A novel serum-based steroid-protein panels for differentiating ovarian cancer from non-malignant adnexal masses

A novel serum-based steroid-protein panels for differentiating ovarian cancer from non-malignant adnexal masses | 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 A novel serum-based steroid-protein panels for differentiating ovarian cancer from non-malignant adnexal masses Marija Gjorgoska, Boštjan Pirš, Špela Smrkolj, Tea Rižner This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6596566/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in Cancer Cell International → Version 1 posted 10 You are reading this latest preprint version Abstract Background Ovarian cancer is the deadliest gynecological malignancy, largely due to the advanced stage at diagnosis in most patients. This study investigates whether systemic steroids can serve as biomarkers to distinguish malignant ovarian tumors from non-malignant adnexal masses. Methods This prospective, single-center observational study included 99 women with adnexal masses who underwent surgery between December 2021 and February 2025. Preoperative serum levels of 17 steroid hormones—including androgens, 11-oxyandrogens, glucocorticoids, and mineralocorticoids—were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Machine learning was employed to assess the diagnostic potential of these steroids in distinguishing ovarian cancer (n = 43) from non-malignant adnexal masses (n = 56). Results Patients with ovarian cancer had lower levels of 11β-hydroxy-testosterone (11OHT), 11-keto-testosterone (11KT), and testosterone compared to those with non-malignant adnexal masses. Using stepwise feature selection, we developed two diagnostic models incorporating three 11-oxyandrogens (11KT, 11OHT, and 11β-hydroxy-androstenedione), patient age, and either cancer antigen 125 (CA-125) or human epididymis protein 4 (HE4) for distinguishing malignant from non-malignant adnexal masses. The model including CA-125 achieved AUC of 0.907, 88.9% sensitivity and 82.0% specificity, while the model including HE4 achieved AUC of 0.911, 94.4% sensitivity and 77.3% specificity as evaluated by cross-validation. Both models significantly outperformed CA-125, HE4, and the Risk of Ovarian Malignancy Algorithm (ROMA) index alone. Conclusion Patients with ovarian cancer exhibit distinct steroid profiles compared to those with non-malignant adnexal masses. If validated, the models could enhance diagnosis, reducing unnecessary surgeries for benign conditions while ensuring timely treatment for ovarian cancer, particularly when conventional biomarkers are inconclusive. ovarian cancer adnexal masses steroids machine learning diagnostic models Figures Figure 1 Figure 2 1. Background Ovarian cancer (OC) is the eighth most diagnosed cancer in women and most common cause of gynecologic cancer death in developed countries. In 2022, approximately 324,603 new cases and 206,956 deaths were reported globally 1 . Epithelial cancer is the most common histological type, encompassing four subtypes: serous, clear cell, mucinous, and endometrioid carcinomas. Prognosis varies significantly by stage, with 5-year survival rates of 93% for cancer confined to the ovaries (localized), 75% for cancer that has spread nearby (regional), and only 31% for cancer that has spread to distant organs (distant) 2 , 3 . Unfortunately, over a third of cases are diagnosed at an advanced stage, largely due to nonspecific symptoms. Most symptomatic patients or those with abnormal clinical findings, such as suspicious ultrasound results or elevated cancer antigen 125 (CA-125) levels, do not have ovarian cancer. A study found that only 3% of premenopausal and 18% of postmenopausal patients referred through the UK’s National Health Services (NHS) expedited pathway were actually diagnosed with ovarian cancer 4 . Clearly, accurate, accessible, and cost-effective diagnostic methods are needed to distinguish malignant tumors from non-malignant adnexal masses and reduce unnecessary procedures. Currently, several risk-prediction models, based on either ultrasound features or blood biomarker levels exist that can be used to triage patients with an adnexal mass. Several tools are currently used to triage patients with adnexal masses, including CA-125, HE4, the Risk of Malignancy Index (RMI1), the Risk of Ovarian Malignancy Algorithm (ROMA), the International Ovarian Tumor Analysis (IOTA) Simple Rules, the IOTA Assessment of Different Neoplasias in the Adnexa (ADNEX) model, and the Ovarian-Adnexal Reporting and Data System (ORADS) 5 – 9 . These diagnostic tools differ in performance. CA-125 at the 35 kU/L threshold has high sensitivity (> 80%) but low specificity (< 80%); RMI1, ROMA and IOTA Simple Rules achieve a better balance, exceeding 80% sensitivity and specificity for diagnosing ovarian cancer in symptomatic postmenopausal women; ADNEX has very high sensitivity (> 95%) but low specificity (< 60%), leading to more false positives 10 . CA-125, used alone or as part of ROMA, lacks specificity due to its elevation in benign conditions common in premenopausal patients such as menstruation, endometriosis, and peritoneal inflammation 11 , as well as conditions common in postmenopausal and elderly patients such as heart failure, cirrhosis, chronic kidney disease, and intraabdominal infections. Other models relying on ultrasound findings require validation in routine practice, as scans are often performed by non-specialists. Emerging molecular biomarkers, including circulating tumor DNA, tumor cells, exosomes, and metabolites, show promise for ovarian cancer diagnosis but remain unadopted due to limited validation and high costs 12 – 14 . This study investigates serum steroids as biomarkers for diagnosing ovarian cancer in patients with adnexal masses, using machine learning. We measured preoperative serum levels of classic androgens, 11-oxyandrogens, glucocorticoids, and mineralocorticoids in 43 patients with primary/recurrent ovarian cancer and 56 with non-neoplastic adnexal masses, benign or borderline ovarian tumors, using a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Logistic regression was used to assess the diagnostic potential of these hormones, alone and in combination with CA-125, human epididymis protein 4 (HE4), and clinical parameters. 2. Materials and Methods 2.1. Study Population and Design This prospective, single-center study was approved by the Medical Ethics Committee of the Republic of Slovenia (ID: 0120–487/2020/3) and carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Adult women (≥ 18 years) who were scheduled for surgery for an adnexal mass or presumed/histologically proven ovarian cancer at the Department of Gynecology, University Medical Center Ljubljana, were included in this study. Exclusion criteria were age < 18, active non-ovarian malignancy, previous ovarian malignancy, presence of polycystic ovary syndrome (PCOS) and non-ovarian endometriosis. Sample collection occurred from December 2021 to February 2025, with written informed consent obtained from all participants. Blood samples were collected in the morning, 1–7 days before surgery, alongside lifestyle, medication, and clinical data. Blood samples (6 mL) were drawn into clot-activator vacutainers (BD Vacutainer, #368815), incubated at room temperature (min 20, max 60 min), and centrifuged (2000 g, 15 min), following standardized procedure for metabolomics studies 15 . Serum was aliquoted to minimize freeze-thaw effects and stored at -80°C. Tissue specimens were examined by a certified pathologist according to WHO Classification of tumors, 5th edition and ICCR dataset 16 . 2.2. Outcomes The primary outcome was the diagnostic accuracy of steroids for detecting ovarian cancer (binary outcome), defined as primary or recurrent malignant ovarian neoplasms (n = 43) versus non-malignant adnexal masses (defined as non-neoplastic adnexal masses or benign or borderline ovarian tumors) (n = 56), confirmed by histological examination obtained by surgery (reference standard). Three additional sub-analyses were performed to further evaluate the models. First, model performance was assessed upon excluding borderline ovarian tumors from the control group (n = 15). Second, the models were assessed specifically in postmenopausal patients, comparing primary/recurrent ovarian cancer (n = 34) to postmenopausal controls (n = 35). Third, model performance was evaluated in distinguishing high-grade serous ovarian cancer (HGSOC) (n = 32) from non-malignant adnexal masses (n = 56). 2.3. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) Unconjugated steroids (n = 17) were quantified using a previously validated multi-steroid profiling LC-MS/MS assay 17 . Schematic representation of the position of these steroids in steroid biosynthesis is given in Supplementary Fig. 1. Briefly, 180 µL of thawed serum, calibrators, and QC samples were mixed with stable isotope-labeled internal standards ([ 13 C 3 ]-dehydroepiandrosterone (DHEA), [ 13 C 3 ]-androstenedione (A4), [ 13 C 3 ]-testosterone (T), 5α-dihydrotestosterone (DHT)-d 3 ) and incubated for 15 minutes. After protein precipitation with 180 µL 3 M Na₂SO₄, samples were extracted using methyl- tert -butyl ether (MTBE). The organic layer was evaporated and the extracted analytes were reconstituted in LC-MS grade methanol-water (50:50) (v/v). Steroids were chromatographically separated using a Phenomenex Kinetex XB-C18 column on a Shimadzu Nexera LC system, with mass spectrometry detection on a Sciex 3500 triple quadrupole mass spectrometer using electrospray ionization. Data acquisition was performed using Analyst 1.6.2. Steroid quantification was based on peak area ratios of analytes to internal standards. All samples were anonymized prior to analysis. Steroid concentrations below the lower limit of quantification (LLOQ) were replaced by 0.5 x LLOQ for statistical analysis. Aldosterone, 5α-dihydrotestosterone (DHT), and progesterone were below the lower limit of quantification (LLOQ) in 69.2%, 58.6%, and 39.4%, respectively, and were excluded from further analysis. 2.4. Proteins Serum CA-125 (kU/L) and HE4 (pmol/L) levels were measured at the Clinical Institute for Clinical Biochemistry, University Medical Center, Ljubljana, using clinically validated electroluminescent immunoassays (ECLIAs), for CA-125, REF: 11776223190, for HE4, REF: 05950929190, on a Cobas e411 analyzer (Roche Diagnostics GmbH, Mannheim, Germany). The ROMA index score was calculated based on the formula established by Moore and collaborators 18 . 2.5. Statistical analysis Data were anonymized and analyzed using R Studio (version 4.3.0 or higher). Statistical significance was defined as p < 0.05. Continuous variables were expressed as median values with interquartile range (IQR) and compared using the Mann-Whitney U test. Robust ANCOVA (Analysis of Covariance) was used to adjust for age and menopause status. Categorical variables were expressed as frequencies with percentages and compared using Fisher exact test or Chi square test of independence. Machine learning was performed using the caret library 19 , with a 5×5-fold cross-validation protocol (1000 iterations). We tested 20 variables, including 12 steroid hormones, 3 steroid pools (classic androgen (sum of dehydroepiandrosterone (DHEA), androstenedione (A4) and testosterone (T)), 11-oxyandrogen (sum of 11β-hydroxy-androstenedione (11OHA4), 11-keto-androstenedione (11KA4), 11β-hydroxy-testosterone (11OHT), 11-keto-testosterone (11KT)), glucocorticoid (sum of 17-hydroxyprogesterone, 11-deoxycortisol, cortisol, cortisone)), two proteins (CA-125, HE4), ROMA index and 2 clinical parameters (age, menopause status). Continuous variables were ln-transformed and standardized. Feature selection was performed via stepAIC (MASS library). Multicollinearity was assessed using the Variance Inflation Factor (VIF) in a logistic regression model, with acceptable values < 5. Diagnostic accuracy was assessed using the area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score and Akaike information criteria (AIC). DeLong’s test was used to compare AUCs between different models. 