Clinical value of clinical symptoms, serum biomarkers, and coagulation parameters for ovarian endometrioma diagnosis

Our study retrospectively analyzed the clinical and laboratory data of 424 patients with ovarian endometrioma (EMs group) and 351 patients with benign non-endometriotic ovarian cysts (cyst group). As showed in Table  1 , the prevalence of dysmenorrhea in the EMs group was significantly higher (68.16% vs. 40.46%, P  < 0.001) than that in the cyst group. Additionally, the cyst size was larger in the EMs group (7.00 cm vs. 6.10 cm, P  < 0.001). In terms of serum tumor markers, the levels of CA125 and HE4 were significantly elevated in the EMs group, while there was no statistically significant difference in CA19-9 between the two groups. Compared to the cyst group, the EMs group exhibited significantly higher absolute values of WBC and neutrophils, a lower absolute lymphocyte count (all P  < 0.001), and elevated NLR and PLR ratios ( P  < 0.001). This inflammatory profile suggests a more prominent chronic inflammatory state in endometriosis. Regarding coagulation status, the PT in the EMs group was prolonged ( P  < 0.001), and the APTT was also prolonged ( P  < 0.001). Table 1 Comparison of clinical characteristics between endometriosis and non-endometriosis cysts Variable EMs group ( N  = 424) Cyst group ( N  = 351) Z/χ 2 P Age 29(26, 34) 30(25,37) −1.437 0.151 Dysmenorrhea 59.709 <0.001  Positive 289(68.16%) 142(40.46%)  Negative 135(31.84%) 209(59.54%) Cyst size (cm) 7.00 (6.00,8.70) 6.10 (4.90, 8.20) −5.309 <0.001 Tumor markers  CA125 * (U/mL) 55.90 (35.23, 95.88) 17.45 (11.90, 25.18) −17.626 <0.001  CA19−9 * (U/mL) 32.62 (11.37, 85.23) 31.30(25.57,37.20) −0.706 0.480  Human epididymis protein4(HE4) * (pmol/L) 31.40 (26.23, 38.10) 15.41 (6.86,42.83) −7.864 <0.001 Inflammatory indicators  White blood cell (WBC) (10 9 /L) 7.62 (5.92, 9.78) 6.57 (5.30,7.93) −6.070 <0.001  Neutrophils (Neu) (10 9 /L) 5.22 (3.59, 7.46) 3.96 (3.04, 5.12) −7.896 <0.001  Lymphocytes (10 9 /L) 1.57(1.28,1.92) 1.83 (1.48, 2.24) −6.315 <0.001  Neutrophil/lymphocyte ratio (NLR) 3.18(1.99, 5.60) 2.16 (1.60,3.12) −8.449 <0.001  Platelet/lymphocyte ratio (PLR) 165.97 (131.30, 215.05) 144.83 (116.50,182.72) −5.262 <0.001 Coagulation parameter  Platelets (PLT) (10 9 /L) 263.00 (223.25, 304.00) 266.00 (232.00,301.00) −0.974 0.330  Thrombin time (TT) (sec) 17.10 (16.60,17.80) 17.30(16.78,18.00) −2.387 0.017  Prothrombin time (PT) (sec) 13.70(13.10,14.40) 13.00(12.60,13.50) −11.294 <0.001  Activated partial thromboplastin time (APTT) (sec) 37.15 (35.40, 39.50) 35.40(33.30,38.20) −6.563 <0.001 EMs group endometriosis group, Cyst group non-endometriosis group * The numbers of subjects with available data were as follows: for CA125: 356 in the endometriosis group and 330 in the cyst group; for HE4: 356 in the endometriosis group and 330 in the cyst group; for CA19-9: 360 in the endometriosis group and 330 in the cyst group Comparison of clinical characteristics between endometriosis and non-endometriosis cysts EMs group endometriosis group, Cyst group non-endometriosis group * The numbers of subjects with available data were as follows: for CA125: 356 in the endometriosis group and 330 in the cyst group; for HE4: 356 in the endometriosis group and 330 in the cyst group; for CA19-9: 360 in the endometriosis group and 330 in the cyst group A multivariable logistic regression analysis was conducted to identify factors independently associated with endometriosis, adjusting for all variables presented in Table  2 . A history of dysmenorrhea was significantly correlated with an increased likelihood of developing endometriosis (OR = 1.977, 95% CI:1.227–3.186, P  = 