Atypical Endometriosis as a Premalignant Histological Lesion in Endometriosis-Associated Ovarian Cancer: Clinical, Imaging, Molecular Characterization and Proposal for Risk Stratification

Atypical Endometriosis as a Premalignant Histological Lesion in Endometriosis-Associated Ovarian Cancer: Clinical, Imaging, Molecular Characterization and Proposal for Risk Stratification | 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 Atypical Endometriosis as a Premalignant Histological Lesion in Endometriosis-Associated Ovarian Cancer: Clinical, Imaging, Molecular Characterization and Proposal for Risk Stratification Rocio Sánchez-Gómez, Sonia Soler-Gabaldon, Celia Gomez-Meseguer, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9723381/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background To develop a risk stratification model for malignant progression in endometriosis-associated ovarian cancer (EAOC), while determining the prevalence of atypical endometriosis (AE) and characterizing its clinical, imaging, and molecular profile. Methods Prospective observational cohort study of 334 patients (January 2019–July 2022) at two tertiary hospitals in Murcia, Spain. Patients were classified into three groups: endometriosis (n = 236), EAOC (n = 27), and non-endometriosis-associated ovarian cancer (n = 41). Clinical variables, ultrasound features (GI-RADS classification), tumor markers (CA-125, HE4, CEA, ROMA index), and immunohistochemical markers (ARID1A/BAF-250a, Ki-67, PTEN, COX-2) were analyzed. Binary logistic regression identified independent predictors of EAOC. Results Global AE prevalence was 10.45%, increasing to 40.74% in EAOC versus 5.75% in isolated endometriosis (p 3 (OR ≈ 48.0), and AE presence (OR ≈ 25.8) as independent predictors (AUC = 0.94; 95%CI: 0.80–0.99; sensitivity 93.8%, specificity 87.5%). ARID1A loss occurred in 72.73% of AE with EAOC versus 10% without EAOC (p = 0.008). Five-year survival was 100% for EAOC versus 60.44% for non-EAOC (p < 0.001). Conclusion AE represents a true premalignant lesion with distinct molecular signatures. Integration of menopausal status, structured ultrasound, and molecular markers enables effective risk stratification and supports individualized surveillance protocols for early EAOC detection. Atypical endometriosis Endometriosis-associated ovarian cancer ARID1A Risk stratification GI-RADS Premalignant lesion Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Endometriosis affects approximately 10% of women of reproductive age and is characterized by the presence of endometrial-like tissue outside the uterine cavity [ 1 ]. While traditionally considered a benign condition, accumulating evidence demonstrates that endometriosis, particularly ovarian endometriosis, is associated with an increased risk of ovarian cancer [ 2 ]. Endometriosis-associated ovarian cancer (EAOC) accounts for approximately 10–15% of all epithelial ovarian cancers, with clear cell carcinoma (CCC) and endometrioid adenocarcinoma (EAC) being the predominant histological subtypes [ 3 ]. The pathogenesis of EAOC is thought to involve a stepwise progression from typical endometriosis through atypical endometriosis (AE) to invasive carcinoma, analogous to the endometrial hyperplasia–carcinoma sequence in endometrial cancer [ 4 ]. AE is characterized by cytological atypia (nuclear enlargement, hyperchromasia, increased nuclear-to-cytoplasmic ratio) and/or architectural atypia (epithelial stratification, tufting, cribriform patterns) in endometriotic lesions [ 5 ]. However, the clinical significance of AE remains debated, with reported prevalence rates varying widely from 1% to 61% depending on diagnostic criteria and study populations [ 6 ]. Recent molecular studies have identified key genetic alterations in the endometriosis-to-cancer continuum. Loss of ARID1A (AT-rich interaction domain 1A), a tumor suppressor gene encoding the BAF-250a protein involved in chromatin remodeling, has been identified in 40–57% of EAOC cases, particularly CCC and EAC subtypes [ 7 ]. Additionally, elevated Ki-67 proliferative index and altered PTEN expression have been associated with neoplastic transformation in endometriotic lesions [ 8 ]. Despite these advances, no validated clinical model integrating clinical, imaging, and molecular markers for AE risk stratification has been established. This study aimed to: (1) determine the prevalence of AE in a prospective cohort of patients with endometriosis and ovarian cancer; (2) characterize the clinical, echographic, tumor marker, and immunohistochemical profile of AE; (3) identify independent predictors of EAOC; and (4) propose a structured risk stratification model for clinical management of AE. METHODS Study Design and Setting This was a prospective observational cohort study conducted at the Gynecology and Obstetrics Services of Hospital General Universitario Reina Sofía (HGURS) and Hospital Clínico Universitario Virgen de la Arrixaca (HUVA), Murcia, Spain, between January 2019 and July 2022. The study received institutional ethics committee approval, and all participants provided written informed consent. Study Population A total of 334 patients with an anatomopathological diagnosis of endometriosis and/or ovarian cancer were enrolled. Patients were classified into three groups: (1) endometriosis without malignancy (n = 236); (2) EAOC (n = 27); and (3) non-endometriosis-associated ovarian cancer (COnoAE, n = 41). Inclusion criteria were: age ≥ 18 years, signed informed consent, and histopathological diagnosis of ovarian endometriosis or ovarian cancer. Exclusion criteria were: cognitive impairment, surgery not performed, absence of informed consent, benign ovarian tumors, and no ovarian pathology found at surgery. Clinical and Imaging Assessment All patients underwent preoperative transvaginal ultrasound with GI-RADS (Gynecologic Imaging Reporting and Data System) classification. Maximum cyst diameter was measured. Serum tumor markers were determined preoperatively: CA-125 and CA 19 − 9 by chemiluminescent microparticle immunoassay (CLIA; Beckman Coulter Access® system); HE4 and CEA by electrochemiluminescence immunoassay (ECLIA; cobas e® system). The ROMA (Risk of Ovarian Malignancy Algorithm) index was calculated from HE4 and CA-125 values adjusted for menopausal status. Histopathological and Immunohistochemical Analysis Surgical specimens were reviewed by two experienced gynecological pathologists. Representative endometriotic lesions and areas of transition to adenocarcinoma were embedded in paraffin and stained with hematoxylin and eosin (H&E). AE was defined as endometriosis with cytological atypia (nuclear enlargement, hyperchromasia, pleomorphism) and/or architectural atypia (glandular crowding, cribriform patterns, complex papillary structures). Immunohistochemical analysis was performed using automated DAKO systems for: ARID1A/BAF-250a (loss defined as > 95% absence of nuclear staining in epithelial cells); Ki-67 (percentage of positive nuclei); PTEN (loss or marked reduction of cytoplasmic/nuclear staining); and COX-2 (cytoplasmic immunoreactivity in epithelial cells). In Fig. 4, some of these immunohistological analyses are shown. Statistical Analysis Normality of quantitative variables was assessed by the Shapiro-Wilk test. Normally