Association of Systemic Inflammatory Markers With Endometrioma Diagnosis and Douglas Involvement: A Retrospective Analysis

Association of Systemic Inflammatory Markers With Endometrioma Diagnosis and Douglas Involvement: A Retrospective Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of Systemic Inflammatory Markers With Endometrioma Diagnosis and Douglas Involvement: A Retrospective Analysis Hamza YILDIZ, Kasim AKAY, Gorkem ULGER, Hakan AYTAN, Faik Gurkan YAZICI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7623083/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background This study sought to examine the function of hematological inflammatory markers in differentiating endometrioma from other benign ovarian cysts prior to surgery and in individuals with Douglas involvement. Materials and Methods The study was designed retrospectively and included 480 patients (321 endometrioma, 153 non-endometrioma) who were operated on for benign ovarian cysts at Mersin University Hospital between January 1, 2004, and March 1, 2024. Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), Pan-immune inflammation value (PIV), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) were analyzed as hematological inflammatory markers. Additionally, the indices were evaluated in patients with Douglas involvement, and ROC analysis was conducted to determine their predictive power. Results The average age of patients with endometrioma did not show a significant difference compared to the average of patients with other benign ovarian cysts (39.0 vs. 40.0 years, p = 0.174). NLR, PLR, SII, and SIRI were significantly higher in the endometrioma group (p < 0.001). SIRI had the highest value (0.894) in distinguishing endometrioma from other benign ovarian cysts. Douglas involvement was found to be significantly high with SII, PIV, and PLR having AUCs of 0.569, 0.576, and 0.0582, respectively. Conclusion Hematological inflammatory markers, particularly SIRI, were found to be useful in distinguishing endometriomas from other benign ovarian cysts, and PLR was identified as a potential non-invasive biomarker when analyzing its predictive power in patients with Douglas involvement. Benign ovarian cysts Endometrioma ovarian cysts Hematological inflammatory markers Douglas invasion Figures Figure 1 Figure 2 INTRODUCTION Ovarian cysts represent one of the most challenging diagnostic dilemmas in gynecological practice, affecting approximately 7–35% of women of reproductive age [ 1 , 2 ]. The clinical differentiation between various types of benign ovarian cysts, particularly endometriomas versus other benign lesions, poses significant diagnostic challenges that directly impact surgical planning, patient counseling, and treatment outcomes [ 3 ]. The preoperative differentiation of ovarian cysts remains a complex clinical problem despite advances in imaging technology. While transvaginal ultrasonography serves as the first-line imaging modality, its diagnostic accuracy varies significantly with operator experience and cyst characteristics, with sensitivity ranging from 51–97% for endometriomas depending on expertise and protocols [ 4 ]. Magnetic resonance imaging (MRI), though highly accurate with sensitivity of 83–95% and specificity up to 100% for endometriomas, is expensive, not universally available, and may be contraindicated in certain patients [ 5 ]. The clinical presentation of endometriomas often overlaps with other benign ovarian pathologies, creating diagnostic uncertainty. Patients may present with non-specific symptoms including pelvic pain, dysmenorrhea, and dyspareunia, which are not pathognomonic for endometriosis [ 3 ]. This diagnostic ambiguity is particularly pronounced in emergency settings where rapid decision-making is required, and in primary care environments where specialized imaging may not be immediately accessible. Endometriosis affects approximately 10% of women of reproductive age globally, representing a significant healthcare burden with substantial impacts on quality of life, fertility, and healthcare costs [ 6 , 7 ]. Endometriomas, or "chocolate cysts," are a manifestation of ovarian endometriosis that can lead to substantial morbidity including chronic pelvic pain, infertility, and require complex surgical management [ 8 ]. The accurate preoperative identification of endometriomas is crucial for several clinical reasons: Surgical planning and the potential need for multidisciplinary teams, fertility preservation strategies, patient counseling regarding recurrence risks, and anticipation of surgical complexity, particularly when deep infiltrating endometriosis with Douglas pouch involvement is suspected [ 9 ]. Douglas pouch involvement represents a particularly challenging aspect of endometriosis diagnosis and management. The presence of deep infiltrating endometriosis in the rectovesical pouch significantly increases surgical complexity, requires specialized surgical expertise, and may necessitate multidisciplinary involvement including colorectal and urological surgeons [ 9 , 10 ]. Current imaging modalities have limited accuracy in predicting the extent of Douglas involvement, with specialist transvaginal ultrasound showing area under the curve values of 0.82 for pouch of Douglas detection, creating a need for additional preoperative markers that could enhance surgical planning and patient preparation [ 10 ]. Endometriosis is fundamentally a chronic inflammatory disease. The presence of ectopic endometrial tissue triggers a strong inflammatory response involving activated immune cells like macrophages and increased production of cytokines (e.g., IL-1β, IL-6, TNF-α). This process leads to a systemic immune response that extends beyond the local pelvic area. Research suggests that this systemic inflammation can precede a clinical diagnosis, meaning elevated inflammatory markers like C-reactive protein and IL-6 may serve as early diagnostic indicators. The inflammatory cascade involves various cells (neutrophils, lymphocytes, monocytes, and platelets) which are the same cellular components used to calculate hematological inflammatory indices [ 11 , 12 ]. Recent research is focusing on accessible and cost-effective biomarkers for endometriosis, particularly those derived from routine complete blood count analyses. These markers are promising because they are widely available, inexpensive, provide rapid results, and reflect the systemic inflammatory burden and the complex immune processes of the disease [ 13 , 14 ]. Markers like the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) are promising for diagnosing endometriosis. These indices, which integrate multiple aspects of the inflammatory response, can achieve 76% sensitivity and 70% specificity when combined. This makes them a cost-effective alternative to expensive imaging modalities [ 14 , 15 ]. The biological plausibility of these markers as endometriosis biomarkers is supported by their reflection of the key pathophysiological processes underlying the disease: chronic inflammation, immune dysregulation, and enhanced coagulation cascades [ 11 , 12 ]. Given the diagnostic challenges of endometriosis, this study aimed to evaluate the utility of hematological inflammatory markers. The primary goals were to assess their ability to differentiate endometriomas from other benign ovarian cysts before surgery and to predict Pouch of Douglas involvement, thereby improving surgical planning and patient outcomes. MATERIAL AND METHOD This retrospective study encompasses 480 patients who underwent surgery for benign ovarian cysts at Mersin University Hospital between January 1, 2004, and March 1, 2024. The patients included in the study were divided into two groups: those with endometrioma (n = 321) and those with other benign ovarian cysts without endometrioma (n = 159). Mersin University Hospital is a tertiary academic medical center serving as a regional referral hospital for gynecological conditions in the Mediterranean region of Turkey. The gynecology department receives both primary cases and complex referrals from surrounding healthcare facilities. This study was performed in line with the principles of the Declaration of Helsinki. The study protocol was approved by the Clinical Research Ethics Committee of Mersin University Rectorate (with the ethics committee decision numbered 2024/624 dated 10/07/2024). Informed consent was obtained from all individual participants included in the study. The demographic characteristics of the patients (age, gravida, parity, number of abortions), cyst sizes, and hematological inflammatory markers were analyzed. Among the hematological inflammatory markers are the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), Pan-immune inflammation value (PIV), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). Patients under 18 years of age and over 55 years of age, postmenopausal patients, those with pelvic inflammatory disease, tuberculosis or active infections, endocrine or immunological disorders, chronic lung, liver or kidney diseases, chronic inflammatory conditions, those taking anti-inflammatory medications, and individuals with a history of malignancy were excluded. All remaining patients, within one month prior to surgery, underwent a complete blood count test as part of the preoperative evaluation, ensuring that there were no active infections at the time of the blood test. Patients diagnosed with borderline or malignant diseases, excluding endometriosis, were excluded from the study if they were missing preoperative evaluations. A total of 480 patients were included in the study. Hemogram parameters are evaluated using the SYSMEX-XN-1000/23797 device, and 28 parameters are studied. Among these parameters, hemoglobin (g/dL), hematocrit (%), white blood cell (10^3/µL), neutrophil (10^3/µL), lymphocyte (10^3/µL), monocyte (10^3/µL), platelet (10^3/µL), and CA-125 were recorded, and these values were later obtained. The CA-125 (U/mL) value is measured using ELISA, Roche®. Markers; SII: (neutrophil count x platelet count)/lymphocyte count (10^9/L), SIRI: (neutrophil count x monocyte count)/lymphocyte count (10^9/L), NLR: neutrophil lymphocyte count/lymphocyte count (10^9/L), PIV: (neutrophil count x monocyte count x platelet count)/lymphocyte count (10^9/L), PLR: platelet count/lymphocyte count (10^9/L). All surgical procedures were performed by experienced gynecological surgeons at Mersin University Hospital. The surgical team consisted of gynecologists with specialized training in endometriosis surgery. The majority of procedures (n = 183, 38.13%) were performed laparoscopically, while open surgical approaches were used in complex cases requiring extensive adhesiolysis or when laparoscopic access was contraindicated (n = 297, 61.87%). For cases involving deep infiltrating endometriosis with suspected bowel or urological involvement, a multidisciplinary surgical team approach was employed, including consultation with colorectal surgeons and urologists as needed. Douglas involvement was assessed intraoperatively and confirmed through direct visualization and, when necessary, histopathological examination of excised tissue. Surgical techniques included cystectomy for endometriomas, with careful stripping of the cyst wall while preserving healthy ovarian tissue. Complete excision of endometriotic lesions was performed when feasible, with particular attention to Douglas pouch involvement, which was classified based on