Clinicopathological Model for Predicting Endometrial Cancer and Atypical Hyperplasia in Women Aged >40 Years: Development and Evaluation in a Single-Institution Retrospective Cohort

Clinicopathological Model for Predicting Endometrial Cancer and Atypical Hyperplasia in Women Aged >40 Years: Development and Evaluation in a Single-Institution Retrospective Cohort | 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 Clinicopathological Model for Predicting Endometrial Cancer and Atypical Hyperplasia in Women Aged >40 Years: Development and Evaluation in a Single-Institution Retrospective Cohort Mengfan Song, Zhen Huang, Zhilin Guo, Yudong Wang, Furei Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7598063/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: Endometrial cancer poses a significant global health burden with rising mortality. Current diagnostics for women ≥40 with abnormal uterine bleeding or imaging abnormalities detect malignancy in <10% of biopsies, subjecting over 90% to unnecessary invasive procedures. Existing prediction models have suboptimal accuracy.To develop and validate a clinically practical nomogram incorporating the novel biomarker cumulative menstrual years, quantifying estrogen exposure, for predicting atypical endometrial hyperplasia or endometrial cancer risk. Methods: This retrospective cohort study included 1,490 women (aged >40 years) who underwent ≥ 2 endometrial biopsies at the International Peace Maternity and Child Health Hospital between 2014- 2023. Univariable and multivariable logistic regression were used to identify potential independent predictors of atypical endometrial hyperplasia or endometrial carcinoma ( AEH/EC ). A nomogram prediction model was developed using significant predictors, with its performance internally validated through AUC analysis (discrimination) and decision curve analysis (clinical utility). Results: Independt Risk factors were postmenopausal bleeding ≥5 years postmenopause (OR=14.55, 95% CI: 7.67–27.04), cumulative menstrual years>40 years (OR=7.28, 95% CI: 2.50–24.01), menstrual irregularity (OR=3.93, 95% CI: 1.74–7.99), abnormal endometrial thickness (OR=2.92, 95% CI: 1.70–5.27), and diabetes mellitus (paradoxical OR=0.40, 95% CI: 0.24–0.66). The nomogram demonstrated robust performance (training AUC=0.82; validation AUC=0.83), excellent calibration (slope=1.000), and clinical utility across thresholds (10–50%). Risk stratification thresholds: low (70 points). Conclusion: This cumulative menstrual years integrated nomogram provides a practical, high-performance tool for dynamic AEH/EC risk stratification using routine parameters, while maintaining high sensitivity, particularly in resource-limited settings. The paradoxical protective association of diabetes (OR=0.40) requires cautious interpretation owing to incomplete BMI adjustment (dichotomized at 23 kg/m² without obesity stratification); prospective validation with granular metabolic profiling is warranted. Endometrial Neoplasms Endometrial Hyperplasia Predictive Value of Tests Perimenopause Menstrual Cycle Nomograms Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Endometrial cancer (EC) is the fourth most prevalent female malignancy globally and the fifth leading cause of cancer-related death in women( 1 – 4 ). Alarmingly, it represents one of the few malignancies with persistently rising mortality over the past four decades( 3 ). Significant risk factors include prolonged estrogen exposure (e.g., early menarche, late menopause), metabolic disorders (obesity, diabetes), and genetic syndromes( 1 , 5 ). Prognosis varies drastically by stage: localized disease (FIGO I–II) has 80–90% 5-year survival, while advanced stages (FIGO III–IV) drop below 20%, with 20–33% of patients presenting at advanced stages( 6 , 7 ). Given this escalating burden and stark prognostic disparity, improving early detection is urgent. Current diagnosis relies on invasive biopsy for women ≥ 40 years with abnormal uterine bleeding (AUB) or imaging abnormalities( 5 , 8 – 10 ). However, 90% of women to unnecessary invasive procedures with significant physical and psychological morbidity( 5 ). Population screening remains unfeasible due to lacking cost-effective tools( 12 ). Despite the availability of various prediction models (e.g., QCancer( 13 )), critical limitations persist: risk assessment tools based on routine clinical indicators exhibit suboptimal discriminatory accuracy (AUC 0.64–0.77)( 14 – 19 ), while advanced models reliant on specialized variables (e.g., molecular markers, MRI)( 20 – 22 )face implementation challenges in primary care settings due to limited availability of these variables, resulting in a lack of high-precision models suitable for basic healthcare facilities; furthermore, existing models, predominantly constructed from