distributed variables were expressed as mean ± standard deviation and compared by Student's t-test or one-way ANOVA. Non-normally distributed variables were expressed as median (interquartile range) and compared by Kruskal-Wallis test. Categorical variables were compared by Pearson chi-squared test or Fisher's exact test. Prevalence and 95% confidence intervals (CI) were estimated by Wilson's method. Binary logistic regression identified independent predictors of EAOC. Survival and recurrence analyses used Kaplan-Meier estimator with log-rank test. Discriminative performance was assessed by area under the receiver operating characteristic curve (AUC). Statistical significance was set at p < 0.05. Analyses were performed using SPSS v.26.0 (IBM Corp.). RESULTS Prevalence of Atypical Endometriosis Of 334 patients enrolled, AE was identified in 21 cases, yielding a global prevalence of 10.45% (95%CI: 6.9–15.5%). AE was significantly more frequent in EAOC (40.74%) compared to isolated endometriosis (5.75%) (p < 0.001). The predominant histological pattern was cytological atypia; however, architectural atypia showed a more consistent association with malignant progression. Clinical and Epidemiological Characteristics EAOC patients exhibited an intermediate clinical profile between endometriosis and non-EAOC ovarian cancer. Mean age differed significantly across groups: endometriosis 41.47 years, EAOC 51.23 years, and COnoAE 56.66 years (p < 0.001). Postmenopausal status was present in 8.55% of endometriosis, 52% of EAOC, and 70.73% of COnoAE patients (p < 0.0001). No significant differences were observed in BMI or parity. Clinical and epidemiological characteristics are summarized in Table 1 . Table 1 Clinical and epidemiological characteristics of the study cohort (n = 334) Variable Endometriosis (n = 236) EAOC (n = 27) COnoAE (n = 41) p-value Mean age (years) 41.47 ± SD 51.23 ± SD 56.66 ± SD < 0.001 Postmenopausal (%) 8.55% 52% 70.73% < 0.0001 Atypical endometriosis (%) 5.75% 40.74% N/A < 0.001 Cytological atypia (%) Predominant Present N/A N/S Architectural atypia (%) Less frequent Present N/A N/S BMI No significant difference between groups N/S Parity No significant difference between groups N/S EAOC: endometriosis-associated ovarian cancer; COnoAE: non-endometriosis-associated ovarian cancer; N/S: not significant; SD: standard deviation. Imaging and Tumor Markers GI-RADS classification differed significantly across groups (p < 0.001): endometriosis cases were predominantly GI-RADS 3 (71.21%), while 81.82% of EAOC and 62.96% of COnoAE were GI-RADS 5. Overall, 95% of EAOC and COnoAE patients were GI-RADS 4–5. Median maximum cyst diameter was 53 mm (endometriosis), 93 mm (EAOC), and 106 mm (COnoAE) (p < 0.001); a cutoff of 7.2 cm showed moderate discriminative capacity (AUC = 0.74, p < 0.001). Median HE4 was 50 pmol/L (endometriosis), 72 pmol/L (EAOC), and 103 pmol/L (COnoAE) (p < 0.001), with elevated HE4 in 30.77%, 68.42%, and 75% of cases, respectively. Median CA-125 was 36.15 UI/mL (endometriosis), 32.45 UI/mL (EAOC), and 105.55 UI/mL (COnoAE) (p < 0.001). ROMA high-risk classification was present in 24.73% of endometriosis, 28.57% of EAOC, and 65.12% of COnoAE patients (p < 0.001). Imaging and tumor marker data are shown in Table 2 . Table 2 Imaging and tumor marker characteristics by group Parameter Endometriosis (n = 236) EAOC (n = 27) COnoAE (n = 41) p-value GI-RADS 3 (%) 71.21% ~ 10% ~ 20% < 0.001 GI-RADS 4–5 (%) ~ 28% 95% 95% < 0.001 Median cyst diameter (mm) 53 93 106 < 0.001 Cyst diameter cutoff (7.2 cm) AUC = 0.74 for endometrioma vs EAOC < 0.001 Median CA-125 (UI/mL) 36.15 32.45 105.55 < 0.001 Elevated CA-125 (%) 57.24% 71.77% 53.85% 0.025 Median HE4 (pmol/L) 50 72 103 < 0.001 Elevated HE4 (%) 30.77% 68.42% 75% < 0.001 Median CEA (ng/mL) 0.8 1.1 1.2 0.011 ROMA high-risk (%) 24.73% 28.57% 65.12% < 0.001 HE4: human epididymis protein 4; ROMA: Risk of Ovarian Malignancy Algorithm; GI-RADS: Gynecologic Imaging Reporting and Data System; AUC: area under the curve. Immunohistochemical Findings Loss of ARID1A/BAF-250a expression was significantly more frequent in AE (42.86%) compared to typical endometriosis (ET, 6.52%) (p = 0.001). When AE was associated with EAOC, ARID1A loss reached 72.73%, compared to 10% in AE without EAOC (p = 0.008). Elevated Ki-67 was present in 75% of AE versus 25% of ET (p = 0.020). PTEN showed a heterogeneous pattern, predominantly observed in endometrioid carcinoma and cytological AE, with no statistically significant overall difference. COX-2 expression did not significantly differ between groups. Immunohistochemical results are presented in Table 3 . Table 3 Immunohistochemical marker expression in atypical endometriosis (AE) versus typical endometriosis (ET) Marker Typical Endometriosis (ET) Atypical Endometriosis (AE) AE with EAOC AE without EAOC p-value ARID1A/BAF-250a loss 6.52% 42.86% 72.73% 10% 0.001 / 0.008 Elevated Ki-67 25% 75% Elevated Lower 0.020 PTEN loss/alteration Variable Variable Predominant in EAC Heterogeneous N/S COX-2 positivity Present Present Present Present N/S ARID1A: AT-rich interaction domain 1A; EAOC: endometriosis-associated ovarian cancer; EAC: endometrioid adenocarcinoma; N/S: not significant. Multivariate Analysis and Risk Stratification Model Binary logistic regression identified three independent predictors of EAOC: postmenopausal status (OR ≈ 28.8; 95%CI: [to be specified]), GI-RADS > 3 (OR ≈ 48.0; 95%CI: [to be specified]), and presence of AE (OR ≈ 25.8; 95%CI: [to be specified]). The model demonstrated excellent discriminative capacity (AUC = 0.94; 95%CI: 0.80–0.99), correctly classifying 90.6% of cases (sensitivity 93.8%, specificity 87.5%). Two clinical probability thresholds were defined: a low-threshold (≥ 0.2) for triage with high sensitivity, and a high-threshold (≥ 0.5) for diagnostic confirmation with high specificity. Model performance is summarized in Table 4 and Fig. 3 . Table 4 Multivariate logistic regression model for prediction of EAOC Variable Odds Ratio (OR) 95% CI p-value Postmenopausal status ≈ 28.8 26.80–32.9 3 ≈ 48.0 44.30–54.9 < 0.001 Atypical endometriosis (AE) ≈ 25.8 21.80–29.9 < 0.001 Model AUC 0.94 0.80–0.99 < 0.001 Sensitivity (cutoff ≥ 0.5) 93.8% — — Specificity (cutoff ≥ 0.5) 87.5% — — Overall correct classification 90.6% — — AUC: area under the curve; CI: confidence interval; GI-RADS: Gynecologic Imaging Reporting and Data System. Survival and Recurrence Outcomes Five-year overall survival was 100% for EAOC versus 60.44% for COnoAE (p < 0.001). CCC and EAC subtypes showed particularly favorable outcomes when associated with endometriosis, with 5-year survival reaching 100%. The median disease-free interval was 2.37 years. COnoAE showed a significantly higher recurrence rate (n = 32, 20.13%) compared to EAOC (n = 1). Only 6.77% of COnoAE patients achieved a disease-free interval exceeding 5 years, compared to 22.22% of EAOC patients (p < 0.001). Kaplan-Meier survival curves are shown in Fig. 1 . DISCUSSION This prospective cohort study provides comprehensive evidence that AE is a true premalignant lesion in the endometriosis-to-ovarian cancer continuum. The global AE prevalence of 10.45%, rising to 40.74% in EAOC, is consistent with previous reports and underscores the clinical relevance