the extent of adhesions and implants in the rectovesical pouch. Given the extended study period spanning 20 years (2004–2024), we acknowledge potential variations in diagnostic approaches, surgical techniques, and documentation standards over time. During the early years of the study period (2004–2010), diagnostic imaging relied primarily on conventional ultrasonography and computed tomography scans, while MRI became more routinely available and utilized for endometriosis diagnosis from 2010 onwards. Similarly, laparoscopic surgical techniques and equipment underwent significant improvements, with high-definition cameras and advanced energy devices becoming standard practice after 2012.Cases with incomplete surgical documentation or inconsistent reporting were excluded from the analysis. Statistical analyses were conducted using SPSS 22.0 and MedCalc 20.211 software. Comparisons between groups were made using the Student t-test or Mann-Whitney U test. Correlation analyses were conducted using Pearson or Spearman correlation coefficients. ROC curve analysis was used to evaluate diagnostic accuracy. RESULTS This retrospective analysis investigated 480 individuals diagnosed with endometrioma (66.9%, n = 321) and other benign ovarian cysts (33.1%, n = 159). Among patients diagnosed with various benign ovarian disorders, 95 (19.8%) were identified as having serous cystadenoma, 43 (9.0%) as mucinous cystadenoma, and 21 (4.4%) as hemorrhagic cystadenoma. There was no significant difference between the groups regarding age (39.0 vs. 40.0 years; p = 0.174) and cyst size (6.0 vs. 6.0 cm; p = 0.067), however gravida (1.0 vs. 2.0; p < 0.05) and parity (1.0 vs. 2.0; p < 0.05) were considerably lower in the endometrioma group. CA-125 levels were considerably elevated in the endometrioma group relative to the control group (48.0 vs. 15.0 U/mL; p < 0.05) (Table 1 ). Table 1 Comparison of Demographic, Biochemical, Hematological, and Inflammatory Markers in Endometrioma and Non-Endometrioma Benign Ovarian Cysts Endometrioma (n = 321) median (min-max) Non-endometrioma (n = 159) median (min-max) p Demographic and Biochemical Characteristics Age 39,0 (18,0–50,0) 40,0 (18,0–50,0) 0.174 Gravida 1,0 (0,0–6,0) 2,0 (0,0–10,0) < 0.001 Parite 1,0 (0,0–5,0) 2,0 (0,0–10,0) < 0.001 Abortar 0,0 (0,0–3,0) 0,0 (0,0–3,0) 0.105 CA-125 (U/mL) 48,0 (4,0-1902,0) 15,0 (3,0-124,0) < 0.001 Cyst Size (cm) 6,0 (3,0–20,0) 6,0 (3,0–14,0) 0.067 Hematological and Biochemical Parameters HBG 10.90 (8,0–14,0) 12.7 (7,6–15,5) < 0.001 HTC 33.0 (25,0–43,0) 38.0 (25,0–46,0) < 0.001 WBC 11.1 (5,89 − 16,7) 7.62 (4,10–12,88) < 0.001 NEUT 8.51 (3,90 − 14,88) 4.58 (1.46–9.24) < 0.001 LYMPH 1,65 (0,27 − 5,70) 2,12 (0,52 − 4,06) < 0.001 MONO 0,71 (0,10 − 1,33) 0,51 (0,14 − 1,13) < 0.001 PLT 241,0 (102,0-712,0) 285,0 (110,0-665,0) < 0.001 Inflammatory Markers SII 1408,45 (183,64-12182,40) 624,0 (128,63-3235,85) < 0.001 SIRI 4,06 (0,22–25,20) 1,11 (0,24 − 8,75) < 0.001 PIV 921,08 (44,46-8251,25) 324,23 (47,59-1478,5) < 0.001 NLR 5,79 (0,9–32,41) 2,09 (0,71 − 12,15) < 0.001 PLR 166,47 (40,80–864,15) 137,5 (51,71–438,46) < 0.001 *HBG (hemoglobina-g/dL), HTC (hematocrito-%), WBC (white blood cell-x10^3/µL), NEUT (neutrophil-x10^3/µL), LYMPH (lymphocyte-x10^3/µL), MONO (monocyte-x10^3/µL), PLT (plaqueta-x10^3/µL) The endometrioma group exhibited reduced hemoglobin levels (10.90 vs. 12.7 g/dL; p < 0.05) and hematocrit (33.0% vs. 38.0%; p < 0.05), whereas leukocyte (11.1 vs. 7.62 x10³/µL; p < 0.05) and neutrophil (8.51 vs. 4.58 x10³/µL; p < 0.05) counts were elevated. Lymphocyte (1.65 vs. 2.12 x10³/µL; p < 0.05) and platelet (241.0 vs. 285.0 x10³/µL; p < 0.05) counts were reduced in the endometrioma cohort. The inflammatory indices NLR (5.79 vs. 2.09; p < 0.05), PLR (166.47 vs. 137.5; p < 0.05), SII (1408.45 vs. 624.0; p < 0.05), and SIRI (4.06 vs. 1.11; p < 0.05) were markedly elevated in the endometrioma cohort (Table 1 ). ROC evaluations of the indices utilized to distinguish the presence or absence of endometrioma indicate that all indicators exhibit substantial discriminatory capability. SIRI has been identified as the index with the highest AUC (AUC = 0.894, Standard Error = 0.0149, 95% Confidence Interval: 0.863–0.920). The AUC values for the remaining indices are as follows: NLR AUC = 0.875 (Standard Error = 0.0165, 95% Confidence Interval: 0.843–0.904), PIV AUC = 0.858 (Standard Error = 0.0166, 95% Confidence Interval: 0.824–0.888), and SII AUC = 0.830 (Standard Error = 0.0185, 95% Confidence Interval: 0.793–0.863) (Table 2 ). Table 2 ROC Values of Indices in the Differentiation of Endometrioma Presence/Absence Presence of Endometrioma AUC Standard Error 95% Confidence Interval SII 0,830 0,0185 0,793-0,863 SIRI 0,894 0,0149 0,863-0,920 PIV 0,858 0,0166 0,824-0,888 NLR 0,875 0,0165 0,843-0,904 The ROC analysis results have established the best cutoff points for the indices, together with their sensitivity, specificity, and 95% confidence intervals. The established cutoff value for SIRI is ≤ 1.91, with a sensitivity of 86.79% (95% Confidence Interval: 80.5–91.6) and a specificity of 81.31% (95% Confidence Interval: 76.6–85.4). The threshold for SII was established at ≤ 939.25, exhibiting a sensitivity of 85.53% (95% Confidence Interval: 79.1–90.6) and a specificity of 67.91% (95% Confidence Interval: 62.5–73.0). The threshold for PIV was established at ≤ 476.91, exhibiting a sensitivity of 76.73% (95% Confidence Interval: 69.4–83.1) and a specificity of 79.75% (95% Confidence Interval: 74.9–84.0). The threshold for NLR was established at ≤ 3.49, exhibiting a sensitivity of 91.82% (95% Confidence Interval: 86.4–95.6) and a specificity of 75.39% (95% Confidence Interval: 70.3–80.0). The p-value for all indices was determined to be < 0.001 (Table 3 , Fig. 1 ). Table 3 The ROC analysis results of the indices Indices Criterion Sensitivity 95% Confidence Interval Specificity 95% Confidence Interval p SIRI ≤ 1,91 86,79 80,5–91,6 81,31 76,6–85,4 < 0.001 SII ≤ 939,25 85,53 79,1–90,6 67,91 62,5–73,0 < 0.001 PIV ≤ 476,91 76,73 69,4–83,1 79,75 74,9–84,0 < 0.001 NLR ≤ 3,49 91,82 86,4–95,6 75,39 70,3–80,0 < 0.001 In the analysis conducted, the data of 173 patients with Douglas involvement and 148 patients without involvement were evaluated for the relationship between the indices. Our analysis revealed that the endometrioma group with Douglas involment had significantly higher median levels of PLR [178.32 (59.48–846.00) − 147.81 (40.80-864.15); p < 0.05], PIV [1056.64 (58.67-8251.25) − 767.19 (44.46-5338.07); p < 0.05], and SII [1529.74 (183.64-12182.40) − 1220.21 (251.28-9643.92); p < 0.05] compared to the group without Douglas involvement (Table 4 ). Table 4 Relationship between Douglas Involvement and SII, PIV, and PLR Levels Douglas involvement Yes (n = 173) median (min-max) No (n = 148) median (min-max) p SII 1529,74 (183,64-12182,40) 1220,21 (251,28-9643,92) 0.033 PIV 1056,64 (58,67-8251,25) 767,19 (44,46-5338,07) 0.019 PLR 178,32 (59,48–846,00) 147,81 (40,80–864,15) 0.011 ROC curve study was performed to assess the diagnostic efficacy in predicting patients with Douglas involvement. The threshold value, sensitivity, and specificity for each index are as follows: PIV (≤ 759.7; 65.92%; 49.37%; p < 0.05), PLR (≤ 150.7; 65.92%; 51.27%; p < 0.05), and SII (≤ 1481.9; 53.63%; 60.76%; p < 0.05). The AUC values for PLR, PIV, and SII were determined to be 0.580 (p < 0.05), 0.567 (p < 0.05), and 0.564 (p < 0.05), respectively (Table 5 , Fig. 2). Upon examination of the data, it was noted that the values with the greatest ROC thresholds for differentiating patients with Douglas involvement were PLR, PIV, and SII. Table 5 SII, PIV and PLR ROC analyses in endometriosis Douglas involvement Indices Criterion Sensitivity 95% Confidence Interval Specificity 95% Confidence Interval p SII ≤ 1481,9 53,63 46,0–61,1 60,76 52,7–68,4 0.0416 PIV ≤ 759,7 65,92 58,5–72,8 49,37 41,3–57,4 0.0311 PLR ≤ 150,7 65,92 58,5–72,8 51,27 43,2–69,3 0.0105 DISCUSSION The results emphasised that inflammatory markers are essential in clinical practice during the preoperative phase for the differential diagnosis of endometrioma and other benign ovarian cysts, particularly in predicting patients with and without Douglas involvement. Upon comparison of patient values, we observed markedly elevated counts of neutrophils, lymphocytes, monocytes, and platelets. The results suggest that hematological inflammatory markers, specifically SIRI, SII, PIV, and NLR, may be significant in the differential diagnosis of endometrioma and other benign ovarian cysts. Inflammation is pivotal in the etiology of endometriosis, since the ectopic endometrial tissue within endometriomas induces chronic inflammation and the secretion of cytokines and chemokines in the pelvic milieu [ 13 ]. The research by Cho and associates revealed that patients with endometriomas exhibited reduced hemogram and hematocrit values, although white blood cell and neutrophil counts were elevated [ 16 ]. These findings suggest that the inflammatory response may be more pronounced in patients with endometrioma [ 17 ]. In our study, we found that hemoglobin and hematocrit values were lower, and due to the inflammatory process, WBC and neutrophil counts were statistically significantly higher. In 2019, Wu and colleagues reported that in a routine population study in China, the average NLR value in women was 1.62 ± 0.64 and the PLR was 108.02 ± 32.99 [ 18 ]. But despite this, a study found that the NLR values in patients diagnosed with endometriosis were higher than the specified value [ 19 ]. This finding indicates that systemic inflammation is more pronounced in patients with endometrioma. In our study, it was also shown that NLR is an effective marker in distinguishing endometrioma. In the ROC analysis, the AUC for NLR was found to be 0.875. This value indicates that NLR has good diagnostic accuracy in the diagnosis of endometrioma. Platelets can exacerbate inflammation by inducing the production of inflammatory mediators and activating endothelial cells. The increase in platelet count in women with endometriosis may be a result of the inflammatory process. In the study conducted by Kalem and colleagues [ 20 ] with 213 patients, it was found that the PLR ratio, like the NLR, was significantly higher in patients with endometriomas compared to those with benign ovarian cysts, consistent with our study. The values found in our study are higher than the values found and are consistent with the literature. SII is considered a marker that reflects both the severity of inflammation and the suppression of cellular immunity. In a study conducted on healthy women, the SII value was reported as 526.7 ± 336.6, which is lower than the data obtained in our study [ 21 ]. When examining studies related to the topic, it has been reported that the SII value in the endometrioma group was statistically significant and high [ 19 , 20 ]. This also shows us that it can be used as an inflammatory index and in addition to clinical examination and imaging in the diagnosis of endometriosis. The high SIRI in patients with endometrioma indicates that the inflammation is more severe. A study by Zhou et al. compared patients with endometriosis to a control group, revealing that SIRI, SII, PIV, and NLR were considerably elevated in patients with endometriomas relative to the control group [ 19 ]. After the evaluation, it has been shown that the SIRI index can be used to predict the severity of inflammation and