cross-sectional studies, employ static risk stratification methods that fail to effectively capture the dynamic progression from endometrial hyperplasia to carcinoma or assess dynamic risk( 15 ); moreover, the applicability of current models across heterogeneous patient populations is limited, particularly by insufficient data from Chinese/Asian populations, compounded by epidemiological variations in endometrial cancer across different ethnic groups, thereby restricting their generalizability within China( 16 , 17 , 23 ). Collectively, this highlights the absence of readily implementable dynamic risk assessment models tailored for Chinese populations in primary care. To bridge this critical gap in early detection and overcome the persistent limitations of current models—suboptimal accuracy with routine variables, impracticality of advanced variables in primary care, static risk assessment, and inadequate generalizability to Chinese populations—we leverage cumulative menstrual years (CMY), a novel, highly predictive biomarker readily available in basic healthcare settings that quantifies cumulative estrogen exposure and exhibits a strong dose-dependent association with AEH/EC risk (e.g., CMY ≥ 40 years confers a 5-fold increase) ( 24 , 25 ). We developed a clinically interpretable dynamic risk stratification nomogram incorporating this fundamental patient metric alongside other primary care-accessible parameters (postmenopausal bleeding, cycle irregularity, endometrial thickness, vascularity). This tool aims to provide a practical solution for Chinese primary care by enabling longitudinal risk profiling, significantly reducing unnecessary biopsies (> 90%) while maintaining high sensitivity (≥ 80%) for detecting AEH/EC, thereby addressing the unmet need for accurate, implementable risk assessment. MATERIALS AND METHODS Study Design and Population We conducted a retrospective cohort study at the International Peace Maternity and Child Health Hospital, selecting patients who undergone endometrial biopsy between January 2014 and December 2023.The cohort comprised 2,568 female patients aged 40–78 years. After applying eligibility criteria, 1,490 participants were included. The flowchart of participants is shown in Fig. 1 . Comprehensive demographic, clinical, and ultrasonographic data were systematically extracted from electronic medical records (EMR-IPMCH v3.2), supplemented by structured telephone interviews when necessary. All participants received transvaginal ultrasonography; diagnostic curettage, hysteroscopy, or surgery was performed based on clinical indications. Eligibility Criteria Participants were included if they: (a) were aged 40 years or older at the time of initial endometrial biopsy; (b) had ≥ 2 histologically confirmed endometrial pathology; (c) maintained a minimum 12-month interval between first and last endometrial pathology; and (d) demonstrated non-AEH/EC (non-atypical endometrial hyperplasia and without EC) on initial pathology. Exclusion criteria comprised: (a) atypical endometrial hyperplasia (AEH) or EC on initial pathology; (b) prior cancer diagnosis; or (c) > 20% missing key variables. Outcome groups were defined by the last pathology: non-AEH/EC or AEH/EC (endometrial carcinoma or AEH). Data Collection Participants were identified from hospital surgical database registries and day-care procedure records, encompassing both inpatient and outpatient cases. Clinical, demographic, and ultrasound data were collected directly from discharge summaries via linkage to the hospital's EMR, with supplementary information acquired through structured telephone interviews when necessary. The collected information included age, occupation, marital status, ethnicity, parity, gravidity, age at first delivery, body mass index (BMI) categorized as underweight and normal (< 23kg/m²), overweight and obesity (≥ 23.0 kg/m²)( 26 ), age at menarche, current use of hormonal medications (estrogen, progesterone, oral contraceptives), medical history (hypertension, diabetes, cancers ), and gynecological history (uterine fibroids, adenomyosis, endometriosis). Surgical approach, duration of follow-up, and postmenopausal status (defined as ≥ 1 year since last menses) were recorded. Age at menopause was categorized as no menopause, < 55 years, or ≥ 55 years( 27 ), ( 28 ) with mode of menopause documented as natural or iatrogenic. Menstrual history included age at menarche (30 to ≤ 40 years; >40 years ), history of abnormal uterine bleeding (present/absent) and postmenopausal bleeding (none; within 5 years of menopause; >5 years after menopause). Endometrial thickness (ET) was classified as "abnormal" if ≥ 13 mm (premenopausal)( 31 ) or ≥ 5 mm (postmenopausal) ( 32 ). Ultrasound and Pathological Evaluation Transvaginal ultrasound was performed using Samsung WS80A or GE Voluson