3. Results 3.1. Description of the cohort Between December 2021 and February 2025, a total of 103 participants were recruited, and preoperative morning serum samples were collected (Figure 1). Among them, 47 (45.6%) had ovarian cancer, including 42 (89.4%) with primary ovarian cancer, one (2.1%) with recurrent disease, and four (8.5%) with metastatic disease from other primary sites. Participants with metastatic disease were excluded from the primary outcome analysis. Of the remaining 43 participants with ovarian cancer, 39 (90.7%) had epithelial tumors, with 32 (74.4%) classified as HGSOC, 2 (4.7%) as clear-cell, 2 (4.7%) as endometrioid, 2 (4.7%) as carcinosarcoma, and 1 (2.3%) as low-grade serous carcinoma. The remaining 4 cases (9.3%) had sex-cord stromal malignant tumors, including 3 (75%) with adult granulosa cell tumors and 1 (25%) with high-grade Sertoli-Leydig tumor. Regarding cancer staging, 7 (16.3%) cases were diagnosed at FIGO stage I, 2 (4.7%) at FIGO stage II, 26 (60.5%) at FIGO stage III, and 6 (14.0%) at FIGO stage IV; data was missing for two patients (4.7%). In terms of treatment status, 39 patients (90.7%) were treatment-naïve, four (9.3%) had been admitted after receiving neoadjuvant chemotherapy. The control group consisted of 56 participants with non-malignant adnexal masses, including 8 (14.3%) with non-neoplastic adnexal masses, 33 (58.9%) with benign ovarian tumors, and 15 (26.8%) with borderline ovarian tumors. Among borderline ovarian tumors, 10 (66.7%) were FIGO stage I, one (6.7%) was FIGO stage II, and 3 (20%) were FIGO stage III. Table 1 provides an overview of the basic demographics and clinical characteristics of the cohort. Significant differences between cases and controls were observed in age at operation, height, and use of oral contraceptives. No significant differences were observed between the groups in terms of weight, body mass index, menopausal status, smoking status, use of hormonal replacement therapy (past or in the previous 3 months), and parity. A detailed description of the clinical and histopathological characteristics of the study participants is provided in Supplementary Table 1. Table 1: Demographics and clinical characteristics of participants, by primary outcome definition of ovarian cancer. Diagnosis based on reference standard Primary/recurrent ovarian cancer (n=43) Non-malignant adnexal masses (n=56) All participants (n=99) Age (years); p=0.006 Median (IQR) 63.00 (53.00-68.00) 54.50 (47.00-64.00) 57.00 (50.00-65.50) Height (cm); p=0.041 Median (IQR) 164.00 (159.20-167.00) 165.00 (162.00-170.00) 165.00 (160.00-169.00) Missing 5 (11.6%) 7 (12.5%) 12 (12.1%) Weight (kg) Median (IQR) 66.00 (59.00-75.00) 65.00 (60.00-80.25) 65.00 (59.00-75.75) Missing 5 (11.6%) 6 (10.7%) 11 (11.1%) Body mass index (kg/m 2 ) Median (IQR) 23.00 (22.00-27.90) 23.80 (22.00-28.10) 23.75 (22.00-28.00) Missing 6 (14.0%) 7 (12.5%) 13 (13.1%) Menopausal status Premenopausal 9 (20.9%) 21 (37.5%) 30 (30.3%) Postmenopausal 34 (79.1%) 35 (62.5%) 69 (69.7%) Race or ethnicity White 18 (41.9%) 32 (57.1%) 50 (50.5%) Missing 25 (58.1%) 24 (42.8%) 49 (49.5%) Smoking status Never 10 (23.3%) 21 (37.5%) 31 (31.3%) Current 5 (11.6%) 3 (5.4%) 8 (8.1%) Ex-smoker 4 (9.35%) 8 (14.3%) 12 (12.1%) Missing 24 (55.8%) 24 (42.9%) 48 (48.5%) Hormonal replacement therapy No 15 (34.9%) 29 (51.8%) 44 (44.4%) Yes 3 (7.0%) 2 (3.6%) 5 (5.1%) Missing 25 (58.1%) 25 (44.6%) 50 (50.5%) Hormonal replacement therapy in the past 3 months No 17 (39.5%) 30 (53.6%) 47 (47.5%) Yes 2 (4.7%) 1 (1.8%) 3 (3.0%) Missing 24 (55.8%) 25 (44.6%) 49 (49.5%) Oral contraceptive use; p=0.030 No 12 (27.9%) 14 (25%) 26 (26.3%) Yes 4 (9.3%) 18 (32.1%) 22 (22.2%) Missing 27 (62.8%) 24 (42.9%) 51 (51.5%) Parity Median (IQR) 2.00 (1.00-2.00) 2.00 (1.50-2.00) 2.00 (1.00-2.00) Missing 21 (48.8%) 21 (37.5%) 42 (42.4%) Tumor stage according to FIGO I 7 (16.3%) 10 (BOTs, 66.7%) 17 (17.2%) II 2 (4.7%) 1 (BOTs, 6.7%) 3 (3.0%) III 26 (60.5%) 3 (BOTs, 20%) 29 (29.3%) IV 6 (14.0%) — 6 (6.1%) Unknown 2 (4.7%) — 2 (2.0%) Histopathological type Epithelial 39 (90.7%) — 39 (39.4%) High-grade serous 32 (74.4%) — 32 (32.3%) Low-grade serous 1 (2.3%) — 1 (1.0%) Clear cell 2 (4.6%) — 2 (2.0%) Endometrioid 2 (4.6%) — 2 (2.0%) Carcinosarcoma 2 (4.6%) — 2 (2.0%) Sex-cord stromal 4 (9.3%) — 4 (4.0%) Primary/recurrent disease Primary disease 42 (97.7%) — 42 (42.4%) Recurrent disease 1 (2.3%) — 1 (1.0%) Chemotherapy status Treatment-naïve 39 (90.7%) — 39 (39.4%) Post NACT 4 (9.3%) — 4 (4.0%) Non-malignant adnexal masses Non-neoplastic adnexal masses Normal adnexa with other pelvic pathology — 5 (.9%) 5 (5.1%) Sactosalpinx — 1 (1.8%) 1 (1.0%) Paraovarian cyst — 1 (1.8%) 1 (1.0%) Ovarian torsion with necrosis — 1 (1.8%) 1 (1.0%) Benign ovarian tumors Simple cyst — 9 (16.1%) 9 (9.1%) Mature teratoma — 6 (10.7%) 6 (6.1%) Fibroma — 3 (5.4%) 3 (3.0%) Endometrioma — 4 (7.1%) 4 (4.0%) Serous cystadenoma or cystadenofibroma — 7 (12.5%) 7 (7.1%) Mucinous cystadenoma or cystadenofibroma — 4 (7.1%) 4 (4.0%) Borderline ovarian tumors Serous — 7 (12.5%) 7 (7.1%) Mucinous — 6 (10.7%) 6 (6.1%) Sero-mucinous — 2 (3.6%) 2 (2.0%) Table 1: The table is at the end of the manuscript as it is longer than one A4. It should be placed here. Data are n (%) unless otherwise specified. Statistical significance was determined by Mann-Whitney U test for continuous variables and Fisher exact test or Chi square test of independence for categorical variables. Two-sided p values below the significance threshold 0.05 are reported. All participants were female. BOT, borderline ovarian tumor; FIGO, the International Federation of Gynecology and Obstetrics; NACT, neoadjuvant chemotherapy. 3.2. Preoperative serum steroid levels differ between patients with ovarian cancer and control women with non-malignant adnexal masses Preoperative serum steroid levels differed between patients with ovarian cancer and patients with non-malignant adnexal masses (Table 2). Univariate analysis showed significantly lower levels of T, 11OHT, and 11KT in cases compared to controls (p=0.012, <0.001, <0.001, respectively). After adjusting for age and menopause, 11OHT and 11KT remained significantly lower (p adj <0.001, 0.003, respectively), while T showed borderline significance (p adj =0.08). In contrast, cortisol levels were higher in cases vs controls, after adjusting for age and menopause (p adj : 0.027). No other significant hormone differences were found between the two groups. As expected, patients with ovarian cancer had higher CA-125 and HE4 levels as well as higher ROMA index score compared to controls. These trends of lower T, 11OHT, and 11KT and higher cortisol levels in cases compared to controls were consistent when patients with borderline ovarian tumors were excluded from the control group (Supplementary Table 2). In a separate sub-analysis comparing patients with HGSOC to controls with non-malignant adnexal masses, similar trends were observed. Patients with HGSOC had significantly lower levels of T (p adj =0.012), 11OHT (p adj =0.001), and 11KT (p adj =0.003), as well as higher cortisol levels (p adj =0.039), compared to controls (Supplementary Table 3). Cases with HGSOC had also significantly higher levels of CA-125, HE4 levels and ROMA index score compared to controls. Table 2. Preoperative serum concentrations of steroid hormones and CA-125 in patients with ovarian cancer (n=43) and controls (n=56), based on the primary outcome definition of ovarian cancer. Primary/recurrent OC (n=43) Non-malignant adnexal masses (n=56) P value Analyte Median IQR Median IQR Unadjusted Adjusted Classic androgens (nM) DHEA 7.04 3.82-13.47 9.44 4.95-17.36 0.061 0.453 A4 2.21 1.51-3.63 2.82 1.89-3.93 0.151 0.467 T 0.55 0.36-1.06 0.80 0.58-1.18 0.012 0.080 11-oxyandrogens (nM) 11OHA4 5.26 3.67-7.40 4.94 3.17-7.59 0.518 0.659 11KA4 0.53 0.25-0.70 0.57 0.39-0.89 0.249 0.295 11OHT 0.34 0.16-0.56 0.68 0.41-0.97 <0.0001 <0.0001 11KT 0.49 0.17-0.62 0.68 0.51-0.95 <0.0001 0.003 Glucocorticoids (nM) 17α-hydroxy-progesterone 0.81 0.56-1.63 1.13 0.65-1.87 0.105 0.286 11-deoxycortisol 0.50 0.14-0.99 0.63 0.36-1.02 0.344 0.351 Cortisol 542.10 459.30-648.60 482.20 350.80-581.00 0.075 0.027 Cortisone 43.09 31.69-58.10 51.15 35.51-63.73 0.108 0.230 Mineralocorticoids (nM) Corticosterone 14.07 9.18-20.81 12.62 7.75-23.41 0.646 0.357 Clinical biomarkers CA-125 (kU/L) 325.00 55.00-779.5 21.67 14.70-34.25 <0.0001 0.081 HE4 (pmol/L) 245.00 99.25-848.25 57.50 51.00-70.50 <0.0001 0.431 ROMA index score 84.39 57.57-96.02 14.24 9.40-21.41 <0.0001 <0.0001 Unadjusted p values were calculated using the non-parametric Mann-Whitney U test. Adjusted p value were calculated with robust ANCOVA (Analysis of Covariance) test with age at operation and menopause status as confounders. P values below 0.05 were considered statistically significant. 11OHA4, 11β-hydroxy-androstenedione; 11OHT, 11β-hydroxy-testosterone; 11KA4, 11-keto-androstenedione; 11KT, 11-keto-testosterone; A4, androstenedione; BOT, borderline ovarian tumor; CA-125, cancer antigen 125; DHEA, dehydroepiandrosterone; IQR, interquartile range; OC, ovarian cancer; T, testosterone. 3.3. Development of machine learning diagnostic models for ovarian cancer In univariate logistic regression, HE4 was the most effective biomarker for distinguishing primary/recurrent ovarian cancer from non-malignant adnexal masses, with an AUC of 0.873, sensitivity of 92.7%, and specificity of 70.4%. Among steroids, 11KT performed best (AUC: 0.709, sensitivity: 82.7%, specificity: 45.9%) (Table 3). Performance metrics for other steroid hormones are presented in Supplementary Table 4. For multivariate models, stepwise feature selection identified 11KT, 11OHA4, 11OHT, and age at operation as the top predictors for distinguishing malignant from non-malignant adnexal masses. The two-variable model incorporating 11KT and 11OHA4, achieved an AUC of 0.770. Adding 11OHT and age at operation improved the AUC to 0.813 (sensitivity: 85.2%, specificity: 64.5%). Incorporating CA-125 into this four-parameter model (11KT, 11OHA4, 11OHT, and age at operation) further increased the AUC to 0.907 (sensitivity: 88.8%, specificity: 82.0%), significantly outperforming CA-125 alone (AUC: 0.868, sensitivity: 87.5%, specificity: 71.1%; p = 0.0003) and the ROMA index (AUC: 0.884, sensitivity: 91.1%, specificity: 78.5%, p = 0.039) (Table 3, Figure 2A, C). Similarly, adding HE4 instead of CA-125 to the four-parameter model improved the AUC to 0.911 (sensitivity: 94.4%, specificity: 77.3%), significantly outperforming HE4 alone (p = 0.0001) and the ROMA index (p = 0.016) (Table 3, Figure 2B, C). In the subset analysis excluding borderline ovarian tumors from the control group, HE4 remained the best-performing single biomarker (AUC: 0.898), whereas 11OHT performed best among steroids (AUC: 0.701). The four-parameter model (11KT, 11OHA4, 11OHT, and age at operation) achieved an AUC of 0.823 (sensitivity: 72.8%, specificity: 73.8%). The four-parameter model combined with CA-125 achieved an AUC of 0.922 (sensitivity: 90.2%, specificity: 84.8%), significantly outperforming CA-125 alone (AUC: 0.872, sensitivity: 89.2%, specificity: 73.8%, p < 0.0001) and the ROMA index (AUC: 0.901, sensitivity: 92.7%, specificity: 78.9%, p = 0.03). Similarly, the four-parameter model combined with HE4 achieved an AUC of 0.932 (sensitivity: 90.3%, specificity: 83.3%), significantly outperforming HE4 alone (p = 0.0003) and the ROMA index (p = 0.004) (Supplementary Table 5). In the second sub-analysis comparing the models’ performance only on postmenopausal patients (34 cases vs 35 controls), the CA-125 was the best performing biomarker (AUC: 0.837), while 11OHT was the top steroid predictor (AUC: 0.705). The four-parameter model (11KT, 11OHA4, 11OHT, and age at operation) achieved an AUC of 0.777 (sensitivity: 78.9%, specificity: 69.9%). The four-parameter model plus CA-124 achieved an AUC of 0.873 (sensitivity: 85.4%, specificity: 82.6%), significantly outperforming CA-125 alone (AUC: 0.837, sensitivity: 84.1%, specificity: 69.8%); p = 0.002) as well as ROMA (AUC: 0.846, sensitivity: 85.7%, specificity: 79.2%; p = 0.041). The four-parameter model plus HE4 achieved an AUC of 0.872 (sensitivity: 91.3%, specificity: 75.5%), significantly outperforming HE4 alone (AUC: 0.832, sensitivity: 88.4%, specificity: 69.3%; p = 0.005), but performing comparably to ROMA (p = 0.065) (Supplementary Table 6). In the third sub-analysis focusing on HGSOC vs. non-malignant adnexal masses, again HE4 was the best-performing biomarker (AUC: 0.940), while 11KT was the top steroid predictor (AUC: 0.707). The four-parameter model (11KT, 11OHA4, 11OHT, and age at operation) achieved an AUC of 0.786 (sensitivity: 89.2%, specificity: 54.6%). The four-parameter model plus CA-125 achieved an AUC to 0.933 (sensitivity: 91.2%, specificity: 85.9%), significantly outperforming CA-125 alone (AUC: 0.907, sensitivity: 90.2%, specificity: 74.4%, p = 0.006) but performing comparably to ROMA (AUC: 0.921, sensitivity: 91.5%, specificity: 83.8%; p = 0.576). The four-parameter model plus HE4 achieved an AUC of 0.930 (sensitivity: 94.5%, specificity: 72.6%), performing slightly worse than HE4 alone (p = 0.042) but comparable to ROMA (p = 0.262) (Supplementary Table 7). Table 3: Diagnostic performance statistics of univariate and multivariate models by primary outcome definition of ovarian cancer (cases, n=43; controls, n=56). Model AUC Sensitivity (%) Specificity (%) PPV (%) NPV (%) F1 score (%) AIC Univariate 11OHT 0.705 (0.696-0.712) 79.5 (78.6-80.7) 49.3 (48.1-50.0) 67.6 (67.0-68.2) 64.3 (63.0-65.6) 73.1 (72.4-73.8) 122.741 11KT 0.709 (0.704-0.713) 82.7 (81.4-83.9) 45.9 (44.8-47.1) 67.1 (66.5-67.7) 66.6 (64.9-68.5) 74.1 (73.3-74.9) 119.641 CA-125 0.868 (0.862-0.873) 87.5 (87.1-87.5) 71.1 (70.0-71.9) 80.2 (79.5-80.6) 81.0 (80.5-81.2) 83.7 (83.3-83.9) 91.293 HE4 0.873 (0.865-0.878) 92.7 (921.