Evidence map›Paper›PMID 42318206›Full record

ArticleFrontiers in endocrinology2026

Identification of diagnostic blood indicators associated with adenomyosis: a retrospective cohort study.

Yuehong Tong, Jiarong Luo, Yiye Chi, Xinyuan Cui, Jianlin Zhang, Hui Wang, Fang Yao

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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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

7 authors.

Yuehong Tong *Department of Gynecology, Jinhua Maternal and Child Healthcare Hospital, Jinhua, Zhejiang, China.
Jiarong Luo *Institute of Maternal and Child Medicine Research, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, China.
Yiye Chi *Bachelor Program of Bioinformatics, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China.
Xinyuan CuiInstitute of Maternal and Child Medicine Research, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, China.
Jianlin ZhangInstitute of Maternal and Child Medicine Research, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, China.
Hui WangShenzhen Key Laboratory of Maternal and Child Health and Diseases, Shenzhen, Guangdong, China.
Fang YaoInstitute of Maternal and Child Medicine Research, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Adenomyosis (AM) is described as a benign invasion of the endometrium into the myometrium, which impacts a large number of childbearing age women. The diagnosis of AM relies on imaging and histological examinations. Although carbohydrate antigen 125 (CA125) has served as a blood indicator for AM diagnosis, its utility is limited to being effective in only approximately half of patients. Currently, there are no reliable blood diagnostic indicators available for AM. Methods: Data of 23 blood indicators examined for 143 patients with AM and 143 age-matched healthy women were collected, including six sex hormones, two tumor biomarkers, nine routine data, two inflammatory and coagulation indicators, and four lipid-related indicators. Wilcoxon rank-sum test was applied to identify differentially changed indicators (DCIs) for AM Results: We identified 15 DCIs for AM. Four DCIs were found in all three MRI subtypes. CA125 and estradiol could distinguish severe from mild AM. The hemoglobin (HGB) concentration, lymphocyte percentage, neutrophil count, neutrophil percentage, and testosterone were different between diffuse AM and adenomyoma. Based on these DCIs, neutrophil count, HGB concentration, and high-density lipoprotein (HDL) were selected using the LASSO and SVMRFE methods, which could discriminate AM cases with CA125<35 and ≥35 U/ml from the controls with AUCs of 0.812 and 0.928, respectively. Similarly, CA125, neutrophil count, HGB concentration, and HDL were screened and a diagnostic model built for AM, which could differentiate all AM cases from the controls with an AUC of 0.935 (sensitivity = 0.902, specificity = 0.888). Conclusion: To our knowledge, these indicators are reported here for the first time as combined biomarkers for the diagnosis of AM. Our findings might provide clues for the pathogenesis research of AM and supply potential blood indicators to assist in its diagnosis.

Indexed as

AdenomyosisBiomarkersAdultBiomarkers, TumorCA-125 AntigenCase-Control StudiesFemaleHumansMiddle AgedRetrospective StudiesROC CurveBiomarkersBiomarkers, TumorCA-125 Antigenadenomyosisblood indicatorsdiagnosislogistic regressionretrospective cohort study

Identifiers

PMID42318206
PMCPMC13272046

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