E8/E10 systems (3–12 MHz probes) following International Endometrial Tumor Analysis criteria( 33 ). Two independent sonographers (> 5 years' experience) conducted blinded assessments, with discrepancies resolved by a senior investigator. Pathological diagnoses were rendered by board-certified gynecological pathologists using WHO 2014 classification( 34 ), with the last endometrial pathology defining the final outcome. Ethical Approval This study was reviewed and approved by the Ethics Committee of the International Peace Maternity and Child Health Hospital (Approval No. GKLW-A-2024-107-01). Informed consent was waived for this retrospective analysis, with all patient identifiers removed prior to data processing. Statistical Analysis All analyses were performed using R software (version 4.2.2). Continuous variables are expressed as mean ± standard deviation (SD) for normally distributed data or median with interquartile range (IQR) for non-normally distributed data, while categorical variables are reported as frequencies and percentages. Normality was evaluated using Shapiro-Wilk tests. Group comparisons between non-AEH/EC and AEH/EC cohorts utilized independent samples t-tests for normally distributed continuous variables, Wilcoxon rank-sum tests for non-normally distributed continuous variables, and chi-square or Fisher’s exact tests for categorical variables. Prior to modeling, categorical variables (e.g., occupation, hypertension status) were converted to factors, and continuous variables (e.g., age) to numeric format. Missing data (<20% missingness) were handled via multiple imputation with chained equations (MICE package; m=5 imputations, seed=6666), with pooled estimates derived from the imputed datasets. Univariate screening identified candidate predictors (P<0.05) using chi-square tests for categorical variables and t-tests/Wilcoxon tests for continuous variables. Class imbalance was addressed by synthetic oversampling (ROSE package). A multivariable logistic regression model was constructed through backward elimination of significant univariate predictors. The cohort was stratified into training (70%) and validation (30%) sets using the createDataPartition() function. Model performance was assessed through three primary metrics: discrimination quantified by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals, calibration evaluated via Hosmer-Lemeshow goodness-of-fit tests and calibration plots, and clinical utility measured by decision curve analysis to estimate net benefit across threshold probabilities. RESULTS 1. Cohort Characteristics This retrospective cohort study enrolled 1,490 women treated at the International Peace Maternity and Child Health Hospital (2014-2023), comprising 1,424 non-AEH/EC cases and 66 AEH/EC cases. Statistically significant intergroup differences (P<0.05) were observed in key parameters (Table 1). The AEH/EC group exhibited an older endpoint age (P<0.05) and higher prevalence of postmenopausal status (59.1% vs. 35.6%; P<0.05). Notably, 28.8% of AEH/EC patients experienced postmenopausal bleeding ≥5 years after menopause versus 3.0% in non-AEH/EC controls (P<0.05). Abnormal endometrial thickness was more frequent in the AEH/EC group (74.2% vs. 49.6%; P<0.05), while progestin therapy usage was lower (51.5% vs. 72.8%; P<0.05). Additional differences included higher rates of menstrual irregularity (13.6% vs. 3.9%; P40 years (15.2% vs. 4.8%; P<0.05) in the AEH/EC cohort (Table1）. Table 1. Cohort Characteristics Stratified by Endometrial Pathology level Non-AEH/EC group AEH/EC Group p No. 1424 66 Follow-up period (median [IQR]) 3.00 [ 2.00, 5.00] 4.00 [ 3.00, 7.00] < 0.05 Endpoint age (median [IQR]) 50.00 [ 46.00, 54.00] 55.50 [ 50.00, 65.00] < 0.05 Occupation ( % ) Office worker 964 ( 67.7) 25 ( 37.9) < 0.05 Retired 266 ( 18.7) 28 ( 42.4) Self-employed 85 ( 6.0) 5 ( 7.6) Factory worker 12 ( 0.8) 0 ( 0.0) Technical W Worker 34 ( 2.4) 2 ( 3.0) Farmer 1 ( 0.1) 1 ( 1.5) Other / Unemployed 62 ( 4.4) 5 ( 7.6) Marital( % ) Married 1396 ( 98.0) 65 ( 98.5) 0.63 Single 15 ( 1.1) 0 ( 0.0) Divorced 13 ( 0.9) 1 ( 1.5) Ethnicity( % ) Han Chinese 1415 ( 99.4) 66 ( 100.0) 0.99 Other ethnicity 9 ( 0.6) 0 ( 0.0) Menstrual regularity ( % ) Regular 1369 ( 96.1) 57 ( 86.4) < 0.05 Irregular 55 ( 3.9) 9 ( 13.6) Menopausal status( % ) Premenopausal 917 ( 64.4) 27 ( 40.9) = 12 years 1356 ( 95.2) 62 ( 93.9) 0.85 < 12 years 68 ( 4.8) 4 ( 6.1) Age at menopause ( % ) < 55years 1358 ( 95.4) 56 ( 84.8) = 55years 66 ( 4.6) 10 ( 15.2) Cumulative menstrual years( % ) < 30years 251 ( 17.6) 5 ( 7.6) 40years 69 ( 4.8) 10 ( 15.2) Age at first delivery( % ) Nulliparous 100 ( 7.0) 8 ( 12.1) 0.13 normal 1292 ( 90.7) 55 ( 83.3) Advanced maternal age 32( 2.2) 3 ( 4.5) Gravidity ( % ) Never 69 ( 4.8) 7 ( 10.6) 0.07 Ever 1355 ( 95.2) 59 ( 89.4) Parity( % ) Nulliparity 