0.005). Among the serum biomarkers, elevated CA125 levels (OR = 1.067, 95% CI:1.052–1.081, P  < 0.001) showed a strong positive association with endometriosis. In contrast, HE4 levels demonstrated a significant inverse relationship with the risk of endometriosis (OR = 0.988, 95% CI:0.981–0.995, P  < 0.001). Regarding coagulation parameters, prolonged prothrombin time (PT) (OR = 3.234, 95% CI:2.212–4.728, P  < 0.001) emerged as an independent factor associated with endometriosis. Table 2 Multivariate analyses of factors associated endometriosis Variable β Exp(B) OR(95%CI) P Dysmenorrhea 0.682 1.977 1.227–3.186 0.005 Cyst size −0.017 0.984 0.925–1.046 0.598 CA125 0.064 1.067 1.052–1.081 <0.001 HE4 −0.012 0.988 0.981–0.995 <0.001 White blood cell (WBC) −0.029 0.972 0.635–1.488 0.895 Neutrophils (Neu) 0.259 1.295 0.772–2.172 0.327 Lymphocytes −0.769 0.463 0.189–1.138 0.093 Neutrophil/lymphocyte ratio (NLR) −0.119 0.888 0.675–1.169 0.397 Platelet/lymphocyte ratio (PLR) −0.003 0.997 0.993–1.001 0.189 Thrombin time (TT) 0.027 1.028 0.812–1.167 0.768 Prothrombin time (PT) 1.174 3.234 2.212–4.728 <0.001 Activated partial thromboplastin time (APTT) 0.038 1.038 0.970–1.112 0.280 Multivariate analyses of factors associated endometriosis As illustrated in Table  3 , the Receiver Operating Characteristic (ROC) curve analysis was employed to assess the diagnostic efficacy of individual indicators. In the context of diagnosing endometriotic cysts, CA125 exhibited the highest diagnostic value, evidenced by an area under the curve (AUC) of 0.895 (95% CI:0.870–0.919). When a threshold of 27.45 U/mL was utilized, CA125 demonstrated a sensitivity of 85.4% and a specificity of 80.6% ( P  < 0.001). Conversely, the diagnostic efficacy of dysmenorrhea, HE4, and PT was found to be suboptimal. As presented in Fig.  1 , in comparison to the singular diagnostic value of CA125, the integration of multiple indicators (dysmenorrhea, CA125, HE4, and PT) enhanced diagnostic efficacy, with the AUC increasing to 0.925 (95% CI:0.904–0.945). Pairwise comparisons of the AUCs (Table  4 ) further confirmed that the combined diagnostic model significantly outperformed each individual marker (all P  < 0.05). The AUC of the combined model was 0.030 higher than that of CA125 alone ( P  = 0.001). Table 3 Sensitivity and specificity of dysmenorrhea, CA125, HE4, PT and combined marker in diagnosing endometriosis Parameters AUC (95% CI) Sensitivity (%) Specificity (%) Cutoff value P Dysmenorrhea 0.644 (0.602,0.686) 69.9 58.9 1 <0.001 CA125 0.895 (0.870,0.919) 85.4 80.6 27.45 <0.001 HE4 0.680 (0.633,0.728) 96.3 57.6 19.57 <0.001 Prothrombin time (PT) 0.745 (0.708,0.782) 68.3 68.3 13.35 <0.001 Combined marker 0.925(0.904,0.945) 88.0 84.3 / <0.001 Sensitivity and specificity of dysmenorrhea, CA125, HE4, PT and combined marker in diagnosing endometriosis Fig. 1 ROC analysis of dysmenorrhea, CA125, HE4, PT and combined marker in diagnosing endometriosis ROC analysis of dysmenorrhea, CA125, HE4, PT and combined marker in diagnosing endometriosis Table 4 Pairwise comparison of AUCs among diagnostic markers Comparison AUC Difference (95% CI) Z P Combined marker vs. CA125 −0.30 (−0.47,−0.12) −3.355 0.001 Combined marker vs. Dysmenorrhea −0.280 (−0.318,−0.243) −14.673 <0.001 Combined marker vs. HE4 −0.605 (−0.655,−0.555) −23.650 <0.001 Combined marker vs. PT −0.18 (−0.214,−0.146) −10.385 <0.001 Pairwise comparison of AUCs among diagnostic markers