of systematic histopathological evaluation of endometriotic specimens [ 9 , 10 ]. The intermediate clinical profile of EAOC patients — older age and higher postmenopausal prevalence than endometriosis, yet younger and with more early-stage disease than COnoAE — supports the concept of EAOC as a distinct biological entity with different carcinogenesis pathways [ 11 ]. This has important implications for clinical management, as EAOC may require different surveillance and treatment strategies compared to sporadic ovarian cancer. The utility of structured ultrasound (GI-RADS) was confirmed, with 95% of malignant cases classified as GI-RADS 4–5. The tumor size cutoff of 7.2 cm (AUC = 0.74) provides a practical preoperative parameter for malignancy suspicion. HE4 demonstrated superior specificity for malignancy compared to CA-125, whose interpretation is confounded by the inflammatory nature of endometriosis [ 12 ]. These findings align with current ESHRE guidelines recommending HE4 and ROMA index in the preoperative assessment of adnexal masses [ 13 ]. The molecular analysis revealed ARID1A/BAF-250a loss as the most robust marker of neoplastic progression, present in 72.73% of AE with concurrent EAOC. This is consistent with the established role of ARID1A mutations in CCC and EAC carcinogenesis, where ARID1A loss is thought to be an early event facilitating immune evasion and oncogenic progression [ 14 ]. The heterogeneous PTEN pattern observed supports subtype-specific carcinogenesis pathways within EAOC [ 15 ]. The multivariate model integrating menopausal status, GI-RADS > 3, and AE presence achieved an AUC of 0.94, representing a clinically actionable tool for risk stratification. The dual-threshold approach enables flexible clinical application: the low-threshold maximizes sensitivity for population-level triage, while the high-threshold ensures specificity for individual clinical decision-making. This model addresses a critical unmet need, as current guidelines lack validated tools for stratifying malignancy risk in endometriosis patients [ 16 ]. The superior survival outcomes in EAOC (100% vs 60.44% 5-year survival) likely reflect the earlier stage at diagnosis, different tumour biology, and potentially greater chemosensitivity of endometriosis-associated histotypes [ 17 ]. These findings reinforce the importance of early detection through structured surveillance. Limitations include the relatively small EAOC subgroup (n = 27), single-country design limiting generalizability, and the retrospective immunohistochemical analysis. External validation of the risk stratification model in independent cohorts is warranted. Future studies should incorporate genomic sequencing to characterize additional molecular alterations (PIK3CA, KRAS, CTNNB1) and evaluate the cost-effectiveness of systematic ARID1A testing in clinical practice. CONCLUSION Atypical endometriosis is a true premalignant lesion with a global prevalence of 10.45%, rising to 40.74% in EAOC. Postmenopausal status, GI-RADS > 3, and AE presence are independent predictors of malignant transformation, enabling a validated risk stratification model with AUC = 0.94. ARID1A/BAF-250a loss is the most consistent molecular marker of progression. EAOC exhibits a distinct, more favorable clinical course than non-endometriosis-associated ovarian cancer. These findings support the implementation of a structured, individualized post-surgical surveillance algorithm integrating clinical, imaging, histological, and molecular variables to optimize early EAOC detection (Fig. 2 ). Abbreviations AE Atypical Endometriosis ANOVA Analysis of Variance ARID1A AT-rich Interaction Domain 1A AUC Area Under the Curve BAF-250a BRG1-associated factor 250a BMI Body Mass Index CA-125 Cancer Antigen 125 CA 19-9 Carbohydrate Antigen 19-9 CEA Carcinoembryonic Antigen CI Confidence Interval CLIA Chemiluminescent Microparticle Immunoassay COnoAE Non-endometriosis-associated Ovarian Cancer COX-2 Cyclooxygenase-2 CCC Clear Cell Carcinoma CT Computed Tomography CTNNB1 Catenin Beta 1 DAKO Dako Automated Immunohistochemistry System EAOC Endometriosis-Associated Ovarian Cancer EAC Endometrioid Adenocarcinoma ECLIA Electrochemiluminescence Immunoassay ESHRE European Society of Human Reproduction and Embryology ET Typical Endometriosis GI-RADS Gynecologic Imaging Reporting and Data System H&E Hematoxylin and Eosin HE4 Human Epididymis Protein 4 HGURS Hospital General Universitario Reina Sofía HUVA Hospital Clínico Universitario Virgen de la Arrixaca IBM International Business Machines KRAS Kirsten Rat Sarcoma Viral Oncogene Homolog MRI Magnetic Resonance Imaging N/A Not Applicable N/S Not Significant OR Odds Ratio PIK3CA Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha PTEN Phosphatase and Tensin Homolog ROC Receiver Operating Characteristic ROMA Risk of Ovarian Malignancy Algorithm SD Standard Deviation SPSS Statistical Package for the Social Sciences UI/mL International Units per Milliliter Declarations Ethics approval and consent to participate. Consent for publication This study was approved by the Institutional Ethics Committee of Hospital General Universitario Reina Sofía and Hospital Clínico Universitario Virgen de la Arrixaca, Murcia, Spain. All participants provided written informed consent prior to enrolment and a written informed consent for publication.. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to individual privacy could be compromisedbut are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding This research received no external funding. Authors' contributions RS: Conceptualization, Methodology, Formal Analysis, Writing – Original Draft. CG and SS Data Curation, Investigation, Resources LL: Validation, Pathology reviews FM and PM: Supervision, Validation, Writing – Review & Editing. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9723381","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":655871332,"identity":"ff3e8104-664b-4244-b037-0d4c741160c3","order_by":0,"name":"Rocio Sánchez-Gómez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBADORBx4AEpWozBWhJI0ZLYACKJ0mLef/zioxsVdenzww4/BNpiJ6fbQECLzI2cYuOcM2y5G2+nGQC1JBubHSCgRUKCJ006t40nd+PsBJCWA4nbCGrhPwPU8k8i3XB2+gcitTCkH5PObTBIkJfOIdYWiRxm45xjCYYbpHMKDiQYEOMX/uMPH+fU1MnLz07f/OFDhZ0cQS0MDDwGYMoArNKAoHIQYH8ApuQbiFI9CkbBKBgFIxEAAPr6QxbiDgiZAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Rocio","middleName":"","lastName":"Sánchez-Gómez","suffix":""},{"id":655871335,"identity":"80c62b56-f9a5-476c-ab39-39e355c37b0d","order_by":1,"name":"Sonia Soler-Gabaldon","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"","lastName":"Soler-Gabaldon","suffix":""},{"id":655871337,"identity":"321c6a38-52a1-41dd-874d-e58b0a38ca25","order_by":2,"name":"Celia Gomez-Meseguer","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Celia","middleName":"","lastName":"Gomez-Meseguer","suffix":""},{"id":655871338,"identity":"06acdcdc-f4b1-4c2f-9c9f-8057f87b0cb1","order_by":3,"name":"Laura Lorente-Gea","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Lorente-Gea","suffix":""},{"id":655871340,"identity":"318d82d7-b5f5-48d4-bad8-a7e35a33239f","order_by":4,"name":"Amparo Torroba","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Amparo","middleName":"","lastName":"Torroba","suffix":""},{"id":655871342,"identity":"29d9e6b8-78fa-4153-aa4b-9ba2e91288d9","order_by":5,"name":"Pilar