100 ( 7.0) 8 ( 12.1) 0.18 Multiparity 1324 ( 93.0) 58 ( 87.9) BMI( % ) Underweight&Normal weight 657( 46.1） 21 ( 31.8) 0.031 Overweight &Obesity 767( 53.9） 45( 68.3） Hypertension ( % ) No 1151 ( 80.8) 45 ( 68.2) < 0.05 Yes 273 ( 19.2) 21 ( 31.8) Diabetes Mellitus ( % ) No 1384 ( 97.2) 60 ( 90.9) < 0.05 Yes 40 ( 2.8) 6 ( 9.1) Gynecological history( % ) No 521 ( 36.6) 31 ( 47.0) 0.12 Yes 903 ( 63.4) 35 ( 53.0) Indications for estrogen/progestin therapy Estrogen( % ) No 1267 ( 89.0) 62 ( 93.9) 0.29 Yes 157 ( 11.0) 4 ( 6.1) Progestin ( % ) No 388 ( 27.2) 32 ( 48.5) < 0.05 Yes 1036 ( 72.8) 34 ( 51.5) Oral contraceptives ( % ) No 1272 ( 89.3) 63 ( 95.5) 0.17 Yes 152 ( 10.7) 3 ( 4.5) Abnormal uterine bleeding ( % ) No 1093 ( 76.8) 50 ( 75.8) 0.97 Yes 331 ( 23.2) 16 ( 24.2) Postmenopausal uterine bleeding Onset Interval( % ) No 1284 ( 90.2) 39 ( 59.1) < 0.05 =5years 43 ( 3.0) 19 ( 28.8) Ultrasound imaging Endometrial thickness ( % ) Normal 717 ( 50.4) 17 ( 25.8) < 0.05 Abnormal 707 ( 49.6) 49 ( 74.2) Endometrial mass/lesion( % ) No 659 ( 46.3) 29 ( 43.9) 0.81 Yes 765 ( 53.7) 37 ( 56.1) Endometrial and mass blood flow ( % ) No 929 ( 65.2) 37 ( 56.1) 0.16 Yes 495 ( 34.8) 29 ( 43.9) Endometrial echo ( % ) Regular and homogeneous endometrial echo 398 ( 27.9) 14 ( 21.2) 0.29 Irregular and heterogeneous endometrial echo 1026 ( 72.1) 52 ( 78.8) Pathological Sampling Methods ( % ) Hysteroscopic curettage 1337 ( 93.9) 54 ( 83.1) < 0.05 Fractional curettage 87 ( 6.1) 11 ( 16.9) Non-normally distributed continuous variables are described using the median(IQR). Values are presented as number (%). Continuous variables use the Wilcoxon rank-sum test, and categorical variables use the Chi-square test. Statistical significance was set at p<0.05. Gynecological history: Uterine fibroids, Adenomyosis, Endometriosis Postmenopausal uterine bleeding Onset Interval: Interval between menopause onset and lastest postmenopausal uterine bleeding Other ethnicity: Korean Chinese, Hui Chinese, Manchu Chinese, Mongol Chinese Additionally, the median follow-up was 3.0 years (IQR 2.0–5.0), with 92% of participants completing ≥2 biopsies during this period. The AEH/EC group had a higher proportion of retirement as occupation (42.4% vs. 18.7%; P<0.05), increased menopause at ≥55 years (15.2% vs. 4.6%; P<0.05), reduced menstruation duration <30 years (7.6% vs. 17.6%; P<0.05), greater overweight/obesity prevalence (68.3% vs. 53.9%; P=0.031), higher hypertension (31.8% vs. 19.2%; P<0.05) and diabetes mellitus (9.1% vs. 2.8%; P<0.05), and more frequent use of fractional curettage for pathological sampling (16.9% vs. 6.1%; P<0.05). No significant differences were observed in marital status, ethnicity, age at menarche, gynecological history (uterine fibroids/adenomyosis/endometriosis), or most ultrasound parameters (endometrial mass/lesion, blood flow, echo pattern). 2. Variable Selection Results Multivariable logistic regression identified five independent predictors of AEH/EC (Table 2). Postmenopausal uterine bleeding occurring ≥ 5 years after menopause demonstrated the strongest association (OR=14.55, 95% CI: 7.67–27.04; P40 years (OR=7.28, 95% CI: 2.50–24.01; P<0.05). Menstrual irregularity significantly increased risk (OR=3.93, 95% CI: 1.74–7.99; P<0.05), as did abnormal endometrial thickness (OR=2.92, 95% CI: 1.70–5.27; P<0.05). Diabetes mellitus was associated with reduced risk (OR=0.40, 95% CI: 0.24–0.66; P<0.05). Postmenopausal bleeding within 5 years of menopause also conferred elevated risk (OR=2.72, 95% CI: 1.15–5.68; P<0.05), while menstruation duration of 30–40 years showed non-significant association (OR=2.32, 95% CI: 1.01–6.72; P=0.076) compared to <30 years. Table 2. Multivariable Predictors of AEH/EC Variable Level OR 95%CI p-value Postmenopausal uterine bleeding Onset Interval No 1 Ref < 5years 2.72 1.15-5.68 = 5years 14.55 7.67-27.04 < 0.05 Endometrial thickness Normal 1 Ref Abnormal 2.92 1.7-5.27 < 0.05 Cumulative menstrual years 40years 7.28 2.5-24.01 < 0.05 Diabetes Mellitus No 1 Ref Yes 0.4 0.24-0.66 < 0.05 Menstrual regularity Regular 1 Ref Irregular 3.93 1.74-7.99 < 0.05 Statistical significance was set at p<0.05. 