In this study, a cohort of 424 patients diagnosed with ovarian endometrioma via histopathological examination at the Third Affiliated Hospital of Sun Yat-sen University between January 2024 and March 2025 was designated as the ovarian endometrioma group (EMs group). Concurrently, 351 cases of benign ovarian tumors, excluding endometriotic cysts, identified through histopathological examination during the same timeframe, were categorized as the non-endometriosis group (cyst group). The tumor types encompassed mature teratoma, serous cystadenoma, mucinous cystadenoma, simple cysts, and other benign ovarian tumors. The exclusion criteria for this study included: (1) patients with borderline or malignant ovarian tumors as determined by pathological results; and (2) patients with other malignant tumors. This study is a diagnostic trial. The sample size was estimated using the formula for diagnostic studies: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:n=\frac{{Z}^{2}\times\:p(1-p)}{{d}^{2}}$$\end{document} where Z = 1.96 (corresponding to a 95% confidence level), p represents the expected diagnostic accuracy (set at 0.85 based on prior literature), and d denotes the margin of error (set at 0.05). The minimum required sample size per group was calculated to be 196. Our study enrolled 424 patients in the endometrioma group and 351 in the benign cyst group, both of which exceeded the minimum sample size requirement, thereby ensuring adequate statistical power. This retrospective analysis received approval from the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University (II2025-179-01). The objective of this study is to assess potential biomarkers for diagnosing endometriosis and non-endometriosis cysts. Clinical and laboratory data were retrospectively collected from electronic medical records. Demographic information included age at data collection and dysmenorrhea status. Dysmenorrhea was assessed based on the patient’s self-reported chief complaint. The pain was cyclic and associated with menstruation, as reported during the preoperative clinical interview. Ovarian cyst size was determined by a transvaginal or transrectal ultrasound conducted by experienced sonographers. The reported cyst size corresponds to the maximum diameter measured in the largest cross-sectional area. Peripheral blood samples were collected within one week before surgery for laboratory analyses. Inflammatory markers—including white blood cell count (WBC), neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR)—and coagulation parameters—including platelet count (PLT), thrombin time (TT), prothrombin time (PT), and activated partial thromboplastin time (APTT)—were measured using blood samples obtained within one week before surgery. Serum tumor markers—CA125, CA19-9, and HE4—along with anti-Müllerian hormone (AMH), were analyzed using blood samples collected within one month before surgery. Data analysis was performed using SPSS version 27.0. Descriptive statistics, including frequencies, percentages, medians, and interquartile ranges, were utilized to summarize the dataset. The chi-square test was employed for the comparison of categorical variables such as dysmenorrhea status. Given that none of the continuous variables in this study adhered to a normal distribution, non-parametric tests were applied for intergroup comparisons. Receiver operating characteristic (ROC) analysis was conducted to determine the area under the curve (AUC), thereby evaluating the diagnostic sensitivity, specificity, and optimal cut-off point for each marker. The optimal cut-off value for each continuous marker, such as CA125, was established by maximizing the Youden index (sensitivity + specificity − 1) derived from the ROC curve. The DeLong test was used to statistically compare the AUCs between different diagnostic indicators or models. A p-value of less than 0.05 was considered indicative of statistical significance.