-92.9) 70.4 (69.5-71.4) 80.7 (80.1-81.3) 87.9 (87.1-88.2) 86.3 (85.9-86.7) 88.717 Multivariate Best 2 parameters 0.770 (0.764-0.774) 88.0 (86.8-88.9) 51.5 (50.5-52.4) 70.7 (70.2-71.3) 76.3 (74.6-77.9) 78.4 (77.8-79.0) 112.820 Best 3 parameters 0.793 (0.787-0.797) 86.8 (85.7-87.5) 61.8 (60.0-63.3) 75.2 (74.3-76.0) 77.9 (76.4-78.9) 80.6 (79.9-81.3) 108.552 Best 4 parameters 0.813 (0.808-0.817) 85.2 (83.9-86.4) 64.5 (63.3-65.7) 76.2 (75.5-76.9) 76.6 (74.9-78.2) 80.5 (79.5-81.3) 104.380 ROMA index 0.884 (0.875-0.891) 91.1 (91.1-91-1) 78.5 (78.1-78.6) 85.0 (84.7-85.0) 86.8 (86.8-86.8) 87.9 (87.8-87.9) 79.334 Best 4 parameters + CA-125 0.907 (0.904-0.910) 88.9 (88.2-89.3) 82.0 (81.4-82.8) 86.4 (86.1-87.1) 84.9 (84.2-85.6) 87.6 (87.1-88.0) 79.293 Best 4 parameters + HE4 0.911 (0.908-0.913) 94.4 (94.0-94.6) 77. 3 (76.7-77.7) 84.4 (84.1-84.7) 91.3 (90.8-91.7) 89.1 (88.9-89.3) 80.428 11OHT, 11β-hydroxy-testosterone; 11KT, 11-keto-testosterone; AIC, akaike information criteria; AUC, area under the curve; CA-125, cancer antigen 125; NPV, negative predictive value; PPV, positive predictive value; Multivariate model with best 2 parameters=11KT + 11OHA4; model with best 3 parameters=11KT + 11OHA4 + age; model with best 4 parameters=11KT + 11OHA4 + age + 11OHT. 4. Discussion In this discovery study, we analyzed preoperative steroid hormone levels in 99 women with adnexal masses to evaluate their potential in distinguishing ovarian cancer from benign conditions. Incorporating 11-oxyandrogens, patient age, and either CA-125 or HE4, we developed two steroid-protein diagnostic models. Both models outperformed CA-125 or HE4 alone (p < 0.001 for both), and the ROMA index (p < 0.05 for both) in differentiating malignant from non-malignant adnexal masses, highlighting the added diagnostic value of steroid hormone profiling. The superior performance of the steroid-protein models remained robust after excluding borderline ovarian tumors from the control group, and when testing the models on postmenopausal patients only. The enhanced performance of these models likely reflects the contribution of 11-oxyandrogens, which may capture tumor-related steroidogenic activity not detected by conventional markers. To the best of our knowledge, this is the first study to characterize 11-oxyandrogen levels in women with adnexal masses. It is also the first study to apply machine learning to evaluate circulating steroids alone and in combination with proteins for distinguishing malignant from non-malignant cases. Given that 11-oxyandrogens are altered in hormone-sensitive cancers 20 , 21 , their integration into diagnostic models represents a novel and biologically relevant improvement. Apart from hormone-sensitive cancers, 11-oxyandrogens have been linked to disorders such as congenital adrenal hyperplasia and PCOS 22 . Their elevated levels in PCOS suggest a role of adipose tissue in regulation. However, in our study, 11OHT, 11KT, and T were lower in cases than in controls, despite no significant differences in body weight. One possible explanation is intra-tumoral steroid metabolism. We recently showed that HGSOC tumors, particularly the epithelial cell population, express key steroid-metabolizing enzymes 23 , potentially accounting for systemic reductions in these steroids. Further research on the steroid conjugation potential of ovarian tumors is needed. If these metabolites are produced locally in ovarian tumors, they could impact key cellular processes, as 11KT and 11-keto-DHT activate the androgen receptor with a potency comparable to testosterone and DHT, respectively 24 – 26 . Androgen receptor expression has been linked to improved survival and greater platinum sensitivity in HGSOC 27 , though mechanistic studies on its signaling in ovarian tumors are lacking. In contrast to 11-oxyandrogens, classic androgens have already been investigated as risk factors for ovarian cancer, however data are conflicting. Some studies suggest higher T levels may increase the risk of endometrioid and mucinous ovarian tumors 28 , while others found no link 29 – 31 . Contrariwise, Mendelian randomization studies suggest higher T might lower the risk of ovarian cancer 32 , including HGSOC 33 . Glucocorticoids have also been linked to ovarian cancer progression. Schrepf et al. reported that patients with ovarian cancer exhibited disrupted cortisol rhythms before surgery 34 , which may explain the higher cortisol levels we observed in cases compared to controls in our study. Disrupted cortisol rhythms have also been associated with systemic inflammation and poorer survival 34 , whereas chemotherapy was shown to normalize these rhythms 35 . Our study has several limitations. First, it is based on a small, single-center cohort. Second, the technical constraints of the LC-MS/MS method prevented quantification of low-concentration steroids like DHT. Third, the cohort includes a small number of patients with early-stage disease. Since early diagnosis significantly improves ovarian cancer survival, developing accurate and sensitive diagnostic tools for early detection remains a major clinical challenge. Fourth, the small number of premenopausal patients limited evaluation of the models in this group. Although 11-oxyandrogens are stable throughout life, one key steroid in our models, 11OHT, differs significantly between pre- and postmenopausal women 36 . This difference may introduce variability or limit the models' applicability in premenopausal patients, so this should be investigated further. Additionally, we could not assess model performance in patients with low CA-125 levels (< 35 kU/L), where CA-125 alone is unreliable. Finally, while cross-validation was performed, external validation in an independent cohort is needed to confirm the models' clinical utility. Nonetheless, our study has key strengths. The steroid-protein models enhance the distinction between malignant and benign adnexal masses, improving sensitivity and specificity. This could improve preoperative risk assessment, potentially leading to more accurate triage and better clinical decision-making. It can also reduce unnecessary surgeries for benign conditions, and ensure timely treatment for ovarian cancer, particularly when conventional biomarkers are inconclusive. By incorporating 11-oxyandrogens, these models provide a more comprehensive biochemical profile beyond CA-125, HE4, or the ROMA index, further strengthening their diagnostic utility. Since mass spectrometry for steroid profiling is already established in clinical practice, these models could be readily implemented after validation. Furthermore, by incorporating additional preoperative clinical data, such as ultrasound features based on IOTA criteria and computed tomography imaging characteristics of adnexal masses, these steroid-protein models could achieve greater accuracy. With validation in a larger cohort, they could become valuable diagnostic tools. 5. Conclusions In conclusion, circulating steroid hormones—particularly 11-oxyandrogens—offer a valuable and noninvasive biomarker class for improving the preoperative differentiation of malignant and benign adnexal masses. When combined with CA-125 or HE4, these hormones significantly enhance diagnostic accuracy beyond conventional markers and the ROMA index. The steroid-protein models remain robust across clinical subgroups and provide insights into tumor-specific steroidogenic activity, supporting their potential as a clinically meaningful tool for risk stratification and improved surgical decision-making in women with adnexal masses. Abbreviations 11KA4 11-keto-androstenedione 11KT 11-keto-testosterone 11OHA4 11β-hydroxy-androstenedione 11OHT 11β-hydroxy-testosterone 17OHP4 17α-hydroxy-progesterone A4 Androstenedione ADNEX Assessment of different neoplasias in the adnexa AIC Akaike information criteria ANCOVA Analysis of covariance AUC Area under the receiver operating curve BOT Borderline Ovarian Tumor CA125 Cancer antigen 125 DHEA Dehydroepiandrosterone DHT 5α-dihydrotestosterone ECLIA Electroluminescent immunoassays FIGO International Federation of Gynecologic Oncology HE4 Human epididymis protein 4 HGSOC High-grade serous ovarian cancer ICCR International Collaboration on Cancer Reporting IOTA International Ovarian Tumor Analysis IQR Interquartile range LC-MS/MS Liquid chromatography-tandem mass spectrometry LLOQ Lower limit of quantification NPV Negative predictive value OC Ovarian cancer ORADS Ovarian-Adnexal Reporting and Data System PCOS Polycystic ovary syndrome PPV Positive predictive value RMI1 Risk of Malignancy Index 1 ROMA Risk of Ovarian Malignancy Algorithm T Testosterone WHO World Health Organization Declarations Supplementary Information: Supplementary data related to this article can be found in the online version of the article. Acknowledgements: We thank Ms. Vesna Sekelj Rangus from the Department of Gynecology and Obstetrics and Ms. Vera Troha Poljančič from the Clinical Institute of Chemistry and Clinical Biochemistry, University Medical Center, Ljubljana for their help in sample collection and processing. We also thank Ms. Vera Troha Poljančič and Prof. Joško Osredkar from the Clinical Institute of Chemistry and Clinical Biochemistry, University Medical Center, Ljubljana for additional CA-125 and HE4 measurements in a subset of patients. Credit authorship contribution statement: Marija Gjorgoska: Conceptualization, Data curation; Formal analysis, Investigation; Methodology, Visualization, Writing - original draft, Writing - review & editing. Boštjan Pirš, Methodology, Writing - review and editing. Špela Smrkolj: Writing - review and editing. Tea Lanišnik Rižner: Conceptualization, Funding acquisition, Supervision, Writing - review & editing. Funding: This study was funded by the Slovenian Research Agency, grant number J3-2535 and program funding P3-0449, both to T.L.R. Data availability: All data related to this article can be found in supplementary materials. Ethics approval and consent to participate The study was approved by the Slovenian Ethics Committee ((ID: 0120-487/2020/3) and carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent was obtained from each patient before sample collection. Consent for publication: Not applicable. Competing interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author details: 1 Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia 2 Department of Gynecology, University Medical Centre, Šlajmerjeva 3, 1000 Ljubljana, Slovenia 3 Department of Gynecology and Obstetrics, Faculty of Medicine, University of Ljubljana, Šlajmerjeva 3, 1000 Ljubljana, Slovenia References Sung H, Ferlay J, Siegel RL et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. https://doi.org/10.3322/caac.21660 . Cancer J Clin. 2021/05/01 2021;71(3):209–49. doi:https://doi.org/10.3322/caac.21660. Ruhl J, Callaghan C, Hurlbut A et al. Summary stage 2018: codes and coding instructions. Bethesda MD: Natl Cancer Inst. 2018. American Cancer Society. Ovarian cancer survival rates. Ovarian cancer early detection, diagnosis, and staging [Internet]. 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PCOS, polycystic ovary syndrome.