Evaluation of inflammatory serum parameters as a diagnostic tool in patients with endometriosis: a case-control study

Mariz Kasoha, P Sklavounos, Panagiotis Sklavounos, István Molnár, Istvan Molnar, Meletios P Nigdelis, Bashar Haj Hamoud, Erich‐Franz Solomayer, Erich-Franz Solomayer, Gilbert Georg Klamminger
Scientific reports · 2025 · vol. 15(1) , pp. 20172 · doi:10.1038/s41598-025-05719-1 · PMID:40542028 · W4411494788
article OA: gold CC0 ⤵ 1 in-corpus citation
📄 Open PDF View on OpenAlex View on PubMed View at publisher
⚙ AI-generated summary by claude@2026-06, 2026-06-09 ⓘ

This case-control study assessed inflammatory serum markers hepcidin, suPar, and IL-6 in 87 patients and found suPar and IL-6 have diagnostic potential for endometriosis, achieving 80% accuracy with a machine learning classifier.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by claude@2026-06, 2026-06-09 ⓘ

This case-control study evaluated whether serum inflammatory markers hepcidin, suPAR, and IL-6 could non-invasively diagnose endometriosis in 87 women, comparing 59 histologically confirmed endometriosis cases with 28 non-endometriosis controls, while also examining correlations with severity using rASRM stage. Univariate analysis found significantly different serum levels of IL-6 and suPAR between groups (IL-6 p < 0.001; suPAR p = 0.024), whereas hepcidin, CRP, ferritin, and soluble transferrin receptor did not differ; these data were then used to train a supervised decision-tree classifier with 77 samples and evaluate it by internal 5-fold cross-validation and external holdout validation (n=10), reporting 80% overall accuracy without evidence of overfitting. The authors explicitly note that the machine-learning analysis was exploratory/post-hoc. This paper is centrally about endometriosis — it tests serum inflammatory biomarkers (especially IL-6 and suPAR) and a decision-tree model for non-surgical diagnosis of endometriosis.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Even though non-invasive prediction of endometriosis may seem technically feasible using sophisticated machine learning algorithms, a standard clinical use case for non-surgical diagnosis of endometriosis has not yet been established. In the present paper, we assess the potential of the inflammatory serum markers hepcidin, soluble urokinase-type plasminogen activator receptor (suPar), and interleukin-6 (IL-6) in a cohort of 87 patients. Hereby, 59 patients were histologically diagnosed with endometriosis, whereas other 28 patients served as our non-endometriosis control group. An initial exploratory univariate statistical analysis (Mann-Whitney test) revealed the diagnostic potential of different serum levels of suPar (p = 0.024) and IL-6 (p < 0.001) between both groups; the formation of a distinct training data set (n = 77) subsequently allowed to train a supervised machine learning analysis (tree classifier) employing serum levels of suPar, hepcidin, and IL-6 as predictor variables. Based on an internal 5-fold cross validation, the classifier performance was initially assessed using standard metrics such as sensitivity, positive predictive value, and AUROC curve. Additionally, the algorithm was tested on an external validation (holdout) data set (n = 10), showing sufficient overall accuracy of 80% without tendencies of overfitting. In conclusion, our data demonstrates the diagnostic potential of IL-6 and suPar as pro-inflammatory serum biomarkers in endometriosis. Using a decision tree-based supervised learning approach, we additionally present a straight-forward way of a potential clinical employment, aiming at less invasive (non-surgical) diagnosis.

My notes (saved in your browser only)

Outcome instruments

rASRM

Condition tags

mesh:D004715endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (30)

Cited by (1)

Source provenance

europepmc
last seen: 2026-06-04T01:30:01.192114+00:00
openalex
last seen: 2026-06-10T17:14:06.276822+00:00
pmc
last seen: 2026-05-13T20:22:03.195721+00:00
pubmed
last seen: 2026-05-28T00:31:17.763179+00:00
License: CC0 · commercial use OK