Evidence map›Paper›PMID 29725285›Full record

ArticlePrzeglad menopauzalny = Menopause review2018

Ovarian endometriosis: risk factor analysis and prediction of malignant transformation.

YanNa Zhou, Ke-Qin Hua

Open access · goldAbstract read
In one paragraph

Article in Przeglad menopauzalny = Menopause review, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
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

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Prediction models for endometriosis-associated ovarian malignancy: A systematic review and meta-analysis.European journal of obstetrics & gynecology and reproductive biology: X · 2026
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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

2 authors at 1 institution in 1 country.

YanNa ZhouJinShan Branch of Shanghai No.6 People's Hospital, China.
Ke-Qin HuaObstetrics and Gynecology Hospital of Fudan University, China.
Obstetrics and Gynecology Hospital of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionFor the prediction of endometriosis associated ovarian carcinoma (EAOC), the risk factors for malignant ovarian endometriosis (MOE) were explored. MATERIAL AND

methodsA group of 104 EAOC patients was compared with a group of 104 ovarian endometrial cyst patients. Using single and multivariate risk analysis of EAOC by calculating the area under the curve (AUC) of receiver-operator characteristics (ROC) curves, risks of MOE transformation were calculated for various burdens of risk factors.

resultsThe age range of 85 EAOC patients (81.73% of EAOC patients) was from 40 to 60 years old, as menopause occurs most frequently in this age range. From single factor ROC curve analysis, if disease duration/age/menopause/times of pregnancy/multiple foci of endometriosis index AUC were above 0.70, this suggested that the above indicators were predictive of MOE. Times of pregnancy/tumour size/myoma of uterus/multiple foci of endometriosis were taken as the independent risk factors of MOE. Using logistic regression, the AUC of 0.89 (95% confidence interval [CI]: 0.84-0.94) was statistically significant (

conclusionsTimes of pregnancy/BMI/irregular vaginal bleeding/thyroid disease/myoma of uterus/tumour serious fixation index indicate higher risk of MOE; age/tumour size/menopause/disease duration/ dysmenorrhea/multiple foci of endometriosis suggest lower risk of MOE. Therefore, in patients with endometriosis, malignant transformation could be predicted early.

Indexed as

endometriosis associated ovarian carcinomaovarian endometriosisprediction of malignant transformation

Identifiers

PMID29725285
PMCPMC5925200
OpenAlexW2798020807

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.