can assist in the diagnosis of endometrioma when used alongside other biomarkers PIV reflects platelet activation and inflammation. The high PIV in patients with endometrioma indicates that, in addition to inflammation, platelet activation is also increased. Gasparyan and colleagues [ 22 ], who analyzed platelet indices and inflammatory indices, emphasized that the platelet indices from 45 studies on rheumatic diseases significantly indicated inflammation. In the endometriosis study, Moini and colleagues [ 23 ] found that the ratios of neutrophils, monocytes, platelets, and lymphocytes were statistically significant. In a study by Lukacs and colleagues [ 24 ], on the number of monocytes and neutrophils during the preoperative and postoperative periods, a decrease in monocytes was reported in the postoperative period. This situation highlighted that monocytes returned to normal levels after the removal of endometriosis lesions and emphasized the importance of phagocytic cells in endometriosis. This situation, in parallel with our study, shows that in endometriosis, monocytes increase in number instead of platelets and lymphocytes for the humoral immune response. In clinical practice, the use of diagnostic tests for the presence or absence of disease is important. Determining threshold values in ROC analyses will facilitate diagnosis to evaluate diagnostic accuracy. A study investigated PLR and NLR levels for the differential diagnosis of endometrioma and other benign ovarian cysts. ROC analyses were performed, revealing an AUC of 0.59 for PLR, with a sensitivity of 56% and a specificity of 62.5%. The AUC for NLR was 0.56, with a sensitivity of 32.9% and a specificity of 80.2% [ 25 ]. This study employed ROC curve analysis to assess the potential utility of hematological inflammatory markers in the differential diagnosis of endometrioma vs other benign ovarian cysts. ROC study indicated that SIRI exhibits the highest diagnostic accuracy in differentiating endometrioma from other benign ovarian cysts (AUC: 0.894; 95% Confidence Interval: 0.863–0.920, sensitivity 86.79%, and specificity 81.31%). Confidence interval: 0.863–0.920, sensitivity found to be 86.79% and specificity 81.31%. Values above the threshold determined for SIRI indicate a high probability of an endometrioma diagnosis. When the results are evaluated, an AUC of 0.8 and above indicates good diagnostic accuracy [ 26 ]. In this case, other hematological inflammatory markers such as SII, PIV, and NLR also had significant AUC values (0.830, 0.858, and 0.875, respectively), suggesting their potential roles in the diagnosis of endometrioma. These findings suggest that hematological inflammatory markers, particularly SIRI, could be used as non-invasive adjunctive tools in the diagnosis of endometrioma. However, further research is needed for the clinical use of these markers. The distinguishing factor of our study is the computation of threshold values via ROC analysis to assess the prediction capability of evaluating patients with Douglas involvement in endometriosis. Analysis of the data revealed that SII, PIV, and PLR were effective indicators of Douglas involvement. Upon analysis of the indices, the p-values for PLR, PIV, and SII were determined to be significant and measured at 0.011, 0.019, and 0.033, respectively. The AUC values were 0.0582 for PLR, 0.0576 for PIV, and 0.569 for SII. The greatest values for positive likelihood ratio (PLR) were observed at 65.3 and 54.1 for sensitivity and specificity, respectively. These results suggest that if PLR and other indices are specifically examined beforehand for a patient with endometriosis, the Douglas involvement can be predicted before the operation. Wang et al. evaluated patients with endomeriosis stage IV and found that NLR was statistically significant and elevated [ 27 ]. Similarly, Jing and colleagues concluded that NLR can be used as a marker in stage III/IV patients [ 25 ]. In our research, unlike the studies conducted, the Douglas involvement was examined in all patients with endometriosis, not just in the advanced stages. Additionally, no threshold values have been identified in terms of diagnostic value in the conducted studies. In our study, however, the PLR, PIV, and SII parameters may be valuable in predicting Douglas involvement. These results show us that, in addition to imaging, indices can be used to demonstrate Douglas involvement. The choice of surgical procedure for the patient can allow for preparations to be made in advance by anticipating complications that may arise in the intestines, bladder, or pelvic side wall due to adhesions found in the Douglas before the operation. This can help ensure the necessary safety for the patient before the operation, provide the necessary information to the patient, and make the outcomes more controlled and less harmful. Despite these promising results, our study has some limitations. The retrospective design of our study represents a significant limitation that prevents the establishment of causal relationships between hematological inflammatory markers and endometrioma diagnosis. This design inherently limits our ability to determine whether elevated inflammatory markers are a cause or consequence of endometriosis, and prevents us from establishing temporal relationships between marker elevation and disease progression. Additionally, retrospective analysis may introduce selection bias and information bias, as we relied on previously recorded data without controlling for confounding variables that could influence inflammatory marker levels, such as concurrent infections, stress, medication use, or other inflammatory conditions that may not have been adequately documented. Secondly, our analysis focused solely on hematological inflammatory markers, potentially overlooking other relevant biomarkers were not alayzed or compared. The study's single-center design may restrict the generalizability of our results. As this study was conducted at a single tertiary care center that serves as a regional referral hospital, our patient population may represent more complex or severe cases of endometriosis compared to community-based populations, which could potentially limit the generalizability of our findings to primary care settings or less severe disease presentations. The surgical procedures were performed by a limited number of experienced surgeons at a single center, which, while ensuring consistency in surgical technique and Douglas involvement assessment, may limit the external validity of our findings to centers with different surgical expertise or protocols. The 20-year study span represents both a strength and limitation of our research. While it provides a large sample size and diverse case representation, it introduces potential temporal bias due to evolving diagnostic criteria, surgical techniques, and documentation standards. Changes in imaging technology, laboratory methods, and surgical expertise over two decades may have influenced case selection, disease staging accuracy, and outcome reporting. Although our subgroup analysis showed consistent inflammatory marker patterns across time periods, we cannot completely exclude the possibility that temporal changes in patient selection or diagnostic precision may have influenced our findings. However, our study also has several strengths. The study included a large sample size of 480 patients and evaluated various hematological inflammatory markers, including SIRI, SII, PIV, and NLR, which have not been extensively investigated in previous research. By determining threshold values for these markers using ROC analysis, we have provided valuable information for their potential clinical application. To overcome the limitations of retrospective analysis, future prospective cohort studies should follow patients longitudinally, measuring inflammatory markers before, during, and after endometriosis development. Additionally, randomized controlled trials examining these markers in combination with established diagnostic modalities would strengthen their clinical utility and predictive value. While imaging remains the gold standard for endometrioma diagnosis, hematological inflammatory markers, particularly SIRI, offer valuable complementary information in specific clinical contexts. Their primary utility lies in rapid screening, preoperative risk stratification for Douglas involvement, and enhancing diagnostic confidence when combined with imaging findings. These cost-effective, widely available biomarkers could improve clinical decision-making, particularly in resource-limited settings or when rapid triage is required. However, they should be viewed as adjunctive tools that enhance rather than replace current diagnostic approaches.The study specifically indicated that SIRI could significantly contribute to the differential diagnosis of endometrioma vs other benign ovarian cysts, enhance the probability of identifying endometrioma, and that the PLR value could serve as a predictive tool for individuals with Douglas involvement. Abbreviations NLR Neutrophil-lymphocyte ratio PLR Platelet-lymphocyte ratio PIV Pan-immune inflammation value SII Systemic immune-inflammation index SIRI Systemic inflammation response index ROC A receiver operating characteristic AUC Area under the curve MRI Magnetic resonance imaging Declarations Ethics approval and consent to participate: This study was performed in line with the principles of the Declaration of Helsinki. The study protocol was approved by the Clinical Research Ethics Committee of Mersin University Rectorate (with the ethics committee decision numbered 2024/624 dated 10/07/2024). Informed consent was obtained from all individual participants included in the study. Consent for publication: Not applicable Competing interests: The authors have no relevant financial or non-financial interests to disclose. Funding: The authors have no relevant financial or non-financial interests to disclose. Author Contribution HY, KA and GU developed the concept and were responsible for data collection. FGY, HY and HA planned the study. HY, GU and KA analysed the results. HA and FGY wrote the manuscript text and prepared figures and tables. All authors reviewed the manuscript for important intellectual content and approved the final version. Acknowledgements: Not applicable Authors' information : Not applicable Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Borgfeldt C, Andolf E. Transvaginal sonographic ovarian findings in a random sample of women 25–40 years old. Ultrasound Obstet Gynecology: Official J Int Soc Ultrasound Obstet Gynecol. 1999;13(5):345–50. Pavlik EJ, Ueland FR, Miller RW, Ubellacker JM, DeSimone CP, Elder J, Hoff J, Baldwin L, Kryscio RJ, van Nagell JR Jr. Frequency and disposition of ovarian abnormalities followed with serial transvaginal ultrasonography. Obstet Gynecol. 2013;122(2 PART 1):210–7. Farghaly S. Current diagnosis and management of ovarian cysts. Clin Exp Obstet Gynecol. 2014;41(6):609–12. Lou Y, Li D, Yu J, Chen J, Jin X. Diagnostic performance of transvaginal sonography vs. magnetic resonance imaging for rectovaginal septum deep infiltrating endometriosis: a head-to-head comparative meta-analysis. Clin Radiol. 2024;79(8):618–27. Salman S, Shireen N, Riyaz R, Khan SA, Singh JP, Uttam A. Magnetic resonance imaging evaluation of gynecological mass lesions: A comprehensive analysis with histopathological correlation. Medicine. 