\u003c/p\u003e","description":"","filename":"floatimage136.png","url":"https://assets-eu.researchsquare.com/files/rs-6596566/v1/abe5ff93bbad6d71446ba92b.png"},{"id":82711620,"identity":"70d767cc-8942-40cb-99ba-3c46cf5d3b34","added_by":"auto","created_at":"2025-05-14 11:35:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":200787,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of logistic regression models in detecting ovarian cancer. (A) AUC curves comparing the Best 4 + CA-125 model to CA-125 alone for distinguishing ovarian cancer (n=43) from non-malignant adnexal masses (n=56). (B) AUC curves comparing the Best 4 + HE4 model to HE4 alone for distinguishing ovarian cancer (n=43) from non-malignant adnexal masses (n=56). (C) AUC curve comparison of the Best 4 + CA-125 model, the Best 4 + HE4 model, and the ROMA index for distinguishing ovarian cancer (n=43) from non-malignant adnexal masses (n=56). The Best 4 model includes 11KT, 11OHA4, age, and 11OHT as predictive parameters. 11OHA4, 11β-hydroxy-androstenedione; 11OHT, 11β-hydroxy-testosterone; 11KT, 11-keto-testosterone; AUC, area under the receiver operating characteristic curve; CA-125, cancer antigen 125; HE4, human epididymis protein 4; ROMA, risk of ovarian malignancy algorithm.\u003c/p\u003e","description":"","filename":"floatimage221.png","url":"https://assets-eu.researchsquare.com/files/rs-6596566/v1/1a746c647d0d2fad9f668799.png"},{"id":96650994,"identity":"a9990db9-126e-421d-8685-9946a16fbf4f","added_by":"auto","created_at":"2025-11-24 16:13:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1704019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6596566/v1/55ae4794-b482-497c-9162-4c3e77a945f2.pdf"},{"id":82711622,"identity":"4ae8019a-5d68-4f3c-b258-9cb2b38dea6d","added_by":"auto","created_at":"2025-05-14 11:35:05","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":47553,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesSteroidbasedbiopsy2025.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6596566/v1/09b5f57fb1a7acc9a2dc0b2c.xlsx"},{"id":82711624,"identity":"8abd2858-aef2-4327-91fa-867f212059d8","added_by":"auto","created_at":"2025-05-14 11:35:06","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":241721,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6596566/v1/e455403a7fcb888ff416c5e8.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel serum-based steroid-protein panels for differentiating ovarian cancer from non-malignant adnexal masses","fulltext":[{"header":"1. Background","content":"\u003cp\u003eOvarian cancer (OC) is the eighth most diagnosed cancer in women and most common cause of gynecologic cancer death in developed countries. In 2022, approximately 324,603 new cases and 206,956 deaths were reported globally \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Epithelial cancer is the most common histological type, encompassing four subtypes: serous, clear cell, mucinous, and endometrioid carcinomas. Prognosis varies significantly by stage, with 5-year survival rates of 93% for cancer confined to the ovaries (localized), 75% for cancer that has spread nearby (regional), and only 31% for cancer that has spread to distant organs (distant) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Unfortunately, over a third of cases are diagnosed at an advanced stage, largely due to nonspecific symptoms.\u003c/p\u003e \u003cp\u003eMost symptomatic patients or those with abnormal clinical findings, such as suspicious ultrasound results or elevated cancer antigen 125 (CA-125) levels, do not have ovarian cancer. A study found that only 3% of premenopausal and 18% of postmenopausal patients referred through the UK\u0026rsquo;s National Health Services (NHS) expedited pathway were actually diagnosed with ovarian cancer \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Clearly, accurate, accessible, and cost-effective diagnostic methods are needed to distinguish malignant tumors from non-malignant adnexal masses and reduce unnecessary procedures.\u003c/p\u003e \u003cp\u003eCurrently, several risk-prediction models, based on either ultrasound features or blood biomarker levels exist that can be used to triage patients with an adnexal mass. Several tools are currently used to triage patients with adnexal masses, including CA-125, HE4, the Risk of Malignancy Index (RMI1), the Risk of Ovarian Malignancy Algorithm (ROMA), the International Ovarian Tumor Analysis (IOTA) Simple Rules, the IOTA Assessment of Different Neoplasias in the Adnexa (ADNEX) model, and the Ovarian-Adnexal Reporting and Data System (ORADS) \u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These diagnostic tools differ in performance. CA-125 at the 35 kU/L threshold has high sensitivity (\u0026gt;\u0026thinsp;80%) but low specificity (\u0026lt;\u0026thinsp;80%); RMI1, ROMA and IOTA Simple Rules achieve a better balance, exceeding 80% sensitivity and specificity for diagnosing ovarian cancer in symptomatic postmenopausal women; ADNEX has very high sensitivity (\u0026gt;\u0026thinsp;95%) but low specificity (\u0026lt;\u0026thinsp;60%), leading to more false positives \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCA-125, used alone or as part of ROMA, lacks specificity due to its elevation in benign conditions common in premenopausal patients such as menstruation, endometriosis, and peritoneal inflammation \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, as well as conditions common in postmenopausal and elderly patients such as heart failure, cirrhosis, chronic kidney disease, and intraabdominal infections. Other models relying on ultrasound findings require validation in routine practice, as scans are often performed by non-specialists. Emerging molecular biomarkers, including circulating tumor DNA, tumor cells, exosomes, and metabolites, show promise for ovarian cancer diagnosis but remain unadopted due to limited validation and high costs \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study investigates serum steroids as biomarkers for diagnosing ovarian cancer in patients with adnexal masses, using machine learning. We measured preoperative serum levels of classic androgens, 11-oxyandrogens, glucocorticoids, and mineralocorticoids in 43 patients with primary/recurrent ovarian cancer and 56 with non-neoplastic adnexal masses, benign or borderline ovarian tumors, using a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Logistic regression was used to assess the diagnostic potential of these hormones, alone and in combination with CA-125, human epididymis protein 4 (HE4), and clinical parameters.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Population and Design\u003c/h2\u003e \u003cp\u003e This prospective, single-center study was approved by the Medical Ethics Committee of the Republic of Slovenia (ID: 0120\u0026ndash;487/2020/3) and carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Adult women (\u0026ge;\u0026thinsp;18 years) who were scheduled for surgery for an adnexal mass or presumed/histologically proven ovarian cancer at the Department of Gynecology, University Medical Center Ljubljana, were included in this study. Exclusion criteria were age\u0026thinsp;\u0026lt;\u0026thinsp;18, active non-ovarian malignancy, previous ovarian malignancy, presence of polycystic ovary syndrome (PCOS) and non-ovarian endometriosis.\u003c/p\u003e \u003cp\u003eSample collection occurred from December 2021 to February 2025, with written informed consent obtained from all participants. Blood samples were collected in the morning, 1\u0026ndash;7 days before surgery, alongside lifestyle, medication, and clinical data. Blood samples (6 mL) were drawn into clot-activator vacutainers (BD Vacutainer, #368815), incubated at room temperature (min 20, max 60 min), and centrifuged (2000 g, 15 min), following standardized procedure for metabolomics studies \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Serum was aliquoted to minimize freeze-thaw effects and stored at -80\u0026deg;C. Tissue specimens were examined by a certified pathologist according to WHO Classification of tumors, 5th edition and ICCR dataset \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Outcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome was the diagnostic accuracy of steroids for detecting ovarian cancer (binary outcome), defined as primary or recurrent malignant ovarian neoplasms (n\u0026thinsp;=\u0026thinsp;43) versus non-malignant adnexal masses (defined as non-neoplastic adnexal masses or benign or borderline ovarian tumors) (n\u0026thinsp;=\u0026thinsp;56), confirmed by histological examination obtained by surgery (reference standard). Three additional sub-analyses were performed to further evaluate the models. First, model performance was assessed upon excluding borderline ovarian tumors from the control group (n\u0026thinsp;=\u0026thinsp;15). Second, the models were assessed specifically in postmenopausal patients, comparing primary/recurrent ovarian cancer (n\u0026thinsp;=\u0026thinsp;34) to postmenopausal controls (n\u0026thinsp;=\u0026thinsp;35). Third, model performance was evaluated in distinguishing high-grade serous ovarian cancer (HGSOC) (n\u0026thinsp;=\u0026thinsp;32) from non-malignant adnexal masses (n\u0026thinsp;=\u0026thinsp;56).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)\u003c/h2\u003e \u003cp\u003eUnconjugated steroids (n\u0026thinsp;=\u0026thinsp;17) were quantified using a previously validated multi-steroid profiling LC-MS/MS assay \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Schematic representation of the position of these steroids in steroid biosynthesis is given in Supplementary Fig.\u0026nbsp;1. Briefly, 180 \u0026micro;L of thawed serum, calibrators, and QC samples were mixed with stable isotope-labeled internal standards ([\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC\u003csub\u003e3\u003c/sub\u003e]-dehydroepiandrosterone (DHEA), [\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC\u003csub\u003e3\u003c/sub\u003e]-androstenedione (A4), [\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC\u003csub\u003e3\u003c/sub\u003e]-testosterone (T), 5α-dihydrotestosterone (DHT)-d\u003csub\u003e3\u003c/sub\u003e) and incubated for 15 minutes. After protein precipitation with 180 \u0026micro;L 3 M Na₂SO₄, samples were extracted using methyl-\u003cem\u003etert\u003c/em\u003e-butyl ether (MTBE). The organic layer was evaporated and the extracted analytes were reconstituted in LC-MS grade methanol-water (50:50) (v/v). Steroids were chromatographically separated using a Phenomenex Kinetex XB-C18 column on a Shimadzu Nexera LC system, with mass spectrometry detection on a Sciex 3500 triple quadrupole mass spectrometer using electrospray ionization. Data acquisition was performed using Analyst 1.6.2.\u003c/p\u003e \u003cp\u003eSteroid quantification was based on peak area ratios of analytes to internal standards. All samples were anonymized prior to analysis. Steroid concentrations below the lower limit of quantification (LLOQ) were replaced by 0.5 x LLOQ for statistical analysis. Aldosterone, 5α-dihydrotestosterone (DHT), and progesterone were below the lower limit of quantification (LLOQ) in 69.2%, 58.6%, and 39.4%, respectively, and were excluded from further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Proteins\u003c/h2\u003e \u003cp\u003eSerum CA-125 (kU/L) and HE4 (pmol/L) levels were measured at the Clinical Institute for Clinical Biochemistry, University Medical Center, Ljubljana, using clinically validated electroluminescent immunoassays (ECLIAs), for CA-125, REF: 11776223190, for HE4, REF: 05950929190, on a Cobas e411 analyzer (Roche Diagnostics GmbH, Mannheim, Germany). The ROMA index score was calculated based on the formula established by Moore and collaborators \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eData were anonymized and analyzed using R Studio (version 4.3.0 or higher). Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Continuous variables were expressed as median values with interquartile range (IQR) and compared using the Mann-Whitney U test. Robust ANCOVA (Analysis of Covariance) was used to adjust for age and menopause status. Categorical variables were expressed as frequencies with percentages and compared using Fisher exact test or Chi square test of independence.