3. Nomogram Development A clinical prediction nomogram integrating these nine predictors was developed (Figure 2). The tool quantifies risk through a point system (0–350 points), with total scores converting to AEH/EC probabilities (0.1–0.99). Key predictors contributing maximal points included menstrual irregularity, prolonged menstruation (> 40 years), and late postmenopausal bleeding. Risk stratification was defined as: low-risk (70 points). For example, a postmenopausal patient with menstrual irregularity (100 points), >40 years menstruation (67.5 points), and abnormal endometrial thickness (27.5 points) accumulates 195 points, corresponding to 90% AEH/EC risk. 4. Model Validation The nomogram demonstrated robust discrimination and calibration performance during validation. ROC analysis yielded AUCs of 0.82 (95% CI: 0.80–0.85) in the training cohort and 0.83 (95% CI: 0.73–0.93) in the validation cohort (both P<0.05; Figure 3a). Calibration plots showed excellent agreement between predicted and observed outcomes, with a calibration slope of 1.000 and intercept of −0.177 (Figure 3b). The model achieved a Brier score of 0.172, indicating low overall prediction error. Decision curve analysis further confirmed the model's clinical utility across threshold probabilities of 10% to 50%, particularly showing significantly greater standardized net benefit compared to the "intervene-all" or "intervene-none" strategies within the critical 20%-40% high-risk threshold range(Figure 3c). DISCUSSION This study developed a nomogram demonstrating significant advantages in predicting AEH/EC risk among women who are over 40 years old and undergoing endometrial biopsy. Key findings identified Cumulative Menstrual Years (CMY >40 years) as a strong independent risk factor (increasing risk by 7.28-fold) and confirmed the critical predictive value of Postmenopausal Bleeding (PMB) timing, particularly PMB occurring ≥5 years postmenopause (OR=14.55). By menstrual irregularity, integrating CMY, BMI status, parity, diabetes status, AUB, stratified PMB, endometrial thickness, and duration of progesterone administration, the model achieved exceptional predictive performance (AUC 0.82–0.83), significantly outperforming existing models based on conventional indicators. Regarding risk factors, the key findings of this study show both consensus and differences with previous research. Points of consensus include: CMY>40 years, as a quantifiable indicator of cumulative estrogen exposure, significantly increasing AEH/EC risk (OR=7.28) aligns with numerous epidemiological studies (24, 25) ; the strongest risk associated with late PMB (≥5 years) confirmed by stratified analysis (OR=14.55) is consistent with clinical guidelines and the majority of academic viewpoints(35); significantly increased risk associated with thickened endometrium (≥13mm premenopause, ≥5mm postmenopause; OR=2.92) also matches classical research and guideline recommendations(31, 32); and menstrual irregularity (OR=3.93) as an independent risk factor likely reflects anovulation or endocrine dysfunction(36). The primary difference lies in this study’s observed inverse association between diabetes and endometrial cancer risk with an odds ratio of 0.40, contrasting with most studies reporting positive associations. (37, 38) This paradox may stem from insufficient control of BMI confounding, as adjustment occurred only via dichotomization at the Asian standard of 23 kg/m², lacking fine stratification or continuous variable analysis; whereas literature identifies BMI as a key shared risk factor and potent confounder—evidenced by Luo et al(39).and Lucenteforte et al(40). showing attenuated associations after BMI adjustment. Other factors include surveillance bias, where increased gynecological examinations in diabetics may enable earlier precancerous intervention; metformin’s potential influence despite the WHI finding no protective effect with a hazard ratio of 1.00, while lab studies suggest antitumor mechanisms; and population heterogeneity, where differing Asian BMI thresholds of 23 kg/m² versus Western standards of ≥25 kg/m² may introduce bias. Thus, the true diabetes-endometrial cancer relationship requires validation through larger prospective studies such as WHI-style extensions; rigorous confounder control including BMI modeled as a continuous variable or finely stratified into six tiers, with waist-hip ratio inclusion; obesity-diabetes interaction assessment; and stratification by diabetes duration and treatment per the WHI’s time-dependent analysis showing residual risk in new-onset diabetes. Compared to models reported in previous literature, the nomogram constructed in this study possesses significant advantages and unique value. The primary advantage is a markedly improved prediction accuracy (AUC 0.82-0.83), significantly better than prior models relying on conventional indicators (age, BMI, bleeding history; AUC 0.64-0.77). This is mainly attributed to the innovative integration of CMY as a quantifiable indicator of estrogen exposure and the critical stratification of PMB timing (<5 years vs ≥5 years). Secondly, this model addresses the limitation of "dynamic risk assessment" in existing models. Existing models often use static stratification based on cross-sectional data, whereas this model, based on a retrospective cohort (≥2 biopsies, median follow-up 3 years), inherently allows its predictors (such as continuously accumulating CMY or newly occurring late PMB) to be suitable for