thereby achieve screening for all three gynecological cancers. A program that screens for all three diseases could increase motivation to participate and thereby population coverage.

Indexed as

Genital Neoplasms, FemaleBiomarkers, TumorEarly Detection of CancerEndometrial NeoplasmsFemaleHumansOvarian NeoplasmsProteomicsSwedenUterine Cervical NeoplasmsBiomarkers, Tumorbiomarkerscervical cancerendometrial cancerGynecological cancerovarian cancerproteomics

Identifiers

PMID39981201
PMCPMC11836773

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.