Marin-Sanchez","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pilar","middleName":"","lastName":"Marin-Sanchez","suffix":""},{"id":655871343,"identity":"00ee17ae-2def-4ffa-a3de-bb22fa27b299","order_by":6,"name":"Francisco Machado-Linde","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Machado-Linde","suffix":""}],"badges":[],"createdAt":"2026-05-15 10:24:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9723381/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9723381/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":111762146,"identity":"9920e111-8c3c-45c9-a06a-dcd4bab39c2c","added_by":"auto","created_at":"2026-06-10 17:19:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Survival Curves. \u003c/strong\u003eOverall survival and disease-free survival curves for EAOC (n=27) versus non-endometriosis-associated ovarian cancer (COnoAE, n=41). Five-year overall survival was 100% for EAOC versus 60.44% for COnoAE (p\u0026lt;0.001). Median disease-free interval was 2.37 years. Recurrence was observed in 1 EAOC patient (3.7%) versus 32 COnoAE patients (20.13%).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9723381/v1/5d177fdea464addc48ade6bd.jpg"},{"id":111762147,"identity":"9b5547d6-21b0-467c-9bea-9ee35629635a","added_by":"auto","created_at":"2026-06-10 17:19:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":185971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed Risk Stratification Algorithm for Atypical Endometriosis. \u003c/strong\u003eFlowchart illustrating the three-tier risk stratification model integrating clinical (menopausal status, age), imaging (GI-RADS, cyst size), histopathological (atypia type), and molecular (ARID1A, Ki-67) variables. Low-risk patients undergo conservative surveillance; intermediate-risk patients require intensified monitoring with consideration of prophylactic surgery; high-risk patients are referred for early surgical intervention at an oncological centre. GI-RADS: Gynecologic Imaging Reporting and Data System; ARID1A: AT-rich interaction domain 1A; TVUS: transvaginal ultrasound; MRI: magnetic resonance imaging.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9723381/v1/726faf72e52293b019ff49da.jpg"},{"id":111762148,"identity":"4bd590e7-6638-4764-8654-a7c0d467bbe3","added_by":"auto","created_at":"2026-06-10 17:19:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve for the Multivariate Prediction Model. \u003c/strong\u003eReceiver operating characteristic curve for the binary logistic regression model identifying EAOC. The model integrating postmenopausal status, GI-RADS \u0026gt;3, and atypical endometriosis achieved an AUC of 0.94 (95%CI: 0.80–0.99), with 93.8% sensitivity and 87.5% specificity at the optimal probability cutoff of ≥0.5. AUC: area under the curve; CI: confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9723381/v1/c801bfa0e26fb32862fe675a.jpg"},{"id":111842307,"identity":"72f2cbb5-3b9c-4eb4-abaf-212e6958bafa","added_by":"auto","created_at":"2026-06-11 13:24:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3279056,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative Histopathology and Immunohistochemistry. \u003c/strong\u003e(A) Hematoxylin and eosin staining showing typical endometriosis (200×). (B) Cytological atypical endometriosis with nuclear enlargement and hyperchromasia (400×). (C) Architectural atypical endometriosis with glandular crowding and cribriform patterns (200×). (D) Loss of ARID1A/BAF-250a nuclear expression in atypical endometriosis with concurrent EAOC (400×). (E) Conserved ARID1A/BAF-250a expression in typical endometriosis (400×).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9723381/v1/dbe92b9072c05cd062e9a65d.png"},{"id":111843699,"identity":"dabf07be-5509-4aed-843b-87240cafc50d","added_by":"auto","created_at":"2026-06-11 13:33:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3377757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9723381/v1/2843a3f0-011e-4490-b659-c9dd4a5bf9c0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Atypical Endometriosis as a Premalignant Histological Lesion in Endometriosis-Associated Ovarian Cancer: Clinical, Imaging, Molecular Characterization and Proposal for Risk Stratification","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eEndometriosis affects approximately 10% of women of reproductive age and is characterized by the presence of endometrial-like tissue outside the uterine cavity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While traditionally considered a benign condition, accumulating evidence demonstrates that endometriosis, particularly ovarian endometriosis, is associated with an increased risk of ovarian cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Endometriosis-associated ovarian cancer (EAOC) accounts for approximately 10\u0026ndash;15% of all epithelial ovarian cancers, with clear cell carcinoma (CCC) and endometrioid adenocarcinoma (EAC) being the predominant histological subtypes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pathogenesis of EAOC is thought to involve a stepwise progression from typical endometriosis through atypical endometriosis (AE) to invasive carcinoma, analogous to the endometrial hyperplasia\u0026ndash;carcinoma sequence in endometrial cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. AE is characterized by cytological atypia (nuclear enlargement, hyperchromasia, increased nuclear-to-cytoplasmic ratio) and/or architectural atypia (epithelial stratification, tufting, cribriform patterns) in endometriotic lesions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the clinical significance of AE remains debated, with reported prevalence rates varying widely from 1% to 61% depending on diagnostic criteria and study populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent molecular studies have identified key genetic alterations in the endometriosis-to-cancer continuum. Loss of ARID1A (AT-rich interaction domain 1A), a tumor suppressor gene encoding the BAF-250a protein involved in chromatin remodeling, has been identified in 40\u0026ndash;57% of EAOC cases, particularly CCC and EAC subtypes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, elevated Ki-67 proliferative index and altered PTEN expression have been associated with neoplastic transformation in endometriotic lesions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite these advances, no validated clinical model integrating clinical, imaging, and molecular markers for AE risk stratification has been established.\u003c/p\u003e \u003cp\u003eThis study aimed to: (1) determine the prevalence of AE in a prospective cohort of patients with endometriosis and ovarian cancer; (2) characterize the clinical, echographic, tumor marker, and immunohistochemical profile of AE; (3) identify independent predictors of EAOC; and (4) propose a structured risk stratification model for clinical management of AE.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\n \u003cp\u003eThis was a prospective observational cohort study conducted at the Gynecology and Obstetrics Services of Hospital General Universitario Reina Sof\u0026iacute;a (HGURS) and Hospital Cl\u0026iacute;nico Universitario Virgen de la Arrixaca (HUVA), Murcia, Spain, between January 2019 and July 2022. The study received institutional ethics committee approval, and all participants provided written informed consent.