dynamically monitoring changes in individual risk over time (e.g., CMY extension, new onset of late PMB). This enables assessment of risk evolution from benign/hyperplastic conditions to AEH/EC, thereby achieving more precise timing for follow-up and intervention. Thirdly, the model boasts high clinical practicality and applicability in primary care settings. All included variables (CMY, PMB timing, ET, menstrual regularity, diabetes status) can be easily obtained at the primary care level through history taking, basic physical examination, and transvaginal ultrasound. This overcomes the major obstacle faced by models relying on advanced imaging (e.g., MRI texture analysis) or expensive molecular markers, which are difficult to implement in resource-limited areas. Its intuitive risk score and stratification (low/medium/high risk) facilitate rapid clinical decision-making. The limitations of this study include its single-center retrospective design with inherent selection bias from including only a biopsy cohort; a limited number of AEH/EC cases (n = 66), affecting precision despite synthetic oversampling; a highly homogeneous cohort (99.4% Han Chinese), reducing generalizability; incomplete adjustment for BMI (a key confounder in the diabetes-EC association); and lack of data on genetic syndromes, molecular markers, and diabetes-specific variables (such as type, duration, and treatment). Future directions include: external validation in multi-ethnic, geographically diverse cohorts; prospective evaluation of biopsy reduction rates and impact on early detection; model refinement through incorporating BMI as a continuous variable or finer strata (e.g., WHO Asian BMI categories); development of a digital calculator for point-of-care use; and larger studies to clarify diabetes’ independent role through rigorous confounder adjustment. The model is developed based on data from a Chinese population (99.4% Han Chinese cohort) and employs BMI cutoffs suitable for Asian populations. This partially addresses the issue of insufficient generalizability of existing mainstream models (mostly built on European and American population data) to the Chinese population, providing a practical tool for primary healthcare in China that is more aligned with local epidemiological characteristics. Its clear potential for clinical benefit lies in effectively identifying low-risk individuals (score 90%) unnecessary invasive endometrial biopsies and their associated physical and psychological burdens and economic costs, while maintaining a high detection rate (sensitivity ≥80%) for AEH/EC. This ability to reduce unnecessary biopsies and optimize resource utilization in primary care and resource-limited settings lacking advanced diagnostic equipment is one of its most significant value propositions. CONCLUSION This study developed and validated a practical nomogram that significantly improves prediction of AEH/EC risk in women undergoing endometrial biopsy. Its superior performance (AUC 0.82-0.83) stems from integrating two key innovations: cumulative menstrual years (CMY >40 years; OR=7.28) as a novel measure of estrogen exposure, and stratified PMB timing (≥5 years postmenopause; OR=14.55) as a critical high-risk indicator. Relying solely on accessible clinical/ultrasound parameters, the model enables dynamic risk monitoring and effectively identifies low-risk women (score <40 points), while maintaining high sensitivity. Optimized for the Han Chinese population, this tool offers substantial clinical utility for resource-limited settings.Future work must: (1) validate the model in multi-ethnic cohorts, (2) refine BMI adjustment using WHO Asian classifications, and (3) prospectively quantify biopsy reduction rates and cost savings. Development of an open-access digital calculator is prioritized for point-of-care use. Abbreviations AEH/EC, Atypical endometrial hyperplasia or Endometrial carcinoma AEH, Atypical endometrial hyperplasia AUB, abnormal uterine bleeding AUC, area under the ROC curve BMI, body mass index CI, Confidence Interval CMY, cumulative menstrual years EC, Endometrial carcinoma EMR-IPMCH, Electronic Medical Record-International Peace Maternity and Child Health Hospital ET, Endometrial thickness IQR, interquartile range OR, Odds Ratio PMB, postmenopausal bleeding (defined as vaginal bleeding ≥12 months after cessation of menses) PMB Onset Interval, Age at PMB diagnosis - Age at menopause ROC, receiver operating characteristic SD, standard deviation WHO, World Health Organization Declarations Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of the International Peace Maternity and Child Health Hospital (Approval No. GKLW-A-2024-107-01). The ethics committee of the International Peace Maternity and Child Health Hospital waived the requirement for informed consent due to the retrospective nature of the study and all data being anonymized. All methods were carried out in accordance with relevant guidelines and regulations (e.g., Helsinki Declaration). Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests No potential conflict of interest relevant to this article was reported. Funding This work was supported by Decision Consulting Project of 2025 China Hospital Development Institute, Shanghai Jiao Tong University (CHDI-2025-Z-39), the Collaborative Guidance Project of Traditional Chinese and Western Medicine in General Hospitals of Shanghai Municipal Health Commission (ZXXT-202315), the major difficult diseases of Chinese and Western clinical cooperation construction project of the National Health Commission of the People's Republic of China (ZXXTQJ-2024), the Fundamental Research Funds for the Central Universities (YG2025QNA31)and Three-Year Action Plan for Improving Clinical Research Capability of International Peace Maternity and Child Health Hospital (IPMCH2024CR02). Authors' contributions S.M. and H.Z. contributed equally to this work as co-first authors, with S.M. listed first. J.F. and W.Y. are co-corresponding authors, with J.F. as the lead. S.M., H.Z. and J.F. conceived and designed the study. S.M., G.Z., W.Y. and J.F. acquired funding. S.M., H.Z. and W.Y. developed the methodology. S.M., H.Z. and G.Z. curated the data. S.M. and H.Z. wrote the original draft. All authors reviewed, edited, and approved the final manuscript. Acknowledgements We extend our gratitude to the Gynecology Department for their meticulous documentation of patients' daily data, which was fundamental to ensuring the completeness of the data collection. We would also like to thank the Information Department for their invaluable support in the data acquisition process. References Gu B, Shang X, Yan M, Li X, Wang W, Wang Q, Zhang C. Variations in incidence and mortality rates of endometrial cancer at the global, regional, and national levels, 1990–2019. Gynecol Oncol. 2021;161(2):573–80. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. 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ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer. International Journal of Gynecological Cancer. 2016;26(1):2–30. Lin Q, Zhang J, Liu X, Zheng Q, Lin D, Pan M. Association between Healthy Eating Index-2015 total and component food scores with reproductive lifespan among postmenopausal women: a population-based study from NHANES 2005–2016. BMC Public Health. 2024;24(1):2631. Ou R, Wei Q, Hou Y, Zhang L, Liu K, Lin J et al. Reproductive Lifespan and Motor Progression of Parkinson’s Disease. J Clin Med. 2022;11(20). Aytekin O, Karagöz Ç, Göktaş E, Tokalıoğlu AA, Tiryaki Güner G, Özkaya Uçar Y, et al. Preoperative predictors of concurrent endometrial carcinoma in patients with endometrial intraepithelial neoplasia: the role of HALP score and other inflammatory markers. J Turkish-German Gynecol Association. 2025;26(1):34–40. Rebecca Smith-Bindman MKK, Vickie MD, Feldstein A, Leslee Subak MD, Juergen Scheidler MD. PhD; Richard Brand, PhD; Deborah Grady, MD. Review Endovaginal Ultrasound to Exclude Endometrial Cancer and Other Endometrial Abnormalities. JAMA. 1998;280:17. Leone FP, Timmerman D, Bourne T, Valentin L, Epstein E, Goldstein SR, et al. Terms, definitions and measurements to describe the sonographic features of the endometrium and intrauterine lesions: a consensus opinion from the International Endometrial Tumor Analysis (IETA) group. Ultrasound Obstet Gynecol. 2010;35(1):103–12. Emons G, Beckmann M, Schmidt D, Mallmann P. New WHO Classification of Endometrial Hyperplasias. Geburtshilfe Frauenheilkd. 2015;75(02):135–6. Bengtsen MB, Veres K, Nørgaard M. First-time postmenopausal bleeding as a clinical marker of long-term cancer risk: A Danish Nationwide Cohort Study. Br J Cancer. 2019;122(3):445–51. Yanli Y, Mei WT, Cong L, Schatten H. Analysis of Characteristics of Endometrial Carcinoma in Peri- and Postmenopausal Women with Abnormal Uterine Bleeding. Biomed Res Int. 2024;2024:1–7. Saed L, Varse F, Baradaran HR, Moradi Y, Khateri S, Friberg E et al. The effect of diabetes on the risk of endometrial Cancer: an updated a systematic review and meta-analysis. BMC Cancer. 2019;19(1). Wang M, Yang Y, Liao Z. Diabetes and cancer: Epidemiological and biological links. World J Diabetes. 2020;11(6):227–38. Luo J, Beresford S, Chen C, Chlebowski R, Garcia L, Kuller L, et al. Association between diabetes, diabetes treatment and risk of developing endometrial cancer. Br J Cancer. 2014;111(7):1432–9. Lucenteforte E, Bosetti C, Talamini R, Montella M, Zucchetto A, Pelucchi C, et al. Diabetes and endometrial cancer: effect modification by body weight, physical activity and hypertension. Br J Cancer. 