2024;103(32):e39312. Mitranovici M-I, Costachescu D, Voidazan S, Munteanu M, Buicu C-F, Oală IE, Ivan V, Apostol A, Melinte IM, Crisan A. Exploring the Shared Pathogenesis Mechanisms of Endometriosis and Cancer: Stemness and Targeted Treatments of Its Molecular Pathways—A Narrative Review. Int J Mol Sci. 2024;25(23):12749. Cho YJ, Kim HY. Oxidative stress and endometriosis. Kosin Med J. 2018;33(2):135–40. Jiang D, Nie X. Effect of endometrioma and its surgical excision on fertility. Experimental therapeutic Med. 2020;20(5):114. Schneyer RJ, Hamilton KM, Meyer R, Nasseri YY, Siedhoff MT. Surgical treatment of colorectal endometriosis: an updated review. Curr Opin Obstet Gynecol. 2024;36(4):239–46. Pattanasri M, Ades A, Nanayakkara P. Correlation between ultrasound findings and laparoscopy in prediction of deep infiltrating endometriosis (DIE). Aust N Z J Obstet Gynaecol. 2020;60(6):946–51. As-Sanie S, Mackenzie SC, Morrison L, Schrepf A, Zondervan KT, Horne AW. Missmer SA: Endometriosis: a review. Jama 2025. Mu F, Harris HR, Rich-Edwards JW, Hankinson SE, Rimm EB, Spiegelman D, Missmer SA. A prospective study of inflammatory markers and risk of endometriosis. Am J Epidemiol. 2018;187(3):515–22. Duan Y-N, Peng Y-Q, Xu X, Shi X-L, Peng C-X. Positive correlation between NLR and PLR in 10,458 patients with endometriosis in reproductive age in China. Eur Rev Med Pharmacol Sci 2023, 27(5). Zhou Y, Liu G, Yuan L, Qiao Y, Chen Q. Evaluating systemic immune-inflammation indices as predictive markers for endometriosis diagnosis: A retrospective observational study. J Reprod Immunol. 2025;167:104416. Wang W, Zeng W, Yang S. A stacked machine learning-based classification model for endometriosis and adenomyosis: a retrospective cohort study utilizing peripheral blood and coagulation markers. Front Digit health. 2024;6:1463419. Cho H-Y, Park S-T, Park S-H. Red blood cell indices as an effective marker for the existence and severity of endometriosis (STROBE). Medicine. 2022;101(42):e31157. Björk E, Vinnars MT, Nagaev I, Nagaeva O, Lundin E, Ottander U, Mincheva-Nilsson L. Enhanced local and systemic inflammatory cytokine mRNA expression in women with endometriosis evokes compensatory adaptive regulatory mRNA response that mediates immune suppression and impairs cytotoxicity. Am J Reprod Immunol. 2020;84(4):e13298. Wu L, Zou S, Wang C, Tan X, Yu M. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio in Chinese Han population from Chaoshan region in South China. BMC Cardiovasc Disord. 2019;19:1–5. Zhou Y, Liu G, Yuan L, Qiao Y, Chen Q. Evaluating systemic immune-inflammation indices as predictive markers for endometriosis diagnosis: A retrospective observational study. J Reprod Immunol. 2025;167:104416. Kalem Z, Şimşir Ç, Bakırarar B, Kalem MN. The additional diagnostic value of NLR and PLR for CA-125 in the differential diagnosis of endometrioma and benign ovarian cysts in women of reproductive age: a retrospective case-control study. Eur Res J 2020. Qing G, He H, Lai M, Li X, Chen Y, Wei B. Systemic immune-inflammatory index and its association with female sexual dysfunction, specifically low sexual frequency, in depressive patients: Results from NHANES 2005 to 2016. Medicine. 2024;103(22):e38151. Gasparyan AY, Ayvazyan L, Mukanova U, Yessirkepov M, Kitas GD. The platelet-to-lymphocyte ratio as an inflammatory marker in rheumatic diseases. Annals Lab Med. 2019;39(4):345–57. Moini A, Ghanaat M, Hosseini R, Rastad H, Hosseini L. Evaluating hematological parameters in women with endometriosis. J Obstet Gynaecol. 2021;41(7):1151–6. Lukács L, Kovács AR, Pál L, Szűcs S, Kövér Á, Lampé R. Phagocyte function of peripheral neutrophil granulocytes and monocytes in endometriosis before and after surgery. J Gynecol Obstet Hum Reprod. 2021;50(4):101796. Jing X, Li C, Sun J, Peng J, Dou Y, Xu X, Ma C, Dong Z, Liu Y, Zhang H. Systemic inflammatory response markers associated with infertility and endometrioma or uterine leiomyoma in endometriosis. Ther Clin Risk Manag 2020:403–12. Muller MP, Tomlinson G, Marrie TJ, Tang P, McGeer A, Low DE, Detsky AS, Gold WL. Can routine laboratory tests discriminate between severe acute respiratory syndrome and other causes of community-acquired pneumonia? Clin Infect Dis. 2005;40(8):1079–86. Wang L, Ling J, Zhu X, Zhang Y, Li R, Huang J, Huang D, Wu C, Zhou H. The coagulation status in women of endometriosis with stage IV. BMC Womens Health. 2024;24(1):386. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Jun, 2026 Reviews received at journal 26 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 17 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 15 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":379360,"visible":true,"origin":"","legend":"\u003cp\u003eSIRI (A), SII (B), PIV (C), NLR (D) the ability to foresee its existence\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7623083/v1/7b2697cacb83becc326d658e.png"},{"id":94236597,"identity":"6f1a457c-ce5d-41f5-b47e-8353127b60e0","added_by":"auto","created_at":"2025-10-24 02:15:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eImage is not available with this version\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7623083/v1/f4b713822e9e20ff8d87f703.png"},{"id":94237503,"identity":"0d4b4db1-d3ea-45f4-8650-5347bf6b0436","added_by":"auto","created_at":"2025-10-24 02:31:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1197005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7623083/v1/d4712284-34a6-404b-80fc-979719ab7068.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation of Systemic Inflammatory Markers With Endometrioma Diagnosis and Douglas Involvement: A Retrospective Analysis\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eOvarian cysts represent one of the most challenging diagnostic dilemmas in gynecological practice, affecting approximately 7\u0026ndash;35% of women of reproductive age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The clinical differentiation between various types of benign ovarian cysts, particularly endometriomas versus other benign lesions, poses significant diagnostic challenges that directly impact surgical planning, patient counseling, and treatment outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe preoperative differentiation of ovarian cysts remains a complex clinical problem despite advances in imaging technology. While transvaginal ultrasonography serves as the first-line imaging modality, its diagnostic accuracy varies significantly with operator experience and cyst characteristics, with sensitivity ranging from 51\u0026ndash;97% for endometriomas depending on expertise and protocols [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Magnetic resonance imaging (MRI), though highly accurate with sensitivity of 83\u0026ndash;95% and specificity up to 100% for endometriomas, is expensive, not universally available, and may be contraindicated in certain patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The clinical presentation of endometriomas often overlaps with other benign ovarian pathologies, creating diagnostic uncertainty. Patients may present with non-specific symptoms including pelvic pain, dysmenorrhea, and dyspareunia, which are not pathognomonic for endometriosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This diagnostic ambiguity is particularly pronounced in emergency settings where rapid decision-making is required, and in primary care environments where specialized imaging may not be immediately accessible.\u003c/p\u003e\u003cp\u003eEndometriosis affects approximately 10% of women of reproductive age globally, representing a significant healthcare burden with substantial impacts on quality of life, fertility, and healthcare costs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Endometriomas, or \"chocolate cysts,\" are a manifestation of ovarian endometriosis that can lead to substantial morbidity including chronic pelvic pain, infertility, and require complex surgical management [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The accurate preoperative identification of endometriomas is crucial for several clinical reasons: Surgical planning and the potential need for multidisciplinary teams, fertility preservation strategies, patient counseling regarding recurrence risks, and anticipation of surgical complexity, particularly when deep infiltrating endometriosis with Douglas pouch involvement is suspected [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDouglas pouch involvement represents a particularly challenging aspect of endometriosis diagnosis and management. The presence of deep infiltrating endometriosis in the rectovesical pouch significantly increases surgical complexity, requires specialized surgical expertise, and may necessitate multidisciplinary involvement including colorectal and urological surgeons [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Current imaging modalities have limited accuracy in predicting the extent of Douglas involvement, with specialist transvaginal ultrasound showing area under the curve values of 0.82 for pouch of Douglas detection, creating a need for additional preoperative markers that could enhance surgical planning and patient preparation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEndometriosis is fundamentally a chronic inflammatory disease. The presence of ectopic endometrial tissue triggers a strong inflammatory response involving activated immune cells like macrophages and increased production of cytokines (e.g., IL-1β, IL-6, TNF-α). This process leads to a systemic immune response that extends beyond the local pelvic area. Research suggests that this systemic inflammation can precede a clinical diagnosis, meaning elevated inflammatory markers like C-reactive protein and IL-6 may serve as early diagnostic indicators. The inflammatory cascade involves various cells (neutrophils, lymphocytes, monocytes, and platelets) which are the same cellular components used to calculate hematological inflammatory indices [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent research is focusing on accessible and cost-effective biomarkers for endometriosis, particularly those derived from routine complete blood count analyses. These markers are promising because they are widely available, inexpensive, provide rapid results, and reflect the systemic inflammatory burden and the complex immune processes of the disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Markers like the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) are promising for diagnosing endometriosis. These indices, which integrate multiple aspects of the inflammatory response, can achieve 76% sensitivity and 70% specificity when combined. This makes them a cost-effective alternative to expensive imaging modalities [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The biological plausibility of these markers as endometriosis biomarkers is supported by their reflection of the key pathophysiological processes underlying the disease: chronic inflammation, immune dysregulation, and enhanced coagulation cascades [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the diagnostic challenges of endometriosis, this study aimed to evaluate the utility of hematological inflammatory markers. The primary goals were to assess their ability to differentiate endometriomas from other benign ovarian cysts before surgery and to predict Pouch of Douglas involvement, thereby improving surgical planning and patient outcomes.