\u003c/p\u003e \u003cp\u003eMachine learning was performed using the caret library \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, with a 5\u0026times;5-fold cross-validation protocol (1000 iterations). We tested 20 variables, including 12 steroid hormones, 3 steroid pools (classic androgen (sum of dehydroepiandrosterone (DHEA), androstenedione (A4) and testosterone (T)), 11-oxyandrogen (sum of 11β-hydroxy-androstenedione (11OHA4), 11-keto-androstenedione (11KA4), 11β-hydroxy-testosterone (11OHT), 11-keto-testosterone (11KT)), glucocorticoid (sum of 17-hydroxyprogesterone, 11-deoxycortisol, cortisol, cortisone)), two proteins (CA-125, HE4), ROMA index and 2 clinical parameters (age, menopause status). Continuous variables were ln-transformed and standardized. Feature selection was performed via stepAIC (MASS library). Multicollinearity was assessed using the Variance Inflation Factor (VIF) in a logistic regression model, with acceptable values\u0026thinsp;\u0026lt;\u0026thinsp;5. Diagnostic accuracy was assessed using the area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score and Akaike information criteria (AIC). DeLong\u0026rsquo;s test was used to compare AUCs between different models.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp;Description of the cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween December 2021 and February 2025, a total of 103 participants were recruited, and preoperative morning serum samples were collected (Figure 1). Among them, 47 (45.6%) had ovarian cancer, including 42 (89.4%) with primary ovarian cancer, one (2.1%) with recurrent disease, and four (8.5%) with metastatic disease from other primary sites. Participants with metastatic disease were excluded from the primary outcome analysis. Of the remaining 43 participants with ovarian cancer, 39 (90.7%) had epithelial tumors, with 32 (74.4%) classified as HGSOC, 2 (4.7%) as clear-cell, 2 (4.7%) as endometrioid, 2 (4.7%) as carcinosarcoma, and 1 (2.3%) as low-grade serous carcinoma. The remaining 4 cases (9.3%) had sex-cord stromal malignant tumors, including 3 (75%) with adult granulosa cell tumors and 1 (25%) with high-grade Sertoli-Leydig tumor. Regarding cancer staging, 7 (16.3%) cases were diagnosed at FIGO stage I, 2 (4.7%) at FIGO stage II, 26 (60.5%) at FIGO stage III, and 6 (14.0%) at FIGO stage IV; data was missing for two patients (4.7%). In terms of treatment status, 39 patients (90.7%) were treatment-na\u0026iuml;ve, four (9.3%) had been admitted after receiving neoadjuvant chemotherapy.\u003c/p\u003e\n\u003cp\u003eThe control group consisted of 56 participants with non-malignant adnexal masses, including 8 (14.3%) with non-neoplastic adnexal masses, 33 (58.9%) with benign ovarian tumors, and 15 (26.8%) with borderline ovarian tumors. Among borderline ovarian tumors, 10 (66.7%) were FIGO stage I, one (6.7%) was FIGO stage II, and 3 (20%) were FIGO stage III. Table 1 provides an overview of the basic demographics and clinical characteristics of the cohort. Significant differences between cases and controls were observed in age at operation, height, and use of oral contraceptives. No significant differences were observed between the groups in terms of weight, body mass index, menopausal status, smoking status, use of hormonal replacement therapy (past or in the previous 3 months), and parity. A detailed description of the clinical and histopathological characteristics of the study participants is provided in Supplementary Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eDemographics and clinical characteristics of participants, by primary outcome definition of ovarian cancer.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 291px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis based on reference standard\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary/recurrent ovarian cancer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-malignant adnexal masses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll participants (n=99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years); p=0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e63.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(53.00-68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e54.50\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(47.00-64.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e57.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(50.00-65.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight (cm); p=0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e164.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(159.20-167.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e165.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(162.00-170.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e165.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(160.00-169.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e7 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e12 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e66.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(59.00-75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e65.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(60.00-80.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e65.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(59.00-75.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e6 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e11 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e23.00\u003c/p\u003e\n \u003cp\u003e(22.00-27.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e23.80\u003c/p\u003e\n \u003cp\u003e(22.00-28.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e23.75\u003c/p\u003e\n \u003cp\u003e(22.00-28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e6 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e7 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e13 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopausal status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003ePremenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e9 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e21 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e30 (30.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003ePostmenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e34 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e35 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e69 (69.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace or ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e18 (41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e32 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e50 (50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e25 (58.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e24 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e49 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e10 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e21 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e31 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eEx-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4 (9.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e8 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e12 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e24 (55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e24 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e48 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHormonal replacement therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e15 (34.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e29 (51.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e44 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e25 (58.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e25 (44.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e50 (50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHormonal replacement therapy in the past 3 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e17 (39.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e30 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e47 (47.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e24 (55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e25 (44.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e49 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOral contraceptive use; p=0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e12 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e14 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e26 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e18 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e22 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e27 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e24 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e51 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003cp\u003e(1.00-2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003cp\u003e(1.50-2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.00-2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e21 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e21 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e42 (42.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor stage according to FIGO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e7 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e10 (BOTs, 66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e17 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1 (BOTs, 6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e26 (60.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3 (BOTs, 20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e29 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e6 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathological type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Epithelial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e39 (90.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e39 (39.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; High-grade serous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e32 (74.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e32 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Low-grade serous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Clear cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Endometrioid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Carcinosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Sex-cord stromal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary/recurrent disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003ePrimary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e42 (97.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e42 (42.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eRecurrent disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eTreatment-na\u0026iuml;ve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e39 (90.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e39 (39.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003ePost NACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-malignant adnexal masses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003eNon-neoplastic adnexal masses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Normal adnexa with other pelvic pathology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e5 (.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Sactosalpinx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Paraovarian cyst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Ovarian torsion with necrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003e\u0026nbsp;Benign ovarian tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Simple cyst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e9 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mature teratoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e6 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Fibroma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Endometrioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Serous cystadenoma or cystadenofibroma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e7 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mucinous cystadenoma or cystadenofibroma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 548px;\"\u003e\n \u003cp\u003eBorderline ovarian tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Serous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e7 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mucinous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e6 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Sero-mucinous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e The table is at the end of the manuscript as it is longer than one A4. It should be placed here.