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eA total of 334 patients with an anatomopathological diagnosis of endometriosis and/or ovarian cancer were enrolled. Patients were classified into three groups: (1) endometriosis without malignancy (n\u0026thinsp;=\u0026thinsp;236); (2) EAOC (n\u0026thinsp;=\u0026thinsp;27); and (3) non-endometriosis-associated ovarian cancer (COnoAE, n\u0026thinsp;=\u0026thinsp;41). Inclusion criteria were: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, signed informed consent, and histopathological diagnosis of ovarian endometriosis or ovarian cancer. Exclusion criteria were: cognitive impairment, surgery not performed, absence of informed consent, benign ovarian tumors, and no ovarian pathology found at surgery.\u003c/p\u003e\n\u003ch3\u003eClinical and Imaging Assessment\u003c/h3\u003e\n\u003cp\u003eAll patients underwent preoperative transvaginal ultrasound with GI-RADS (Gynecologic Imaging Reporting and Data System) classification. Maximum cyst diameter was measured. Serum tumor markers were determined preoperatively: CA-125 and CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9 by chemiluminescent microparticle immunoassay (CLIA; Beckman Coulter Access\u0026reg; system); HE4 and CEA by electrochemiluminescence immunoassay (ECLIA; cobas e\u0026reg; system). The ROMA (Risk of Ovarian Malignancy Algorithm) index was calculated from HE4 and CA-125 values adjusted for menopausal status.\u003c/p\u003e\n\u003ch3\u003eHistopathological and Immunohistochemical Analysis\u003c/h3\u003e\n\u003cp\u003eSurgical specimens were reviewed by two experienced gynecological pathologists. Representative endometriotic lesions and areas of transition to adenocarcinoma were embedded in paraffin and stained with hematoxylin and eosin (H\u0026amp;E). AE was defined as endometriosis with cytological atypia (nuclear enlargement, hyperchromasia, pleomorphism) and/or architectural atypia (glandular crowding, cribriform patterns, complex papillary structures). Immunohistochemical analysis was performed using automated DAKO systems for: ARID1A/BAF-250a (loss defined as \u0026gt;\u0026thinsp;95% absence of nuclear staining in epithelial cells); Ki-67 (percentage of positive nuclei); PTEN (loss or marked reduction of cytoplasmic/nuclear staining); and COX-2 (cytoplasmic immunoreactivity in epithelial cells). In Fig. 4, some of these immunohistological analyses are shown.\u003c/p\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eNormality of quantitative variables was assessed by the Shapiro-Wilk test. Normally distributed variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared by Student\u0026apos;s t-test or one-way ANOVA. Non-normally distributed variables were expressed as median (interquartile range) and compared by Kruskal-Wallis test. Categorical variables were compared by Pearson chi-squared test or Fisher\u0026apos;s exact test. Prevalence and 95% confidence intervals (CI) were estimated by Wilson\u0026apos;s method. Binary logistic regression identified independent predictors of EAOC. Survival and recurrence analyses used Kaplan-Meier estimator with log-rank test. Discriminative performance was assessed by area under the receiver operating characteristic curve (AUC). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Analyses were performed using SPSS v.26.0 (IBM Corp.).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of Atypical Endometriosis\u003c/h2\u003e \u003cp\u003eOf 334 patients enrolled, AE was identified in 21 cases, yielding a global prevalence of 10.45% (95%CI: 6.9\u0026ndash;15.5%). AE was significantly more frequent in EAOC (40.74%) compared to isolated endometriosis (5.75%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The predominant histological pattern was cytological atypia; however, architectural atypia showed a more consistent association with malignant progression.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical and Epidemiological Characteristics\u003c/h3\u003e\n\u003cp\u003eEAOC patients exhibited an intermediate clinical profile between endometriosis and non-EAOC ovarian cancer. Mean age differed significantly across groups: endometriosis 41.47 years, EAOC 51.23 years, and COnoAE 56.66 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Postmenopausal status was present in 8.55% of endometriosis, 52% of EAOC, and 70.73% of COnoAE patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). No significant differences were observed in BMI or parity. Clinical and epidemiological characteristics are summarized in 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\u003eClinical and epidemiological characteristics of the study cohort (n\u0026thinsp;=\u0026thinsp;334)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;236)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEAOC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOnoAE\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.47\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.23\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.66\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtypical endometriosis (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytological atypia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/S\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArchitectural atypia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess frequent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/S\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo significant difference between groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/S\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo significant difference between groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/S\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 \u003cem\u003eEAOC: endometriosis-associated ovarian cancer; COnoAE: non-endometriosis-associated ovarian cancer; N/S: not significant; SD: standard deviation.\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImaging and Tumor Markers\u003c/h2\u003e \u003cp\u003eGI-RADS classification differed significantly across groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): endometriosis cases were predominantly GI-RADS 3 (71.21%), while 81.82% of EAOC and 62.96% of COnoAE were GI-RADS 5. Overall, 95% of EAOC and COnoAE patients were GI-RADS 4\u0026ndash;5. Median maximum cyst diameter was 53 mm (endometriosis), 93 mm (EAOC), and 106 mm (COnoAE) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); a cutoff of 7.2 cm showed moderate discriminative capacity (AUC\u0026thinsp;=\u0026thinsp;0.