The diagnostic value of immune-inflammatory markers in endometriosis

WANG Fei’e, ZHAO Qiaohong, YAN Xing
2025 · doi:10.12173/j.issn.1004-5511.202411125 · W6922566717
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This study evaluated the diagnostic value of immune-inflammatory markers for endometriosis by enrolling 6,570 women from a single hospital (2021–2024), classifying them into endometriosis and non-endometriosis groups, and comparing clinical data. Using univariate/multivariate logistic regression and Lasso regression with separate training (70%) and validation (30%) sets, the authors identified multiple candidate predictors and then built a final model using three variables: lymphocyte count, C-reactive protein, and tumor necrosis factor-α. The model showed moderate discrimination (AUC 0.814 in training; 0.786 in validation) with good calibration, and the authors report potential clinical benefit within certain decision-threshold ranges. The study’s main limitation is that it is based on a single-center cohort with data collected through one diagnostic criteria-based grouping, which may affect generalizability. This paper is centrally about endometriosis—specifically constructing and validating an immune-inflammatory biomarker prediction model for diagnosis.

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Abstract

Objective To explore the diagnostic value of immune-inflammatory indicators in endometriosis (EMs) and to construct a prediction model.Methods Women who visited the department of health management medicine and the department of obstetrics and gynecology of the First People's Hospital of Foshan from January 2021 to June 2024 were selected as research subjects. They were divided into EMs group and Non EMs group according to the EMs diagnostic criteria, and the clinical data of the two groups were compared. The subjects were randomly divided into a training set and a validation set at a ratio of 7 ∶ 3. Univariate and multivariate Logistic regression analysis and Lasso regression were used to screen predictive variables, and a prediction model was constructed. The receiver operating characteristic curve (ROC) and its area under the curve (AUC), calibration curve, and decision curve were used to evaluate the prediction efficiency of the model.Results A total of 6,570 subjects were included, including 6,090 in the Non EMs group and 480 in the EMs group. There were 4,599 cases in the training set and 1,971 cases in the validation set. Multivariate logistic regression analysis showed that white blood cell count [OR=2.997, 95%CI (1.165, 7.709)], lymphocyte count [OR=6.625, 95%CI (3.436, 12.774)], neutrophil count [OR=3.248, 95%CI (1.082, 9.753)], monocyte count [OR=4.269, 95%CI (1.291, 14.111)], C-reactive protein [OR=7.226, 95%CI (1.840, 28.382)], carbohydrate antigen 125 [OR=1.603, 95%CI (1.112, 2.311)], Carbohydrate antigen 19-9 [OR=1.470, 95%CI (1.091, 1.980)], interleukin-18 [OR=6.251, 95%CI (1.698, 23.015)], and tumor necrosis factor-α [OR=4.435, 95%CI (1.619, 12.146)] may be the influencing factors of EMs. The prediction model finally included three variables, lymphocyte count, C-reactive protein, and tumor necrosis factor-α. ROC analysis showed that the sensitivity of the model in the training set was 78.18%, the specificity was 75.94%, and the AUC was 0.814. The sensitivity of the model in the validation set was 80.95%, the specificity was 66.29%, and the AUC was 0.786. The calibration curve showed that the predicted probability fit well with the actual probability. The clinical decision curve showed that the model could bring clinical benefits within a certain threshold range.Conclusion The prediction model constructed with three immune-inflammatory indicators of lymphocyte count, C-reactive protein and tumor necrosis factor-α has good diagnostic efficacy and can provide a reference for the early diagnosis of EMs.
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Yixue xinzhi zazhi (Jul 2025) The diagnostic value of immune-inflammatory markers in endometriosis Abstract Objective To explore the diagnostic value of immune-inflammatory indicators in endometriosis (EMs) and to construct a prediction model.Methods Women who visited the department of health management medicine and the department of obstetrics and gynecology of the First People's Hospital of Foshan from January 2021 to June 2024 were selected as research subjects. They were divided into EMs group and Non EMs group according to the EMs diagnostic criteria, and the clinical data of the two groups were compared. The subjects were randomly divided into a training set and a validation set at a ratio of 7 ∶ 3. Univariate and multivariate Logistic regression analysis and Lasso regression were used to screen predictive variables, and a prediction model was constructed. The receiver operating characteristic curve (ROC) and its area under the curve (AUC), calibration curve, and decision curve were used to evaluate the prediction efficiency of the model.Results A total of 6,570 subjects were included, including 6,090 in the Non EMs group and 480 in the EMs group. There were 4,599 cases in the training set and 1,971 cases in the validation set. Multivariate logistic regression analysis showed that white blood cell count [OR=2.997, 95%CI (1.165, 7.709)], lymphocyte count [OR=6.625, 95%CI (3.436, 12.774)], neutrophil count [OR=3.248, 95%CI (1.082, 9.753)], monocyte count [OR=4.269, 95%CI (1.291, 14.111)], C-reactive protein [OR=7.226, 95%CI (1.840, 28.382)], carbohydrate antigen 125 [OR=1.603, 95%CI (1.112, 2.311)], Carbohydrate antigen 19-9 [OR=1.470, 95%CI (1.091, 1.980)], interleukin-18 [OR=6.251, 95%CI (1.698, 23.015)], and tumor necrosis factor-α [OR=4.435, 95%CI (1.619, 12.146)] may be the influencing factors of EMs. The prediction model finally included three variables, lymphocyte count, C-reactive protein, and tumor necrosis factor-α. ROC analysis showed that the sensitivity of the model in the training set was 78.18%, the specificity was 75.94%, and the AUC was 0.814. The sensitivity of the model in the validation set was 80.95%, the specificity was 66.29%, and the AUC was 0.786. The calibration curve showed that the predicted probability fit well with the actual probability. The clinical decision curve showed that the model could bring clinical benefits within a certain threshold range.Conclusion The prediction model constructed with three immune-inflammatory indicators of lymphocyte count, C-reactive protein and tumor necrosis factor-α has good diagnostic efficacy and can provide a reference for the early diagnosis of EMs. Keywords

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