This study systematically evaluated the diagnostic value of clinical symptoms, tumor markers, inflammatory indicators, and coagulation parameters in endometriosis, and constructed a combined diagnostic model based on independent influencing factors. The key findings are as follows: (1) Dysmenorrhea, cyst size, CA125, HE4, NLR, PLR, PT, and APTT showed significant differences between the EMs group and the cyst group; (2) Multivariate analysis identified dysmenorrhea, CA125, HE4, and PT as independent factors associated with endometriosis; (3) The combined diagnostic model integrating these four indicators exhibited higher diagnostic efficacy (AUC = 0.925, 95% CI:0.904–0.945) than the single indicator CA125 (AUC = 0.895, 95% CI:0.870–0.919). The difference between these AUCs was statistically significant ( P  = 0.001). These results provide new insights into the non-invasive diagnosis of endometriosis.​ Consistent with previous studies [ 8 , 9 , 16 , 17 ], CA125 showed excellent diagnostic performance in this study, with an AUC of 0.895, a sensitivity of 85.4%, and a specificity of 80.6% at the optimal cut-off value of 27.45 U/mL. It is important to note that the odds ratio (OR) for CA125 in the multivariate model was 1.067 (per unit increase), indicating a modest independent association with endometriosis rather than a strong predictive effect [ 18 – 20 ], which limits its application as a single diagnostic marker. In this study, the diagnostic efficacy was significantly improved after combining CA125 with other indicators, confirming that the multi-marker strategy can make up for the deficiency of single markers. The choice of a 27.45 U/mL cutoff (lower than the conventional 35 U/mL) was driven by maximizing the Youden index in our cohort to optimize the trade-off between sensitivity and specificity. This underscores the context-dependent nature of biomarker thresholds and the challenge of defining a universally optimal cutoff for a non-specific marker like CA125.​ HE4, initially recognized as an ovarian cancer biomarker [ 21 , 22 ], has recently been explored in ovarian endometrioma diagnosis. Our univariate analysis showed significantly higher HE4 levels in the EMs group. However, in the multivariable model, HE4 showed a small inverse association with endometriosis (OR = 0.988). This apparent discrepancy likely reflects the adjustment for stronger correlated predictors (e.g., CA125 and PT) in the multivariate analysis, wherein the modest independent contribution of HE4 is attenuated. Consistent with its low AUC (0.680) and low specificity (57.6%) when used alone, HE4 appears unsuitable as a primary diagnostic marker but may provide marginal complementary information within a multi-parameter panel.​ Notably, this study confirmed the association between endometriosis and chronic inflammation from the perspective of inflammatory indicators. In the endometriosis (EMs) group, there was a significant elevation in white blood cells, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), aligning with findings from previous research [ 23 – 25 ]. Endometriosis is characterized by the ectopic implantation and proliferation of endometrial tissue, which can instigate local inflammatory responses, recruit neutrophils, and suppress lymphocyte function. Although inflammatory indicators did not enter the final combined model, they still provide important clues for understanding the pathogenesis of endometriosis.​ In terms of coagulation function, this study found that PT and APTT were prolonged in the EMs group, and PT was identified as an independent influencing factor. The prolonged PT may be related to the consumption of coagulation factors in the local microenvironment of endometriosis lesions, as repeated bleeding and tissue damage can activate the coagulation system and lead to the depletion of prothrombin and other factors. This finding expands the understanding of the pathophysiological characteristics of endometriosis and suggests that coagulation parameters may have potential diagnostic value.​ The combined diagnostic model (dysmenorrhea + CA125 + HE4 + PT) constructed in this study showed an AUC of 0.925, which was statistically superior to that of CA125 alone (AUC = 0.895). While the absolute increase in sensitivity (88.0% vs. 85.4%) and specificity (84.3% vs. 80.6%) appears modest, the integration leverages the complementary information from different pathophysiological domains (clinical symptomatology, tumor markers, coagulation). This strategy partially mitigates the limitations of any single marker. The model’s strength lies in its practicality: dysmenorrhea is assessed via routine inquiry, and CA125, HE4, and PT are commonly available laboratory tests, facilitating potential adoption in clinical settings.​ This study has several strengths. First, the sample size is relatively large, including 424 endometriosis patients and 351 control cases, which ensures the statistical reliability of the results. Second, the study comprehensively evaluated multiple types of indicators, covering clinical symptoms, tumor markers, inflammation, and coagulation, providing a more comprehensive perspective for the development of diagnostic models. However, in addition, this study has several limitations that should be acknowledged. First, as a single-center retrospective study, potential selection bias may exist, and our findings require validation through multi-center prospective investigations. Second, this study did not systematically collect or adjust for potential confounding factors such as comorbid medical conditions or the use of medications (e.g., hormonal therapies, anticoagulants, anti-inflammatory drugs) that could influence serum biomarker levels, inflammatory indices, or coagulation parameters. The potential impact of these unmeasured confounders on the performance of the diagnostic model cannot be excluded. Finally, the current model focuses on distinguishing ovarian endometrioma; further development and validation of models for non-cystic endometriosis are needed to enhance non-invasive early diagnosis and treatment. In conclusion, dysmenorrhea, CA125, HE4, and PT are independent factors associated with endometriosis. The non-invasive diagnostic model integrating dysmenorrhea, CA125, HE4, and PT demonstrates high clinical value in differentiating ovarian endometrioma from other benign ovarian masses. Its components are highly accessible in clinical practice—dysmenorrhea is obtainable via routine medical inquiry, and CA125, HE4, and PT are standard laboratory tests—enabling easy promotion in primary medical institutions. This model effectively addresses the limitations of invasive laparoscopy, providing a practical, reliable tool for early screening, diagnosis, and clinical management of ovarian endometrioma, which is expected to reduce unnecessary surgical interventions and improve the efficiency of clinical decision-making.