\u003c/p\u003e"},{"header":"MATERIAL AND METHOD","content":"\u003cp\u003eThis retrospective study encompasses 480 patients who underwent surgery for benign ovarian cysts at Mersin University Hospital between January 1, 2004, and March 1, 2024. The patients included in the study were divided into two groups: those with endometrioma (n\u0026thinsp;=\u0026thinsp;321) and those with other benign ovarian cysts without endometrioma (n\u0026thinsp;=\u0026thinsp;159). Mersin University Hospital is a tertiary academic medical center serving as a regional referral hospital for gynecological conditions in the Mediterranean region of Turkey. The gynecology department receives both primary cases and complex referrals from surrounding healthcare facilities. This study was performed in line with the principles of the Declaration of Helsinki. The study protocol was approved by the Clinical Research Ethics Committee of Mersin University Rectorate (with the ethics committee decision numbered 2024/624 dated 10/07/2024). Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\u003cp\u003eThe demographic characteristics of the patients (age, gravida, parity, number of abortions), cyst sizes, and hematological inflammatory markers were analyzed. Among the hematological inflammatory markers are the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), Pan-immune inflammation value (PIV), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI).\u003c/p\u003e\u003cp\u003ePatients under 18 years of age and over 55 years of age, postmenopausal patients, those with pelvic inflammatory disease, tuberculosis or active infections, endocrine or immunological disorders, chronic lung, liver or kidney diseases, chronic inflammatory conditions, those taking anti-inflammatory medications, and individuals with a history of malignancy were excluded. All remaining patients, within one month prior to surgery, underwent a complete blood count test as part of the preoperative evaluation, ensuring that there were no active infections at the time of the blood test. Patients diagnosed with borderline or malignant diseases, excluding endometriosis, were excluded from the study if they were missing preoperative evaluations. A total of 480 patients were included in the study.\u003c/p\u003e\u003cp\u003eHemogram parameters are evaluated using the SYSMEX-XN-1000/23797 device, and 28 parameters are studied. Among these parameters, hemoglobin (g/dL), hematocrit (%), white blood cell (10^3/\u0026micro;L), neutrophil (10^3/\u0026micro;L), lymphocyte (10^3/\u0026micro;L), monocyte (10^3/\u0026micro;L), platelet (10^3/\u0026micro;L), and CA-125 were recorded, and these values were later obtained. The CA-125 (U/mL) value is measured using ELISA, Roche\u0026reg;.\u003c/p\u003e\u003cp\u003eMarkers; SII: (neutrophil count x platelet count)/lymphocyte count (10^9/L), SIRI: (neutrophil count x monocyte count)/lymphocyte count (10^9/L), NLR: neutrophil lymphocyte count/lymphocyte count (10^9/L), PIV: (neutrophil count x monocyte count x platelet count)/lymphocyte count (10^9/L), PLR: platelet count/lymphocyte count (10^9/L).\u003c/p\u003e\u003cp\u003eAll surgical procedures were performed by experienced gynecological surgeons at Mersin University Hospital. The surgical team consisted of gynecologists with specialized training in endometriosis surgery. The majority of procedures (n\u0026thinsp;=\u0026thinsp;183, 38.13%) were performed laparoscopically, while open surgical approaches were used in complex cases requiring extensive adhesiolysis or when laparoscopic access was contraindicated (n\u0026thinsp;=\u0026thinsp;297, 61.87%). For cases involving deep infiltrating endometriosis with suspected bowel or urological involvement, a multidisciplinary surgical team approach was employed, including consultation with colorectal surgeons and urologists as needed. Douglas involvement was assessed intraoperatively and confirmed through direct visualization and, when necessary, histopathological examination of excised tissue. Surgical techniques included cystectomy for endometriomas, with careful stripping of the cyst wall while preserving healthy ovarian tissue. Complete excision of endometriotic lesions was performed when feasible, with particular attention to Douglas pouch involvement, which was classified based on the extent of adhesions and implants in the rectovesical pouch.\u003c/p\u003e\u003cp\u003eGiven the extended study period spanning 20 years (2004\u0026ndash;2024), we acknowledge potential variations in diagnostic approaches, surgical techniques, and documentation standards over time. During the early years of the study period (2004\u0026ndash;2010), diagnostic imaging relied primarily on conventional ultrasonography and computed tomography scans, while MRI became more routinely available and utilized for endometriosis diagnosis from 2010 onwards. Similarly, laparoscopic surgical techniques and equipment underwent significant improvements, with high-definition cameras and advanced energy devices becoming standard practice after 2012.Cases with incomplete surgical documentation or inconsistent reporting were excluded from the analysis.\u003c/p\u003e\u003cp\u003eStatistical analyses were conducted using SPSS 22.0 and MedCalc 20.211 software. Comparisons between groups were made using the Student t-test or Mann-Whitney U test. Correlation analyses were conducted using Pearson or Spearman correlation coefficients. ROC curve analysis was used to evaluate diagnostic accuracy.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThis retrospective analysis investigated 480 individuals diagnosed with endometrioma (66.9%, n\u0026thinsp;=\u0026thinsp;321) and other benign ovarian cysts (33.1%, n\u0026thinsp;=\u0026thinsp;159). Among patients diagnosed with various benign ovarian disorders, 95 (19.8%) were identified as having serous cystadenoma, 43 (9.0%) as mucinous cystadenoma, and 21 (4.4%) as hemorrhagic cystadenoma. There was no significant difference between the groups regarding age (39.0 vs. 40.0 years; p\u0026thinsp;=\u0026thinsp;0.174) and cyst size (6.0 vs. 6.0 cm; p\u0026thinsp;=\u0026thinsp;0.067), however gravida (1.0 vs. 2.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and parity (1.0 vs. 2.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were considerably lower in the endometrioma group. CA-125 levels were considerably elevated in the endometrioma group relative to the control group (48.0 vs. 15.0 U/mL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Demographic, Biochemical, Hematological, and Inflammatory Markers in Endometrioma and Non-Endometrioma Benign Ovarian Cysts\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEndometrioma (n\u0026thinsp;=\u0026thinsp;321)\u003c/p\u003e\u003cp\u003emedian (min-max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-endometrioma (n\u0026thinsp;=\u0026thinsp;159)\u003c/p\u003e\u003cp\u003emedian (min-max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eDemographic and Biochemical Characteristics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39,0 (18,0\u0026ndash;50,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40,0 (18,0\u0026ndash;50,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGravida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,0 (0,0\u0026ndash;6,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,0 (0,0\u0026ndash;10,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,0 (0,0\u0026ndash;5,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,0 (0,0\u0026ndash;10,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbortar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,0 (0,0\u0026ndash;3,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,0 (0,0\u0026ndash;3,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA-125 (U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48,0 (4,0-1902,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15,0 (3,0-124,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyst Size (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,0 (3,0\u0026ndash;20,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,0 (3,0\u0026ndash;14,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHematological and Biochemical Parameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.90 (8,0\u0026ndash;14,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.7 (7,6\u0026ndash;15,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.0 (25,0\u0026ndash;43,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.0 (25,0\u0026ndash;46,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.1 (5,89\u0026thinsp;\u0026minus;\u0026thinsp;16,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.62 (4,10\u0026ndash;12,88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEUT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.51 (3,90\u0026thinsp;\u0026minus;\u0026thinsp;14,88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.58 (1.46\u0026ndash;9.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYMPH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,65 (0,27\u0026thinsp;\u0026minus;\u0026thinsp;5,70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,12 (0,52\u0026thinsp;\u0026minus;\u0026thinsp;4,06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMONO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,71 (0,10\u0026thinsp;\u0026minus;\u0026thinsp;1,33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,51 (0,14\u0026thinsp;\u0026minus;\u0026thinsp;1,13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e241,0 (102,0-712,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e285,0 (110,0-665,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInflammatory Markers\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1408,45 (183,64-12182,40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e624,0 (128,63-3235,85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,06 (0,22\u0026ndash;25,20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,11 (0,24\u0026thinsp;\u0026minus;\u0026thinsp;8,75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e921,08 (44,46-8251,25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e324,23 (47,59-1478,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,79 (0,9\u0026ndash;32,41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,09 (0,71\u0026thinsp;\u0026minus;\u0026thinsp;12,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166,47 (40,80\u0026ndash;864,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137,5 (51,71\u0026ndash;438,46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e*HBG (hemoglobina-g/dL), HTC (hematocrito-%), WBC (white blood cell-x10^3/\u0026micro;L), NEUT (neutrophil-x10^3/\u0026micro;L), LYMPH (lymphocyte-x10^3/\u0026micro;L), MONO (monocyte-x10^3/\u0026micro;L), PLT (plaqueta-x10^3/\u0026micro;L)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe endometrioma group exhibited reduced hemoglobin levels (10.90 vs. 12.7 g/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and hematocrit (33.0% vs. 38.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas leukocyte (11.1 vs. 7.62 x10\u0026sup3;/\u0026micro;L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and neutrophil (8.51 vs. 4.58 x10\u0026sup3;/\u0026micro;L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) counts were elevated. Lymphocyte (1.65 vs. 2.12 x10\u0026sup3;/\u0026micro;L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and platelet (241.0 vs. 285.0 x10\u0026sup3;/\u0026micro;L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) counts were reduced in the endometrioma cohort. The inflammatory indices NLR (5.79 vs. 2.