\u003c/p\u003e\n\u003cp\u003eData are n (%) unless otherwise specified. Statistical significance was determined by Mann-Whitney U test for continuous variables and Fisher exact test or Chi square test of independence for categorical variables. Two-sided p values below the significance threshold 0.05 are reported. All participants were female. BOT, borderline ovarian tumor; FIGO, the International Federation of Gynecology and Obstetrics; NACT, neoadjuvant chemotherapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.\u0026nbsp;Preoperative serum steroid levels differ between patients with ovarian cancer and control women with non-malignant adnexal masses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreoperative serum steroid levels differed between patients with ovarian cancer and patients with non-malignant adnexal masses (Table 2). Univariate analysis showed significantly lower levels of T, 11OHT, and 11KT in cases compared to controls (p=0.012, \u0026lt;0.001, \u0026lt;0.001, respectively). After adjusting for age and menopause, 11OHT and 11KT remained significantly lower (p\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.001, 0.003, respectively), while T showed borderline significance (p\u003csub\u003eadj\u003c/sub\u003e=0.08). In contrast, cortisol levels were higher in cases vs controls, after adjusting for age and menopause (p\u003csub\u003eadj\u003c/sub\u003e: 0.027). No other significant hormone differences were found between the two groups. As expected, patients with ovarian cancer had higher CA-125 and HE4 levels as well as higher ROMA index score compared to controls. These trends of lower T, 11OHT, and 11KT and higher cortisol levels in cases compared to controls were consistent when patients with borderline ovarian tumors were excluded from the control group (Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003eIn a separate sub-analysis comparing patients with HGSOC to controls with non-malignant adnexal masses, similar trends were observed. Patients with HGSOC had significantly lower levels of T (p\u003csub\u003eadj\u003c/sub\u003e=0.012), 11OHT (p\u003csub\u003eadj\u003c/sub\u003e=0.001), and 11KT (p\u003csub\u003eadj\u003c/sub\u003e=0.003), as well as higher cortisol levels (p\u003csub\u003eadj\u003c/sub\u003e=0.039), compared to controls (Supplementary Table 3). Cases with HGSOC had also significantly higher levels of CA-125, HE4 levels and ROMA index score compared to controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Preoperative serum concentrations of steroid hormones and CA-125 in patients with ovarian cancer (n=43) and controls (n=56), based on the primary outcome definition of ovarian cancer.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary/recurrent OC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-malignant adnexal masses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalyte\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIQR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIQR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 606px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClassic androgens (nM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDHEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e3.82-13.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.95-17.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.51-3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1.89-3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.36-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.58-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 606px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11-oxyandrogens (nM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11OHA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e3.67-7.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e3.17-7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11KA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.25-0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.39-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11OHT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.16-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.41-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11KT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.17-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.51-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 606px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucocorticoids (nM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e17\u0026alpha;-hydroxy-progesterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.56-1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.65-1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11-deoxycortisol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.14-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.36-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCortisol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e542.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e459.30-648.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e482.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e350.80-581.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCortisone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e43.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e31.69-58.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e51.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e35.51-63.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 606px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMineralocorticoids (nM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCorticosterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e14.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e9.18-20.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e12.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e7.75-23.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 606px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical biomarkers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCA-125 (kU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e325.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e55.00-779.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e21.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e14.70-34.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eHE4 (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e245.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e99.25-848.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e57.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e51.00-70.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eROMA index score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e84.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e57.57-96.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e14.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.40-21.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUnadjusted p values were calculated using the non-parametric Mann-Whitney \u003cem\u003eU\u003c/em\u003e test. Adjusted p value were calculated with robust ANCOVA (Analysis of Covariance) test with age at operation and menopause status as confounders. P values below 0.05 were considered statistically significant. 11OHA4, 11\u0026beta;-hydroxy-androstenedione; 11OHT, 11\u0026beta;-hydroxy-testosterone; 11KA4, 11-keto-androstenedione; 11KT, 11-keto-testosterone; A4, androstenedione; BOT, borderline ovarian tumor; CA-125, cancer antigen 125; DHEA, dehydroepiandrosterone; IQR, interquartile range; OC, ovarian cancer; T, testosterone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.\u0026nbsp;Development of machine learning diagnostic models for ovarian cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn univariate logistic regression, HE4 was the most effective biomarker for distinguishing primary/recurrent ovarian cancer from non-malignant adnexal masses, with an AUC of 0.873, sensitivity of 92.7%, and specificity of 70.4%. Among steroids, 11KT performed best (AUC: 0.709, sensitivity: 82.7%, specificity: 45.9%) (Table 3). Performance metrics for other steroid hormones are presented in Supplementary Table 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor multivariate models, stepwise feature selection identified 11KT, 11OHA4, 11OHT, and age at operation as the top predictors for distinguishing malignant from non-malignant adnexal masses. The two-variable model incorporating 11KT and 11OHA4, achieved an AUC of 0.770. Adding 11OHT and age at operation improved the AUC to 0.813 (sensitivity: 85.2%, specificity: 64.5%). Incorporating CA-125 into this four-parameter model (11KT, 11OHA4, 11OHT, and age at operation) further increased the AUC to 0.907 (sensitivity: 88.8%, specificity: 82.0%), significantly outperforming CA-125 alone (AUC: 0.868, sensitivity: 87.5%, specificity: 71.1%; p = 0.0003) and the ROMA index (AUC: 0.884, sensitivity: 91.1%, specificity: 78.5%, p = 0.039) (Table 3, Figure 2A, C). Similarly, adding HE4 instead of CA-125 to the four-parameter model improved the AUC to 0.911 (sensitivity: 94.4%, specificity: 77.3%), significantly outperforming HE4 alone (p = 0.0001) and the ROMA index (p = 0.016) (Table 3, Figure 2B, C).\u003c/p\u003e\n\u003cp\u003eIn the subset analysis excluding borderline ovarian tumors from the control group, HE4 remained the best-performing single biomarker (AUC: 0.898), whereas 11OHT performed best among steroids (AUC: 0.701). The four-parameter model (11KT, 11OHA4, 11OHT, and age at operation) achieved an AUC of 0.823 (sensitivity: 72.8%, specificity: 73.8%). The four-parameter model combined with CA-125 achieved an AUC of 0.922 (sensitivity: 90.2%, specificity: 84.8%), significantly outperforming CA-125 alone (AUC: 0.872, sensitivity: 89.2%, specificity: 73.8%, p \u0026lt; 0.0001) and the ROMA index (AUC: 0.901, sensitivity: 92.7%, specificity: 78.9%, p = 0.03). Similarly, the four-parameter model combined with HE4 achieved an AUC of 0.932 (sensitivity: 90.3%, specificity: 83.3%), significantly outperforming HE4 alone (p = 0.0003) and the ROMA index (p = 0.004) (Supplementary Table 5).\u003c/p\u003e\n\u003cp\u003eIn the second sub-analysis comparing the models\u0026rsquo; performance only on postmenopausal patients (34 cases vs 35 controls), the CA-125 was the best performing biomarker (AUC: 0.837), while 11OHT was the top steroid predictor (AUC: 0.705). The four-parameter model (11KT, 11OHA4, 11OHT, and age at operation) achieved an AUC of 0.777 (sensitivity: 78.9%, specificity: 69.9%). The four-parameter model plus CA-124 achieved an AUC of 0.873 (sensitivity: 85.4%, specificity: 82.6%), significantly outperforming CA-125 alone (AUC: 0.837, sensitivity: 84.1%, specificity: 69.8%); p = 0.002) as well as ROMA (AUC: 0.846, sensitivity: 85.7%, specificity: 79.2%; p = 0.041). The four-parameter model plus HE4 achieved an AUC of 0.872 (sensitivity: 91.3%, specificity: 75.5%), significantly outperforming HE4 alone (AUC: 0.832, sensitivity: 88.4%, specificity: 69.3%; p = 0.005), but performing comparably to ROMA (p = 0.065) (Supplementary Table 6).\u003c/p\u003e\n\u003cp\u003eIn the third sub-analysis focusing on HGSOC vs. non-malignant adnexal masses, again HE4 was the best-performing biomarker (AUC: 0.940), while 11KT was the top steroid predictor (AUC: 0.707). The four-parameter model (11KT, 11OHA4, 11OHT, and age at operation) achieved an AUC of 0.786 (sensitivity: 89.2%, specificity: 54.6%). The four-parameter model plus CA-125 achieved an AUC to 0.933 (sensitivity: 91.2%, specificity: 85.9%), significantly outperforming CA-125 alone (AUC: 0.907, sensitivity: 90.2%, specificity: 74.4%, p = 0.006) but performing comparably to ROMA (AUC: 0.921, sensitivity: 91.5%, specificity: 83.8%; p = 0.576). The four-parameter model plus HE4 achieved an AUC of 0.930 (sensitivity: 94.5%, specificity: 72.6%), performing slightly worse than HE4 alone (p = 0.042) but comparable to ROMA (p = 0.262) (Supplementary Table 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Diagnostic performance statistics of univariate and multivariate models by primary outcome definition of ovarian cancer (cases, n=43; controls, n=56).