Endometriosis, defined as the ectopic presence of endometrial-like tissue outside the uterine cavity, affects 10–15% of reproductive-age women and is a leading cause of chronic pelvic pain and infertility [ 1 , 2 ]. Despite its significant clinical burden, the diagnosis of endometriosis remains challenging. The disease manifests with diverse phenotypes, among which ovarian endometriomas represent a common form, often detected during ultrasound or magnetic resonance imaging (MRI) examinations [ 3 , 4 ]. While imaging is invaluable for cyst identification, the differentiation of endometriomas from other benign non-endometriotic ovarian cysts (e.g., mature teratomas, serous or mucinous cystadenomas) can be challenging in some cases. This diagnostic ambiguity may lead to clinical indecision, delays in management, or potentially unnecessary surgical interventions. Therefore, developing complementary, non-invasive diagnostic tools to accurately distinguish ovarian endometriotic cysts from other benign cysts in cases of uncertain imaging findings holds direct clinical significance for preoperative decision-making. Although laparoscopy with histopathological confirmation has traditionally been considered a definitive diagnostic method [ 5 , 6 ], current guidelines recommend reserving it for cases with negative imaging results or when empirical treatment is unsuccessful or inappropriate [ 7 ]. Consequently, there is an urgent need for non-invasive biomarkers to facilitate early detection and clinical management. Serum biomarkers, particularly cancer antigen 125 (CA125), have been widely investigated for endometriosis diagnosis [ 8 , 9 ]. However, CA125 exhibits limited specificity, as elevated levels are also observed in other benign gynecological conditions (e.g., ovarian cysts, pelvic inflammatory disease) and malignancies [ 10 , 11 ]. Human epididymis protein 4 (HE4), initially identified as a biomarker for ovarian cancer, has recently emerged as a potential candidate for endometriosis diagnosis [ 12 ]. Beyond tumor markers, emerging evidence suggests that endometriosis is associated with chronic inflammation and altered coagulation profiles. Elevated inflammatory indices (e.g., neutrophil-to-lymphocyte ratio, NLR) and prolonged coagulation times (e.g., prothrombin time, PT) have been reported in endometriosis patients [ 13 – 15 ], yet their diagnostic value remains underexplored. To date, prior research has predominantly concentrated on single-marker approaches, resulting in a paucity of comprehensive evaluations of diagnostic models that integrate clinical symptoms, tumor markers, inflammatory indicators, and coagulation parameters. Therefore, this study aims to systematically evaluate the diagnostic performance of various candidate markers by conducting a retrospective analysis of clinical and laboratory data from patients with histopathologically confirmed ovarian endometriotic cysts and those with other types of benign ovarian cysts. We seek to develop a simplified, multi-dimensional diagnostic model. The objective of this model is to enhance clinicians’ ability to accurately differentiate ovarian endometriotic cysts from other benign ovarian masses preoperatively in patients where an ovarian cyst has been identified, thereby providing more targeted auxiliary information for clinical management.