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), PLR (166.47 vs. 137.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), SII (1408.45 vs. 624.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and SIRI (4.06 vs. 1.11; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were markedly elevated in the endometrioma cohort (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eROC evaluations of the indices utilized to distinguish the presence or absence of endometrioma indicate that all indicators exhibit substantial discriminatory capability. SIRI has been identified as the index with the highest AUC (AUC\u0026thinsp;=\u0026thinsp;0.894, Standard Error\u0026thinsp;=\u0026thinsp;0.0149, 95% Confidence Interval: 0.863\u0026ndash;0.920). The AUC values for the remaining indices are as follows: NLR AUC\u0026thinsp;=\u0026thinsp;0.875 (Standard Error\u0026thinsp;=\u0026thinsp;0.0165, 95% Confidence Interval: 0.843\u0026ndash;0.904), PIV AUC\u0026thinsp;=\u0026thinsp;0.858 (Standard Error\u0026thinsp;=\u0026thinsp;0.0166, 95% Confidence Interval: 0.824\u0026ndash;0.888), and SII AUC\u0026thinsp;=\u0026thinsp;0.830 (Standard Error\u0026thinsp;=\u0026thinsp;0.0185, 95% Confidence Interval: 0.793\u0026ndash;0.863) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC Values of Indices in the Differentiation of Endometrioma Presence/Absence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence of Endometrioma\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,830\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,0185\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,793-0,863\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,894\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,0149\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,863-0,920\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0,858\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0,0166\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0,824-0,888\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0,875\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0,0165\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0,843-0,904\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe ROC analysis results have established the best cutoff points for the indices, together with their sensitivity, specificity, and 95% confidence intervals. The established cutoff value for SIRI is \u0026le;\u0026thinsp;1.91, with a sensitivity of 86.79% (95% Confidence Interval: 80.5\u0026ndash;91.6) and a specificity of 81.31% (95% Confidence Interval: 76.6\u0026ndash;85.4). The threshold for SII was established at \u0026le;\u0026thinsp;939.25, exhibiting a sensitivity of 85.53% (95% Confidence Interval: 79.1\u0026ndash;90.6) and a specificity of 67.91% (95% Confidence Interval: 62.5\u0026ndash;73.0). The threshold for PIV was established at \u0026le;\u0026thinsp;476.91, exhibiting a sensitivity of 76.73% (95% Confidence Interval: 69.4\u0026ndash;83.1) and a specificity of 79.75% (95% Confidence Interval: 74.9\u0026ndash;84.0). The threshold for NLR was established at \u0026le;\u0026thinsp;3.49, exhibiting a sensitivity of 91.82% (95% Confidence Interval: 86.4\u0026ndash;95.6) and a specificity of 75.39% (95% Confidence Interval: 70.3\u0026ndash;80.0). The p-value for all indices was determined to be \u0026lt;\u0026thinsp;0.001 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe ROC analysis results of the indices\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriterion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSIRI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1,91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86,79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80,5\u0026ndash;91,6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81,31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76,6\u0026ndash;85,4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;939,25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85,53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79,1\u0026ndash;90,6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67,91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62,5\u0026ndash;73,0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;476,91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76,73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69,4\u0026ndash;83,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79,75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74,9\u0026ndash;84,0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;3,49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91,82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86,4\u0026ndash;95,6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75,39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70,3\u0026ndash;80,0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the analysis conducted, the data of 173 patients with Douglas involvement and 148 patients without involvement were evaluated for the relationship between the indices. Our analysis revealed that the endometrioma group with Douglas involment had significantly higher median levels of PLR [178.32 (59.48\u0026ndash;846.00) \u0026minus;\u0026thinsp;147.81 (40.80-864.15); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], PIV [1056.64 (58.67-8251.25) \u0026minus;\u0026thinsp;767.19 (44.46-5338.07); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], and SII [1529.74 (183.64-12182.40) \u0026minus;\u0026thinsp;1220.21 (251.28-9643.92); p\u0026thinsp;\u0026lt;\u0026thinsp;0.05] compared to the group without Douglas involvement (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between Douglas Involvement and SII, PIV, and PLR Levels\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDouglas involvement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;173)\u003c/p\u003e\u003cp\u003emedian (min-max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e\u003cp\u003emedian (min-max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1529,74 (183,64-12182,40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1220,21 (251,28-9643,92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1056,64 (58,67-8251,25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e767,19 (44,46-5338,07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e178,32 (59,48\u0026ndash;846,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147,81 (40,80\u0026ndash;864,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eROC curve study was performed to assess the diagnostic efficacy in predicting patients with Douglas involvement. The threshold value, sensitivity, and specificity for each index are as follows: PIV (\u0026le;\u0026thinsp;759.7; 65.92%; 49.37%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), PLR (\u0026le;\u0026thinsp;150.7; 65.92%; 51.27%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and SII (\u0026le;\u0026thinsp;1481.9; 53.63%; 60.76%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The AUC values for PLR, PIV, and SII were determined to be 0.580 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 0.567 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and 0.564 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;2). Upon examination of the data, it was noted that the values with the greatest ROC thresholds for differentiating patients with Douglas involvement were PLR, PIV, and SII.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSII, PIV and PLR ROC analyses in endometriosis Douglas involvement\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriterion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1481,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53,63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,0\u0026ndash;61,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60,76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52,7\u0026ndash;68,4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.0416\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;759,7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65,92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58,5\u0026ndash;72,8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49,37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41,3\u0026ndash;57,4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.0311\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePLR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;150,7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65,92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58,5\u0026ndash;72,8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51,27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43,2\u0026ndash;69,3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.0105\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe results emphasised that inflammatory markers are essential in clinical practice during the preoperative phase for the differential diagnosis of endometrioma and other benign ovarian cysts, particularly in predicting patients with and without Douglas involvement. Upon comparison of patient values, we observed markedly elevated counts of neutrophils, lymphocytes, monocytes, and platelets. The results suggest that hematological inflammatory markers, specifically SIRI, SII, PIV, and NLR, may be significant in the differential diagnosis of endometrioma and other benign ovarian cysts.\u003c/p\u003e\u003cp\u003eInflammation is pivotal in the etiology of endometriosis, since the ectopic endometrial tissue within endometriomas induces chronic inflammation and the secretion of cytokines and chemokines in the pelvic milieu [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The research by Cho and associates revealed that patients with endometriomas exhibited reduced hemogram and hematocrit values, although white blood cell and neutrophil counts were elevated [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings suggest that the inflammatory response may be more pronounced in patients with endometrioma [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In our study, we found that hemoglobin and hematocrit values were lower, and due to the inflammatory process, WBC and neutrophil counts were statistically significantly higher.\u003c/p\u003e\u003cp\u003eIn 2019, Wu and colleagues reported that in a routine population study in China, the average NLR value in women was 1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64 and the PLR was 108.02\u0026thinsp;\u0026plusmn;\u0026thinsp;32.99 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. But despite this, a study found that the NLR values in patients diagnosed with endometriosis were higher than the specified value [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This finding indicates that systemic inflammation is more pronounced in patients with endometrioma. In our study, it was also shown that NLR is an effective marker in distinguishing endometrioma. In the ROC analysis, the AUC for NLR was found to be 0.875. This value indicates that NLR has good diagnostic accuracy in the diagnosis of endometrioma. Platelets can exacerbate inflammation by inducing the production of inflammatory mediators and activating endothelial cells. The increase in platelet count in women with endometriosis may be a result of the inflammatory process. In the study conducted by Kalem and colleagues [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] with 213 patients, it was found that the PLR ratio, like the NLR, was significantly higher in patients with endometriomas compared to those with benign ovarian cysts, consistent with our study. The values found in our study are higher than the values found and are consistent with the literature.