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF1 score\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 576px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e11OHT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003cp\u003e(0.696-0.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003cp\u003e(78.6-80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e49.3\u003c/p\u003e\n \u003cp\u003e(48.1-50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e67.6\u003c/p\u003e\n \u003cp\u003e(67.0-68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003cp\u003e(63.0-65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e73.1\u003c/p\u003e\n \u003cp\u003e(72.4-73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e122.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e11KT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.709\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.704-0.713)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e82.7\u003c/p\u003e\n \u003cp\u003e(81.4-83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e45.9\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(44.8-47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e67.1\u003c/p\u003e\n \u003cp\u003e(66.5-67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e66.6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(64.9-68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e74.1\u003c/p\u003e\n \u003cp\u003e(73.3-74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e119.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eCA-125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003cp\u003e(0.862-0.873)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003cp\u003e(87.1-87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003cp\u003e(70.0-71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e80.2\u003c/p\u003e\n \u003cp\u003e(79.5-80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e81.0\u003c/p\u003e\n \u003cp\u003e(80.5-81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e83.7\u003c/p\u003e\n \u003cp\u003e(83.3-83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e91.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eHE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003cp\u003e(0.865-0.878)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e92.7\u003c/p\u003e\n \u003cp\u003e(921.-92.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e70.4\u003c/p\u003e\n \u003cp\u003e(69.5-71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e80.7\u003c/p\u003e\n \u003cp\u003e(80.1-81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e87.9\u003c/p\u003e\n \u003cp\u003e(87.1-88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e86.3\u003c/p\u003e\n \u003cp\u003e(85.9-86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e88.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 576px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eBest 2 parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.770\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.764-0.774)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e88.0\u003c/p\u003e\n \u003cp\u003e(86.8-88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003cp\u003e(50.5-52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e70.7\u003c/p\u003e\n \u003cp\u003e(70.2-71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e76.3\u003c/p\u003e\n \u003cp\u003e(74.6-77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e78.4\u003c/p\u003e\n \u003cp\u003e(77.8-79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e112.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eBest 3 parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.793\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.787-0.797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e86.8\u003c/p\u003e\n \u003cp\u003e(85.7-87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e61.8\u003c/p\u003e\n \u003cp\u003e(60.0-63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e75.2\u003c/p\u003e\n \u003cp\u003e(74.3-76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e77.9\u003c/p\u003e\n \u003cp\u003e(76.4-78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e80.6\u003c/p\u003e\n \u003cp\u003e(79.9-81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e108.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eBest 4 parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003cp\u003e(0.808-0.817)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e85.2\u003c/p\u003e\n \u003cp\u003e(83.9-86.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003cp\u003e(63.3-65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e76.2\u003c/p\u003e\n \u003cp\u003e(75.5-76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e76.6\u003c/p\u003e\n \u003cp\u003e(74.9-78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e80.5\u003c/p\u003e\n \u003cp\u003e(79.5-81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e104.380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eROMA index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003cp\u003e(0.875-0.891)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e91.1\u003c/p\u003e\n \u003cp\u003e(91.1-91-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003cp\u003e(78.1-78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e85.0\u003c/p\u003e\n \u003cp\u003e(84.7-85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e86.8\u003c/p\u003e\n \u003cp\u003e(86.8-86.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e87.9\u003c/p\u003e\n \u003cp\u003e(87.8-87.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e79.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eBest 4 parameters + CA-125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003cp\u003e(0.904-0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e88.9\u003c/p\u003e\n \u003cp\u003e(88.2-89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e82.0\u003c/p\u003e\n \u003cp\u003e(81.4-82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e86.4\u003c/p\u003e\n \u003cp\u003e(86.1-87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e84.9\u003c/p\u003e\n \u003cp\u003e(84.2-85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e87.6\u003c/p\u003e\n \u003cp\u003e(87.1-88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e79.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eBest 4 parameters + HE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003cp\u003e(0.908-0.913)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e94.4\u003c/p\u003e\n \u003cp\u003e(94.0-94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e77. 3\u003c/p\u003e\n \u003cp\u003e(76.7-77.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e84.4\u003c/p\u003e\n \u003cp\u003e(84.1-84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e91.3\u003c/p\u003e\n \u003cp\u003e(90.8-91.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e89.1\u003c/p\u003e\n \u003cp\u003e(88.9-89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e80.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e11OHT, 11\u0026beta;-hydroxy-testosterone; 11KT, 11-keto-testosterone; AIC, akaike information criteria; AUC, area under the curve; CA-125, cancer antigen 125; NPV, negative predictive value; PPV, positive predictive value; Multivariate model with best 2 parameters=11KT + 11OHA4; model with best 3 parameters=11KT + 11OHA4 + age; model with best 4 parameters=11KT + 11OHA4 + age + 11OHT.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this discovery study, we analyzed preoperative steroid hormone levels in 99 women with adnexal masses to evaluate their potential in distinguishing ovarian cancer from benign conditions. Incorporating 11-oxyandrogens, patient age, and either CA-125 or HE4, we developed two steroid-protein diagnostic models. Both models outperformed CA-125 or HE4 alone (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both), and the ROMA index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for both) in differentiating malignant from non-malignant adnexal masses, highlighting the added diagnostic value of steroid hormone profiling. The superior performance of the steroid-protein models remained robust after excluding borderline ovarian tumors from the control group, and when testing the models on postmenopausal patients only. The enhanced performance of these models likely reflects the contribution of 11-oxyandrogens, which may capture tumor-related steroidogenic activity not detected by conventional markers.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to characterize 11-oxyandrogen levels in women with adnexal masses. It is also the first study to apply machine learning to evaluate circulating steroids alone and in combination with proteins for distinguishing malignant from non-malignant cases. Given that 11-oxyandrogens are altered in hormone-sensitive cancers \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, their integration into diagnostic models represents a novel and biologically relevant improvement.\u003c/p\u003e \u003cp\u003eApart from hormone-sensitive cancers, 11-oxyandrogens have been linked to disorders such as congenital adrenal hyperplasia and PCOS \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Their elevated levels in PCOS suggest a role of adipose tissue in regulation. However, in our study, 11OHT, 11KT, and T were lower in cases than in controls, despite no significant differences in body weight. One possible explanation is intra-tumoral steroid metabolism. We recently showed that HGSOC tumors, particularly the epithelial cell population, express key steroid-metabolizing enzymes \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, potentially accounting for systemic reductions in these steroids. Further research on the steroid conjugation potential of ovarian tumors is needed. If these metabolites are produced locally in ovarian tumors, they could impact key cellular processes, as 11KT and 11-keto-DHT activate the androgen receptor with a potency comparable to testosterone and DHT, respectively \u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Androgen receptor expression has been linked to improved survival and greater platinum sensitivity in HGSOC \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, though mechanistic studies on its signaling in ovarian tumors are lacking.\u003c/p\u003e \u003cp\u003eIn contrast to 11-oxyandrogens, classic androgens have already been investigated as risk factors for ovarian cancer, however data are conflicting. Some studies suggest higher T levels may increase the risk of endometrioid and mucinous ovarian tumors \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, while others found no link \u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Contrariwise, Mendelian randomization studies suggest higher T might lower the risk of ovarian cancer \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, including HGSOC \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Glucocorticoids have also been linked to ovarian cancer progression. Schrepf et al. reported that patients with ovarian cancer exhibited disrupted cortisol rhythms before surgery \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, which may explain the higher cortisol levels we observed in cases compared to controls in our study. Disrupted cortisol rhythms have also been associated with systemic inflammation and poorer survival \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, whereas chemotherapy was shown to normalize these rhythms \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, it is based on a small, single-center cohort. Second, the technical constraints of the LC-MS/MS method prevented quantification of low-concentration steroids like DHT. Third, the cohort includes a small number of patients with early-stage disease. Since early diagnosis significantly improves ovarian cancer survival, developing accurate and sensitive diagnostic tools for early detection remains a major clinical challenge. Fourth, the small number of premenopausal patients limited evaluation of the models in this group. Although 11-oxyandrogens are stable throughout life, one key steroid in our models, 11OHT, differs significantly between pre- and postmenopausal women \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This difference may introduce variability or limit the models' applicability in premenopausal patients, so this should be investigated further. Additionally, we could not assess model performance in patients with low CA-125 levels (\u0026lt;\u0026thinsp;35 kU/L), where CA-125 alone is unreliable. Finally, while cross-validation was performed, external validation in an independent cohort is needed to confirm the models' clinical utility.