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Median HE4 was 50 pmol/L (endometriosis), 72 pmol/L (EAOC), and 103 pmol/L (COnoAE) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with elevated HE4 in 30.77%, 68.42%, and 75% of cases, respectively. Median CA-125 was 36.15 UI/mL (endometriosis), 32.45 UI/mL (EAOC), and 105.55 UI/mL (COnoAE) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ROMA high-risk classification was present in 24.73% of endometriosis, 28.57% of EAOC, and 65.12% of COnoAE patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Imaging and tumor marker data are shown in 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\u003eImaging and tumor marker characteristics by group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;236)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEAOC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOnoAE\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI-RADS 3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI-RADS 4\u0026ndash;5 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian cyst diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyst diameter cutoff (7.2 cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u0026thinsp;=\u0026thinsp;0.74 for endometrioma vs EAOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian CA-125 (UI/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated CA-125 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian HE4 (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated HE4 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian CEA (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROMA high-risk (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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 \u003cem\u003eHE4: human epididymis protein 4; ROMA: Risk of Ovarian Malignancy Algorithm; GI-RADS: Gynecologic Imaging Reporting and Data System; AUC: area under the curve.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical Findings\u003c/h2\u003e \u003cp\u003eLoss of ARID1A/BAF-250a expression was significantly more frequent in AE (42.86%) compared to typical endometriosis (ET, 6.52%) (p\u0026thinsp;=\u0026thinsp;0.001). When AE was associated with EAOC, ARID1A loss reached 72.73%, compared to 10% in AE without EAOC (p\u0026thinsp;=\u0026thinsp;0.008). Elevated Ki-67 was present in 75% of AE versus 25% of ET (p\u0026thinsp;=\u0026thinsp;0.020). PTEN showed a heterogeneous pattern, predominantly observed in endometrioid carcinoma and cytological AE, with no statistically significant overall difference. COX-2 expression did not significantly differ between groups. Immunohistochemical results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eImmunohistochemical marker expression in atypical endometriosis (AE) versus typical endometriosis (ET)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTypical Endometriosis\u003c/p\u003e \u003cp\u003e(ET)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtypical Endometriosis\u003c/p\u003e \u003cp\u003e(AE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAE with EAOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAE without EAOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARID1A/BAF-250a loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001 / 0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated Ki-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTEN loss/alteration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredominant in EAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHeterogeneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/S\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOX-2 positivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/S\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 \u003cem\u003eARID1A: AT-rich interaction domain 1A; EAOC: endometriosis-associated ovarian cancer; EAC: endometrioid adenocarcinoma; N/S: not significant.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Analysis and Risk Stratification Model\u003c/h2\u003e \u003cp\u003eBinary logistic regression identified three independent predictors of EAOC: postmenopausal status (OR\u0026thinsp;\u0026asymp;\u0026thinsp;28.8; 95%CI: [to be specified]), GI-RADS\u0026thinsp;\u0026gt;\u0026thinsp;3 (OR\u0026thinsp;\u0026asymp;\u0026thinsp;48.0; 95%CI: [to be specified]), and presence of AE (OR\u0026thinsp;\u0026asymp;\u0026thinsp;25.8; 95%CI: [to be specified]). The model demonstrated excellent discriminative capacity (AUC\u0026thinsp;=\u0026thinsp;0.94; 95%CI: 0.80\u0026ndash;0.99), correctly classifying 90.6% of cases (sensitivity 93.8%, specificity 87.5%). Two clinical probability thresholds were defined: a low-threshold (\u0026ge;\u0026thinsp;0.2) for triage with high sensitivity, and a high-threshold (\u0026ge;\u0026thinsp;0.5) for diagnostic confirmation with high specificity. Model performance is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\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\u003eMultivariate logistic regression model for prediction of EAOC\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=\"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 \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026asymp;\u0026thinsp;28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.80\u0026ndash;32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI-RADS\u0026thinsp;\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026asymp;\u0026thinsp;48.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.30\u0026ndash;54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtypical endometriosis (AE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026asymp;\u0026thinsp;25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.80\u0026ndash;29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel AUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity (cutoff\u0026thinsp;\u0026ge;\u0026thinsp;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity (cutoff\u0026thinsp;\u0026ge;\u0026thinsp;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall correct classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\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\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAUC: area under the curve; CI: confidence interval; GI-RADS: Gynecologic Imaging Reporting and Data System.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSurvival and Recurrence Outcomes\u003c/h2\u003e \u003cp\u003eFive-year overall survival was 100% for EAOC versus 60.44% for COnoAE (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). CCC and EAC subtypes showed particularly favorable outcomes when associated with endometriosis, with 5-year survival reaching 100%. The median disease-free interval was 2.37 years. COnoAE showed a significantly higher recurrence rate (n\u0026thinsp;=\u0026thinsp;32, 20.13%) compared to EAOC (n\u0026thinsp;=\u0026thinsp;1). Only 6.77% of COnoAE patients achieved a disease-free interval exceeding 5 years, compared to 22.22% of EAOC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Kaplan-Meier survival curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis prospective cohort study provides comprehensive evidence that AE is a true premalignant lesion in the endometriosis-to-ovarian cancer continuum. The global AE prevalence of 10.45%, rising to 40.74% in EAOC, is consistent with previous reports and underscores the clinical relevance of systematic histopathological evaluation of endometriotic specimens [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe intermediate clinical profile of EAOC patients \u0026mdash; older age and higher postmenopausal prevalence than endometriosis, yet younger and with more early-stage disease than COnoAE \u0026mdash; supports the concept of EAOC as a distinct biological entity with different carcinogenesis pathways [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This has important implications for clinical management, as EAOC may require different surveillance and treatment strategies compared to sporadic ovarian cancer.