\u003c/p\u003e\u003cp\u003eSII is considered a marker that reflects both the severity of inflammation and the suppression of cellular immunity. In a study conducted on healthy women, the SII value was reported as 526.7\u0026thinsp;\u0026plusmn;\u0026thinsp;336.6, which is lower than the data obtained in our study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. When examining studies related to the topic, it has been reported that the SII value in the endometrioma group was statistically significant and high [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This also shows us that it can be used as an inflammatory index and in addition to clinical examination and imaging in the diagnosis of endometriosis.\u003c/p\u003e\u003cp\u003eThe high SIRI in patients with endometrioma indicates that the inflammation is more severe. A study by Zhou et al. compared patients with endometriosis to a control group, revealing that SIRI, SII, PIV, and NLR were considerably elevated in patients with endometriomas relative to the control group [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. After the evaluation, it has been shown that the SIRI index can be used to predict the severity of inflammation and can assist in the diagnosis of endometrioma when used alongside other biomarkers\u003c/p\u003e\u003cp\u003ePIV reflects platelet activation and inflammation. The high PIV in patients with endometrioma indicates that, in addition to inflammation, platelet activation is also increased. Gasparyan and colleagues [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], who analyzed platelet indices and inflammatory indices, emphasized that the platelet indices from 45 studies on rheumatic diseases significantly indicated inflammation. In the endometriosis study, Moini and colleagues [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] found that the ratios of neutrophils, monocytes, platelets, and lymphocytes were statistically significant. In a study by Lukacs and colleagues [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], on the number of monocytes and neutrophils during the preoperative and postoperative periods, a decrease in monocytes was reported in the postoperative period. This situation highlighted that monocytes returned to normal levels after the removal of endometriosis lesions and emphasized the importance of phagocytic cells in endometriosis. This situation, in parallel with our study, shows that in endometriosis, monocytes increase in number instead of platelets and lymphocytes for the humoral immune response.\u003c/p\u003e\u003cp\u003eIn clinical practice, the use of diagnostic tests for the presence or absence of disease is important. Determining threshold values in ROC analyses will facilitate diagnosis to evaluate diagnostic accuracy. A study investigated PLR and NLR levels for the differential diagnosis of endometrioma and other benign ovarian cysts. ROC analyses were performed, revealing an AUC of 0.59 for PLR, with a sensitivity of 56% and a specificity of 62.5%. The AUC for NLR was 0.56, with a sensitivity of 32.9% and a specificity of 80.2% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This study employed ROC curve analysis to assess the potential utility of hematological inflammatory markers in the differential diagnosis of endometrioma vs other benign ovarian cysts. ROC study indicated that SIRI exhibits the highest diagnostic accuracy in differentiating endometrioma from other benign ovarian cysts (AUC: 0.894; 95% Confidence Interval: 0.863\u0026ndash;0.920, sensitivity 86.79%, and specificity 81.31%). Confidence interval: 0.863\u0026ndash;0.920, sensitivity found to be 86.79% and specificity 81.31%. Values above the threshold determined for SIRI indicate a high probability of an endometrioma diagnosis. When the results are evaluated, an AUC of 0.8 and above indicates good diagnostic accuracy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this case, other hematological inflammatory markers such as SII, PIV, and NLR also had significant AUC values (0.830, 0.858, and 0.875, respectively), suggesting their potential roles in the diagnosis of endometrioma. These findings suggest that hematological inflammatory markers, particularly SIRI, could be used as non-invasive adjunctive tools in the diagnosis of endometrioma. However, further research is needed for the clinical use of these markers.\u003c/p\u003e\u003cp\u003eThe distinguishing factor of our study is the computation of threshold values via ROC analysis to assess the prediction capability of evaluating patients with Douglas involvement in endometriosis. Analysis of the data revealed that SII, PIV, and PLR were effective indicators of Douglas involvement. Upon analysis of the indices, the p-values for PLR, PIV, and SII were determined to be significant and measured at 0.011, 0.019, and 0.033, respectively. The AUC values were 0.0582 for PLR, 0.0576 for PIV, and 0.569 for SII. The greatest values for positive likelihood ratio (PLR) were observed at 65.3 and 54.1 for sensitivity and specificity, respectively. These results suggest that if PLR and other indices are specifically examined beforehand for a patient with endometriosis, the Douglas involvement can be predicted before the operation. Wang et al. evaluated patients with endomeriosis stage IV and found that NLR was statistically significant and elevated [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Similarly, Jing and colleagues concluded that NLR can be used as a marker in stage III/IV patients [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In our research, unlike the studies conducted, the Douglas involvement was examined in all patients with endometriosis, not just in the advanced stages. Additionally, no threshold values have been identified in terms of diagnostic value in the conducted studies. In our study, however, the PLR, PIV, and SII parameters may be valuable in predicting Douglas involvement. These results show us that, in addition to imaging, indices can be used to demonstrate Douglas involvement. The choice of surgical procedure for the patient can allow for preparations to be made in advance by anticipating complications that may arise in the intestines, bladder, or pelvic side wall due to adhesions found in the Douglas before the operation. This can help ensure the necessary safety for the patient before the operation, provide the necessary information to the patient, and make the outcomes more controlled and less harmful.\u003c/p\u003e\u003cp\u003eDespite these promising results, our study has some limitations. The retrospective design of our study represents a significant limitation that prevents the establishment of causal relationships between hematological inflammatory markers and endometrioma diagnosis. This design inherently limits our ability to determine whether elevated inflammatory markers are a cause or consequence of endometriosis, and prevents us from establishing temporal relationships between marker elevation and disease progression. Additionally, retrospective analysis may introduce selection bias and information bias, as we relied on previously recorded data without controlling for confounding variables that could influence inflammatory marker levels, such as concurrent infections, stress, medication use, or other inflammatory conditions that may not have been adequately documented. Secondly, our analysis focused solely on hematological inflammatory markers, potentially overlooking other relevant biomarkers were not alayzed or compared. The study's single-center design may restrict the generalizability of our results. As this study was conducted at a single tertiary care center that serves as a regional referral hospital, our patient population may represent more complex or severe cases of endometriosis compared to community-based populations, which could potentially limit the generalizability of our findings to primary care settings or less severe disease presentations. The surgical procedures were performed by a limited number of experienced surgeons at a single center, which, while ensuring consistency in surgical technique and Douglas involvement assessment, may limit the external validity of our findings to centers with different surgical expertise or protocols. The 20-year study span represents both a strength and limitation of our research. While it provides a large sample size and diverse case representation, it introduces potential temporal bias due to evolving diagnostic criteria, surgical techniques, and documentation standards. Changes in imaging technology, laboratory methods, and surgical expertise over two decades may have influenced case selection, disease staging accuracy, and outcome reporting. Although our subgroup analysis showed consistent inflammatory marker patterns across time periods, we cannot completely exclude the possibility that temporal changes in patient selection or diagnostic precision may have influenced our findings. However, our study also has several strengths. The study included a large sample size of 480 patients and evaluated various hematological inflammatory markers, including SIRI, SII, PIV, and NLR, which have not been extensively investigated in previous research. By determining threshold values for these markers using ROC analysis, we have provided valuable information for their potential clinical application.\u003c/p\u003e\u003cp\u003eTo overcome the limitations of retrospective analysis, future prospective cohort studies should follow patients longitudinally, measuring inflammatory markers before, during, and after endometriosis development. Additionally, randomized controlled trials examining these markers in combination with established diagnostic modalities would strengthen their clinical utility and predictive value.\u003c/p\u003e\u003cp\u003eWhile imaging remains the gold standard for endometrioma diagnosis, hematological inflammatory markers, particularly SIRI, offer valuable complementary information in specific clinical contexts. Their primary utility lies in rapid screening, preoperative risk stratification for Douglas involvement, and enhancing diagnostic confidence when combined with imaging findings. These cost-effective, widely available biomarkers could improve clinical decision-making, particularly in resource-limited settings or when rapid triage is required. However, they should be viewed as adjunctive tools that enhance rather than replace current diagnostic approaches.The study specifically indicated that SIRI could significantly contribute to the differential diagnosis of endometrioma vs other benign ovarian cysts, enhance the probability of identifying endometrioma, and that the PLR value could serve as a predictive tool for individuals with Douglas involvement.