\u003c/p\u003e \u003cp\u003eNonetheless, our study has key strengths. The steroid-protein models enhance the distinction between malignant and benign adnexal masses, improving sensitivity and specificity. This could improve preoperative risk assessment, potentially leading to more accurate triage and better clinical decision-making. It can also reduce unnecessary surgeries for benign conditions, and ensure timely treatment for ovarian cancer, particularly when conventional biomarkers are inconclusive. By incorporating 11-oxyandrogens, these models provide a more comprehensive biochemical profile beyond CA-125, HE4, or the ROMA index, further strengthening their diagnostic utility. Since mass spectrometry for steroid profiling is already established in clinical practice, these models could be readily implemented after validation. Furthermore, by incorporating additional preoperative clinical data, such as ultrasound features based on IOTA criteria and computed tomography imaging characteristics of adnexal masses, these steroid-protein models could achieve greater accuracy. With validation in a larger cohort, they could become valuable diagnostic tools.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, circulating steroid hormones\u0026mdash;particularly 11-oxyandrogens\u0026mdash;offer a valuable and noninvasive biomarker class for improving the preoperative differentiation of malignant and benign adnexal masses. When combined with CA-125 or HE4, these hormones significantly enhance diagnostic accuracy beyond conventional markers and the ROMA index. The steroid-protein models remain robust across clinical subgroups and provide insights into tumor-specific steroidogenic activity, supporting their potential as a clinically meaningful tool for risk stratification and improved surgical decision-making in women with adnexal masses.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003e11KA4\u0026nbsp;11-keto-androstenedione\u003c/p\u003e\n\u003cp\u003e11KT\u0026nbsp; \u0026nbsp;11-keto-testosterone\u003c/p\u003e\n\u003cp\u003e11OHA4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;11\u0026beta;-hydroxy-androstenedione\u003c/p\u003e\n\u003cp\u003e11OHT\u0026nbsp;11\u0026beta;-hydroxy-testosterone\u003c/p\u003e\n\u003cp\u003e17OHP4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;17\u0026alpha;-hydroxy-progesterone\u003c/p\u003e\n\u003cp\u003eA4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Androstenedione\u003c/p\u003e\n\u003cp\u003eADNEX\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Assessment of different neoplasias in the adnexa\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAIC\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Akaike information criteria\u003c/p\u003e\n\u003cp\u003eANCOVA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Analysis of covariance\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp;\u0026nbsp;Area under the receiver operating curve\u003c/p\u003e\n\u003cp\u003eBOT\u0026nbsp; \u0026nbsp; \u0026nbsp;Borderline Ovarian Tumor\u003c/p\u003e\n\u003cp\u003eCA125\u0026nbsp;Cancer antigen 125\u003c/p\u003e\n\u003cp\u003eDHEA\u0026nbsp;\u0026nbsp;Dehydroepiandrosterone\u003c/p\u003e\n\u003cp\u003eDHT\u0026nbsp; \u0026nbsp;\u0026nbsp;5\u0026alpha;-dihydrotestosterone\u003c/p\u003e\n\u003cp\u003eECLIA\u0026nbsp;Electroluminescent immunoassays\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFIGO\u0026nbsp; \u0026nbsp;International Federation of Gynecologic Oncology\u003c/p\u003e\n\u003cp\u003eHE4\u0026nbsp; \u0026nbsp; \u0026nbsp;Human epididymis protein 4\u003c/p\u003e\n\u003cp\u003eHGSOC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-grade serous ovarian cancer\u003c/p\u003e\n\u003cp\u003eICCR\u0026nbsp; \u0026nbsp;International Collaboration on Cancer Reporting\u003c/p\u003e\n\u003cp\u003eIOTA\u0026nbsp; \u0026nbsp;International Ovarian Tumor Analysis\u003c/p\u003e\n\u003cp\u003eIQR\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Interquartile range\u003c/p\u003e\n\u003cp\u003eLC-MS/MS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Liquid chromatography-tandem mass spectrometry\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLLOQ\u0026nbsp;\u0026nbsp;Lower limit of quantification\u003c/p\u003e\n\u003cp\u003eNPV\u0026nbsp; \u0026nbsp; \u0026nbsp;Negative predictive value\u003c/p\u003e\n\u003cp\u003eOC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ovarian cancer\u003c/p\u003e\n\u003cp\u003eORADS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ovarian-Adnexal Reporting and Data System\u003c/p\u003e\n\u003cp\u003ePCOS\u0026nbsp; \u0026nbsp;Polycystic ovary syndrome\u003c/p\u003e\n\u003cp\u003ePPV\u0026nbsp; \u0026nbsp; \u0026nbsp;Positive predictive value\u003c/p\u003e\n\u003cp\u003eRMI1\u0026nbsp; \u0026nbsp;Risk of Malignancy Index 1\u003c/p\u003e\n\u003cp\u003eROMA\u0026nbsp;Risk of Ovarian Malignancy Algorithm\u003c/p\u003e\n\u003cp\u003eT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Testosterone\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data related to this article can be found in the online version of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Ms. Vesna Sekelj Rangus from the Department of Gynecology and Obstetrics and Ms. Vera Troha Poljančič from the Clinical Institute of Chemistry and Clinical Biochemistry, University Medical Center, Ljubljana for their help in sample collection and processing. We also thank Ms. Vera Troha Poljančič and Prof. Jo\u0026scaron;ko Osredkar from the Clinical Institute of Chemistry and Clinical Biochemistry, University Medical Center, Ljubljana for additional CA-125 and HE4 measurements in a subset of patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarija Gjorgoska: Conceptualization, Data curation; Formal analysis, Investigation; Methodology, Visualization, Writing - original draft, Writing - review \u0026amp; editing. Bo\u0026scaron;tjan Pir\u0026scaron;, Methodology, Writing - review and editing. \u0026Scaron;pela Smrkolj: Writing - review and editing. Tea Lani\u0026scaron;nik Rižner: Conceptualization, Funding acquisition, Supervision, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Slovenian Research Agency, grant number J3-2535 and program funding P3-0449, both to T.L.R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data related to this article can be found in supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Slovenian Ethics Committee ((ID: 0120-487/2020/3) and carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent was obtained from each patient before sample collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eDepartment of Gynecology, University Medical Centre, \u0026Scaron;lajmerjeva 3, 1000 Ljubljana, Slovenia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Department of Gynecology and Obstetrics, Faculty of Medicine, University of Ljubljana, \u0026Scaron;lajmerjeva 3, 1000 Ljubljana, Slovenia\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al. 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Brain Behav Immun Mar. 2013;30(Suppl0):S126\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbi.2012.07.022\u003c/span\u003e\u003cspan address=\"10.1016/j.bbi.2012.07.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNanba AT, Rege J, Ren J, Auchus RJ, Rainey WE, Turcu AF. 11-Oxygenated C19 Steroids Do Not Decline With Age in Women. J Clin Endocrinol Metabolism Jul. 2019;1(7):2615\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2018-02527\u003c/span\u003e\u003cspan address=\"10.1210/jc.2018-02527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ovarian cancer, adnexal masses, steroids, machine learning, diagnostic models","lastPublishedDoi":"10.21203/rs.3.rs-6596566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6596566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOvarian cancer is the deadliest gynecological malignancy, largely due to the advanced stage at diagnosis in most patients. This study investigates whether systemic steroids can serve as biomarkers to distinguish malignant ovarian tumors from non-malignant adnexal masses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective, single-center observational study included 99 women with adnexal masses who underwent surgery between December 2021 and February 2025. Preoperative serum levels of 17 steroid hormones\u0026mdash;including androgens, 11-oxyandrogens, glucocorticoids, and mineralocorticoids\u0026mdash;were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Machine learning was employed to assess the diagnostic potential of these steroids in distinguishing ovarian cancer (n\u0026thinsp;=\u0026thinsp;43) from non-malignant adnexal masses (n\u0026thinsp;=\u0026thinsp;56).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients with ovarian cancer had lower levels of 11β-hydroxy-testosterone (11OHT), 11-keto-testosterone (11KT), and testosterone compared to those with non-malignant adnexal masses. Using stepwise feature selection, we developed two diagnostic models incorporating three 11-oxyandrogens (11KT, 11OHT, and 11β-hydroxy-androstenedione), patient age, and either cancer antigen 125 (CA-125) or human epididymis protein 4 (HE4) for distinguishing malignant from non-malignant adnexal masses. The model including CA-125 achieved AUC of 0.907, 88.9% sensitivity and 82.0% specificity, while the model including HE4 achieved AUC of 0.911, 94.4% sensitivity and 77.3% specificity as evaluated by cross-validation. Both models significantly outperformed CA-125, HE4, and the Risk of Ovarian Malignancy Algorithm (ROMA) index alone.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePatients with ovarian cancer exhibit distinct steroid profiles compared to those with non-malignant adnexal masses. If validated, the models could enhance diagnosis, reducing unnecessary surgeries for benign conditions while ensuring timely treatment for ovarian cancer, particularly when conventional biomarkers are inconclusive.\u003c/p\u003e","manuscriptTitle":"A novel serum-based steroid-protein panels for differentiating ovarian cancer from non-malignant adnexal masses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 11:35:01","doi":"10.21203/rs.3.rs-6596566/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-03T00:05:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T12:30:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T14:08:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185783470931404957427866404111927696673","date":"2025-06-04T16:59:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135948163353465705126512329316698501670","date":"2025-06-03T05:44:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172143832750715087767358649196126402697","date":"2025-05-09T14:54:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T15:13:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-07T10:35:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-06T04:26:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2025-05-05T17:38:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d946649-1094-483d-b092-c91125556ae6","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:10:40+00:00","versionOfRecord":{"articleIdentity":"rs-6596566","link":"https://doi.org/10.1186/s12935-025-04047-8","journal":{"identity":"cancer-cell-international","isVorOnly":false,"title":"Cancer Cell International"},"publishedOn":"2025-11-17 15:57:48","publishedOnDateReadable":"November 17th, 2025"},"versionCreatedAt":"2025-05-14 11:35:01","video":"","vorDoi":"10.1186/s12935-025-04047-8","vorDoiUrl":"https://doi.org/10.1186/s12935-025-04047-8","workflowStages":[]},"version":"v1","identity":"rs-6596566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6596566","identity":"rs-6596566","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}