\u003c/p\u003e \u003cp\u003eThe utility of structured ultrasound (GI-RADS) was confirmed, with 95% of malignant cases classified as GI-RADS 4\u0026ndash;5. The tumor size cutoff of 7.2 cm (AUC\u0026thinsp;=\u0026thinsp;0.74) provides a practical preoperative parameter for malignancy suspicion. HE4 demonstrated superior specificity for malignancy compared to CA-125, whose interpretation is confounded by the inflammatory nature of endometriosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings align with current ESHRE guidelines recommending HE4 and ROMA index in the preoperative assessment of adnexal masses [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe molecular analysis revealed ARID1A/BAF-250a loss as the most robust marker of neoplastic progression, present in 72.73% of AE with concurrent EAOC. This is consistent with the established role of ARID1A mutations in CCC and EAC carcinogenesis, where ARID1A loss is thought to be an early event facilitating immune evasion and oncogenic progression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The heterogeneous PTEN pattern observed supports subtype-specific carcinogenesis pathways within EAOC [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe multivariate model integrating menopausal status, GI-RADS\u0026thinsp;\u0026gt;\u0026thinsp;3, and AE presence achieved an AUC of 0.94, representing a clinically actionable tool for risk stratification. The dual-threshold approach enables flexible clinical application: the low-threshold maximizes sensitivity for population-level triage, while the high-threshold ensures specificity for individual clinical decision-making. This model addresses a critical unmet need, as current guidelines lack validated tools for stratifying malignancy risk in endometriosis patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe superior survival outcomes in EAOC (100% vs 60.44% 5-year survival) likely reflect the earlier stage at diagnosis, different tumour biology, and potentially greater chemosensitivity of endometriosis-associated histotypes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These findings reinforce the importance of early detection through structured surveillance.\u003c/p\u003e \u003cp\u003eLimitations include the relatively small EAOC subgroup (n\u0026thinsp;=\u0026thinsp;27), single-country design limiting generalizability, and the retrospective immunohistochemical analysis. External validation of the risk stratification model in independent cohorts is warranted. Future studies should incorporate genomic sequencing to characterize additional molecular alterations (PIK3CA, KRAS, CTNNB1) and evaluate the cost-effectiveness of systematic ARID1A testing in clinical practice.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAtypical endometriosis is a true premalignant lesion with a global prevalence of 10.45%, rising to 40.74% in EAOC. Postmenopausal status, GI-RADS\u0026thinsp;\u0026gt;\u0026thinsp;3, and AE presence are independent predictors of malignant transformation, enabling a validated risk stratification model with AUC\u0026thinsp;=\u0026thinsp;0.94. ARID1A/BAF-250a loss is the most consistent molecular marker of progression. EAOC exhibits a distinct, more favorable clinical course than non-endometriosis-associated ovarian cancer. These findings support the implementation of a structured, individualized post-surgical surveillance algorithm integrating clinical, imaging, histological, and molecular variables to optimize early EAOC detection (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAtypical Endometriosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnalysis of Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eARID1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAT-rich Interaction Domain 1A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBAF-250a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBRG1-associated factor 250a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCA-125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer Antigen 125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCA 19-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCarbohydrate Antigen 19-9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCarcinoembryonic Antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCLIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChemiluminescent Microparticle Immunoassay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOnoAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-endometriosis-associated Ovarian Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOX-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCyclooxygenase-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClear Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComputed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCTNNB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCatenin Beta 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDAKO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDako Automated Immunohistochemistry System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEAOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEndometriosis-Associated Ovarian Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEndometrioid Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECLIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElectrochemiluminescence Immunoassay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eESHRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEuropean Society of Human Reproduction and Embryology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTypical Endometriosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGI-RADS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGynecologic Imaging Reporting and Data System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eH\u0026amp;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHematoxylin and Eosin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHuman Epididymis Protein 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHGURS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHospital General Universitario Reina Sofía\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHUVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHospital Clínico Universitario Virgen de la Arrixaca\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInternational Business Machines\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKirsten Rat Sarcoma Viral Oncogene Homolog\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot Applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot Significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePIK3CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePTEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhosphatase and Tensin Homolog\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eROMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk of Ovarian Malignancy Algorithm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSPSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUI/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInternational Units per Milliliter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate. Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Ethics Committee of Hospital General Universitario Reina Sofía and Hospital Clínico Universitario Virgen de la Arrixaca, Murcia, Spain. All participants provided written informed consent prior to enrolment and a written informed consent for publication..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to individual privacy could be compromisedbut are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRS: Conceptualization, Methodology, Formal Analysis, Writing – Original Draft.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCG and SS Data Curation, Investigation, Resources\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLL: Validation, Pathology reviews\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFM and PM: Supervision, Validation, Writing – Review \u0026amp; Editing.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to sincerely thank Dr. Amparo Torroba and Dr. Paco García from the Department of Pathology for their valuable support, expertise, and collaboration throughout this study. We also acknowledge the technical staff for their assistance and contribution to the processing and analysis of the samples.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLamceva J, Uljanovs R, Strumfa I. The main theories on the pathogenesis of endometriosis. Int J Mol Sci. 2023;24(5):4254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms24054254\u003c/span\u003e\u003cspan address=\"10.3390/ijms24054254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHermens M, van Altena AM, van der Aa M, Bulten J, van Vliet HAAM, Siebers AG, et al. Ovarian cancer prognosis in women with endometriosis: a retrospective nationwide cohort study of 32,419 women. Am J Obstet Gynecol. 2021;224(3):284. e1-284.e10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen P, Zhang CY. 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Obstet Gynecol. 1990;75(6):1023\u0026ndash;8.\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":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atypical endometriosis, Endometriosis-associated ovarian cancer, ARID1A, Risk stratification, GI-RADS, Premalignant lesion","lastPublishedDoi":"10.21203/rs.3.rs-9723381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9723381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo develop a risk stratification model for malignant progression in endometriosis-associated ovarian cancer (EAOC), while determining the prevalence of atypical endometriosis (AE) and characterizing its clinical, imaging, and molecular profile.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eProspective observational cohort study of 334 patients (January 2019\u0026ndash;July 2022) at two tertiary hospitals in Murcia, Spain. Patients were classified into three groups: endometriosis (n\u0026thinsp;=\u0026thinsp;236), EAOC (n\u0026thinsp;=\u0026thinsp;27), and non-endometriosis-associated ovarian cancer (n\u0026thinsp;=\u0026thinsp;41). Clinical variables, ultrasound features (GI-RADS classification), tumor markers (CA-125, HE4, CEA, ROMA index), and immunohistochemical markers (ARID1A/BAF-250a, Ki-67, PTEN, COX-2) were analyzed. Binary logistic regression identified independent predictors of EAOC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGlobal AE prevalence was 10.45%, increasing to 40.74% in EAOC versus 5.75% in isolated endometriosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). EAOC patients showed intermediate clinical characteristics between benign endometriosis and ovarian cancer. Multivariate analysis identified menopause (OR\u0026thinsp;\u0026asymp;\u0026thinsp;28.8), GI-RADS\u0026thinsp;\u0026gt;\u0026thinsp;3 (OR\u0026thinsp;\u0026asymp;\u0026thinsp;48.0), and AE presence (OR\u0026thinsp;\u0026asymp;\u0026thinsp;25.8) as independent predictors (AUC\u0026thinsp;=\u0026thinsp;0.94; 95%CI: 0.80\u0026ndash;0.99; sensitivity 93.8%, specificity 87.5%). ARID1A loss occurred in 72.73% of AE with EAOC versus 10% without EAOC (p\u0026thinsp;=\u0026thinsp;0.008). Five-year survival was 100% for EAOC versus 60.44% for non-EAOC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAE represents a true premalignant lesion with distinct molecular signatures. Integration of menopausal status, structured ultrasound, and molecular markers enables effective risk stratification and supports individualized surveillance protocols for early EAOC detection.\u003c/p\u003e","manuscriptTitle":"Atypical Endometriosis as a Premalignant Histological Lesion in Endometriosis-Associated Ovarian Cancer: Clinical, Imaging, Molecular Characterization and Proposal for Risk Stratification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-06-10 17:19:37","doi":"10.21203/rs.3.rs-9723381/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"80782830647046458786761624278885099410","date":"2026-06-08T01:26:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133584192251339811326186852379151238269","date":"2026-06-07T12:21:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240028591465314686278318916745261845454","date":"2026-06-06T11:30:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330890691423330111416230261374701918783","date":"2026-06-06T10:12:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57563122582462017937699913703076894467","date":"2026-06-05T13:10:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237049061997744856818281490097973116610","date":"2026-06-04T10:20:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-06-04T09:23:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-21T04:42:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-21T04:41:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ovarian Research","date":"2026-05-15T10:11:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"428a7044-3b03-4818-90c9-d7b9dc662969","owner":[],"postedDate":"June 10th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"80782830647046458786761624278885099410","date":"2026-06-08T01:26:27+00:00","index":111,"fulltext":""},{"type":"reviewerAgreed","content":"133584192251339811326186852379151238269","date":"2026-06-07T12:21:29+00:00","index":109,"fulltext":""},{"type":"reviewerAgreed","content":"240028591465314686278318916745261845454","date":"2026-06-06T11:30:59+00:00","index":107,"fulltext":""},{"type":"reviewerAgreed","content":"330890691423330111416230261374701918783","date":"2026-06-06T10:12:29+00:00","index":106,"fulltext":""},{"type":"reviewerAgreed","content":"57563122582462017937699913703076894467","date":"2026-06-05T13:10:02+00:00","index":104,"fulltext":""},{"type":"reviewerAgreed","content":"237049061997744856818281490097973116610","date":"2026-06-04T10:20:13+00:00","index":100,"fulltext":""},{"type":"reviewersInvited","content":"86","date":"2026-06-04T09:23:45+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-06-10T17:19:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-06-10 17:19:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9723381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9723381","identity":"rs-9723381","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}