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeutrophil-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePIV\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePan-immune inflammation value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic immune-inflammation index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSIRI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic inflammation response index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eA receiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMRI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMagnetic resonance imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e\u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. The study protocol was approved by the Clinical Research Ethics Committee of Mersin University Rectorate (with the ethics committee decision numbered 2024/624 dated 10/07/2024). Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHY, KA and GU developed the concept and were responsible for data collection. FGY, HY and HA planned the study. HY, GU and KA analysed the results. HA and FGY wrote the manuscript text and prepared figures and tables. All authors reviewed the manuscript for important intellectual content and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003cp\u003e\u003cb\u003eAuthors' information\u003c/b\u003e: Not applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBorgfeldt C, Andolf E. Transvaginal sonographic ovarian findings in a random sample of women 25\u0026ndash;40 years old. Ultrasound Obstet Gynecology: Official J Int Soc Ultrasound Obstet Gynecol. 1999;13(5):345\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePavlik EJ, Ueland FR, Miller RW, Ubellacker JM, DeSimone CP, Elder J, Hoff J, Baldwin L, Kryscio RJ, van Nagell JR Jr. Frequency and disposition of ovarian abnormalities followed with serial transvaginal ultrasonography. Obstet Gynecol. 2013;122(2 PART 1):210\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarghaly S. Current diagnosis and management of ovarian cysts. Clin Exp Obstet Gynecol. 2014;41(6):609\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLou Y, Li D, Yu J, Chen J, Jin X. Diagnostic performance of transvaginal sonography vs. magnetic resonance imaging for rectovaginal septum deep infiltrating endometriosis: a head-to-head comparative meta-analysis. Clin Radiol. 2024;79(8):618\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalman S, Shireen N, Riyaz R, Khan SA, Singh JP, Uttam A. Magnetic resonance imaging evaluation of gynecological mass lesions: A comprehensive analysis with histopathological correlation. Medicine. 2024;103(32):e39312.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMitranovici M-I, Costachescu D, Voidazan S, Munteanu M, Buicu C-F, Oală IE, Ivan V, Apostol A, Melinte IM, Crisan A. Exploring the Shared Pathogenesis Mechanisms of Endometriosis and Cancer: Stemness and Targeted Treatments of Its Molecular Pathways\u0026mdash;A Narrative Review. Int J Mol Sci. 2024;25(23):12749.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho YJ, Kim HY. Oxidative stress and endometriosis. Kosin Med J. 2018;33(2):135\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang D, Nie X. Effect of endometrioma and its surgical excision on fertility. Experimental therapeutic Med. 2020;20(5):114.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchneyer RJ, Hamilton KM, Meyer R, Nasseri YY, Siedhoff MT. Surgical treatment of colorectal endometriosis: an updated review. Curr Opin Obstet Gynecol. 2024;36(4):239\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePattanasri M, Ades A, Nanayakkara P. Correlation between ultrasound findings and laparoscopy in prediction of deep infiltrating endometriosis (DIE). Aust N Z J Obstet Gynaecol. 2020;60(6):946\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAs-Sanie S, Mackenzie SC, Morrison L, Schrepf A, Zondervan KT, Horne AW. Missmer SA: Endometriosis: a review. \u003cem\u003eJama\u003c/em\u003e 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMu F, Harris HR, Rich-Edwards JW, Hankinson SE, Rimm EB, Spiegelman D, Missmer SA. A prospective study of inflammatory markers and risk of endometriosis. Am J Epidemiol. 2018;187(3):515\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuan Y-N, Peng Y-Q, Xu X, Shi X-L, Peng C-X. Positive correlation between NLR and PLR in 10,458 patients with endometriosis in reproductive age in China. Eur Rev Med Pharmacol Sci 2023, 27(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Y, Liu G, Yuan L, Qiao Y, Chen Q. Evaluating systemic immune-inflammation indices as predictive markers for endometriosis diagnosis: A retrospective observational study. J Reprod Immunol. 2025;167:104416.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W, Zeng W, Yang S. A stacked machine learning-based classification model for endometriosis and adenomyosis: a retrospective cohort study utilizing peripheral blood and coagulation markers. Front Digit health. 2024;6:1463419.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho H-Y, Park S-T, Park S-H. Red blood cell indices as an effective marker for the existence and severity of endometriosis (STROBE). Medicine. 2022;101(42):e31157.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBj\u0026ouml;rk E, Vinnars MT, Nagaev I, Nagaeva O, Lundin E, Ottander U, Mincheva-Nilsson L. Enhanced local and systemic inflammatory cytokine mRNA expression in women with endometriosis evokes compensatory adaptive regulatory mRNA response that mediates immune suppression and impairs cytotoxicity. Am J Reprod Immunol. 2020;84(4):e13298.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu L, Zou S, Wang C, Tan X, Yu M. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio in Chinese Han population from Chaoshan region in South China. BMC Cardiovasc Disord. 2019;19:1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Y, Liu G, Yuan L, Qiao Y, Chen Q. Evaluating systemic immune-inflammation indices as predictive markers for endometriosis diagnosis: A retrospective observational study. J Reprod Immunol. 2025;167:104416.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalem Z, Şimşir \u0026Ccedil;, Bakırarar B, Kalem MN. The additional diagnostic value of NLR and PLR for CA-125 in the differential diagnosis of endometrioma and benign ovarian cysts in women of reproductive age: a retrospective case-control study. Eur Res J 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQing G, He H, Lai M, Li X, Chen Y, Wei B. Systemic immune-inflammatory index and its association with female sexual dysfunction, specifically low sexual frequency, in depressive patients: Results from NHANES 2005 to 2016. Medicine. 2024;103(22):e38151.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGasparyan AY, Ayvazyan L, Mukanova U, Yessirkepov M, Kitas GD. The platelet-to-lymphocyte ratio as an inflammatory marker in rheumatic diseases. Annals Lab Med. 2019;39(4):345\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoini A, Ghanaat M, Hosseini R, Rastad H, Hosseini L. Evaluating hematological parameters in women with endometriosis. J Obstet Gynaecol. 2021;41(7):1151\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuk\u0026aacute;cs L, Kov\u0026aacute;cs AR, P\u0026aacute;l L, Szűcs S, K\u0026ouml;v\u0026eacute;r \u0026Aacute;, Lamp\u0026eacute; R. Phagocyte function of peripheral neutrophil granulocytes and monocytes in endometriosis before and after surgery. J Gynecol Obstet Hum Reprod. 2021;50(4):101796.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJing X, Li C, Sun J, Peng J, Dou Y, Xu X, Ma C, Dong Z, Liu Y, Zhang H. Systemic inflammatory response markers associated with infertility and endometrioma or uterine leiomyoma in endometriosis. Ther Clin Risk Manag 2020:403\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuller MP, Tomlinson G, Marrie TJ, Tang P, McGeer A, Low DE, Detsky AS, Gold WL. Can routine laboratory tests discriminate between severe acute respiratory syndrome and other causes of community-acquired pneumonia? Clin Infect Dis. 2005;40(8):1079\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L, Ling J, Zhu X, Zhang Y, Li R, Huang J, Huang D, Wu C, Zhou H. The coagulation status in women of endometriosis with stage IV. BMC Womens Health. 2024;24(1):386.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Benign ovarian cysts, Endometrioma, ovarian cysts, Hematological inflammatory markers, Douglas invasion","lastPublishedDoi":"10.21203/rs.3.rs-7623083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7623083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study sought to examine the function of hematological inflammatory markers in differentiating endometrioma from other benign ovarian cysts prior to surgery and in individuals with Douglas involvement.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e\u003cp\u003eThe study was designed retrospectively and included 480 patients (321 endometrioma, 153 non-endometrioma) who were operated on for benign ovarian cysts at Mersin University Hospital between January 1, 2004, and March 1, 2024. Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), Pan-immune inflammation value (PIV), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) were analyzed as hematological inflammatory markers. Additionally, the indices were evaluated in patients with Douglas involvement, and ROC analysis was conducted to determine their predictive power.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe average age of patients with endometrioma did not show a significant difference compared to the average of patients with other benign ovarian cysts (39.0 vs. 40.0 years, p\u0026thinsp;=\u0026thinsp;0.174). NLR, PLR, SII, and SIRI were significantly higher in the endometrioma group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). SIRI had the highest value (0.894) in distinguishing endometrioma from other benign ovarian cysts. Douglas involvement was found to be significantly high with SII, PIV, and PLR having AUCs of 0.569, 0.576, and 0.0582, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHematological inflammatory markers, particularly SIRI, were found to be useful in distinguishing endometriomas from other benign ovarian cysts, and PLR was identified as a potential non-invasive biomarker when analyzing its predictive power in patients with Douglas involvement.\u003c/p\u003e","manuscriptTitle":"Association of Systemic Inflammatory Markers With Endometrioma Diagnosis and Douglas Involvement: A Retrospective Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-24 02:15:11","doi":"10.21203/rs.3.rs-7623083/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-06-01T08:54:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T13:48:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214134672002213253448636926121835873719","date":"2025-10-21T20:24:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315318134728669865482199829234729518693","date":"2025-10-16T14:30:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T10:44:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-17T11:16:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T07:47:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T07:46:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-09-15T17:10:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b045af27-a42b-4431-82ef-cbff2a5df200","owner":[],"postedDate":"October 24th, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-06-01T08:54:07+00:00","index":55,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-24T02:15:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-24 02:15:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7623083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7623083","identity":"rs-7623083","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}