2007;97(7):995–8. Additional Declarations No competing interests reported. Supplementary Files Abstract.png Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 10 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Editor invited by journal 19 Sep, 2025 Submission checks completed at journal 19 Sep, 2025 First submitted to journal 19 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7598063","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533670757,"identity":"f4b82e01-9b0e-46a1-8e5a-580ecb6b63b5","order_by":0,"name":"Mengfan Song","email":"","orcid":"","institution":"International Peace Maternity \u0026 Child Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mengfan","middleName":"","lastName":"Song","suffix":""},{"id":533670758,"identity":"7aef3919-94be-4e66-aa91-bfdac594a774","order_by":1,"name":"Zhen Huang","email":"","orcid":"","institution":"Shanghai Key Laboratory of Embryo 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1","display":"","copyAsset":false,"role":"figure","size":120745,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant selection flowchart illustrating inclusion/exclusion criteria.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7598063/v1/be86584dfd9e6fbdd82914de.png"},{"id":94225543,"identity":"823c2b7c-d15e-4ec0-b206-767ab8e08d55","added_by":"auto","created_at":"2025-10-23 19:30:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204164,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for endometrial cancer/atypical hyperplasia risk prediction.\u003c/p\u003e\n\u003cp\u003ePoints assigned per predictor (top) yield total points (middle) corresponding to risk probability (bottom). Risk stratification: low (\u0026lt;40), medium (40-70), high (\u0026gt;70).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7598063/v1/952d5d64253b4edb0d7eb382.png"},{"id":94225546,"identity":"8fe9f350-51ed-4815-ae18-dabd2431a24f","added_by":"auto","created_at":"2025-10-23 19:30:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":176863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e ROC curves: training (navy; AUC=0.82), validation (red; AUC=0.83);\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb.\u003c/strong\u003e Calibration: logistic fit (solid), nonparametric (dashed) vs. ideal (grey); Brier=0.172. (c) Decision curve: model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec.\u003c/strong\u003eDecision curve: model (thick solid) vs. 'All' (dashed) and 'None' (thin solid) strategies; net benefit superior at 10-50% thresholds (optimal 20-40%).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7598063/v1/808ab28e5c5c69890aceb42a.png"},{"id":94227091,"identity":"c06d58f0-93d2-426b-a6cd-36b5619c8581","added_by":"auto","created_at":"2025-10-23 20:02:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1308776,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7598063/v1/e342248e-2b32-48cc-87db-46aae9bb75a1.pdf"},{"id":94225545,"identity":"476c41f1-c270-43a6-9340-bca05f708f05","added_by":"auto","created_at":"2025-10-23 19:30:50","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":233676,"visible":true,"origin":"","legend":"","description":"","filename":"Abstract.png","url":"https://assets-eu.researchsquare.com/files/rs-7598063/v1/0bcddd12a85ff9a58fc8d685.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinicopathological Model for Predicting Endometrial Cancer and Atypical Hyperplasia in Women Aged \u003e40 Years: Development and Evaluation in a Single-Institution Retrospective Cohort","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEndometrial cancer (EC) is the fourth most prevalent female malignancy globally and the fifth leading cause of cancer-related death in women(\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Alarmingly, it represents one of the few malignancies with persistently rising mortality over the past four decades(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Significant risk factors include prolonged estrogen exposure (e.g., early menarche, late menopause), metabolic disorders (obesity, diabetes), and genetic syndromes(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Prognosis varies drastically by stage: localized disease (FIGO I\u0026ndash;II) has 80\u0026ndash;90% 5-year survival, while advanced stages (FIGO III\u0026ndash;IV) drop below 20%, with 20\u0026ndash;33% of patients presenting at advanced stages(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Given this escalating burden and stark prognostic disparity, improving early detection is urgent.\u003c/p\u003e\u003cp\u003eCurrent diagnosis relies on invasive biopsy for women\u0026thinsp;\u0026ge;\u0026thinsp;40 years with abnormal uterine bleeding (AUB) or imaging abnormalities(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, \u0026lt;\u0026thinsp;10% of these biopsies in symptomatic women confirm malignancy(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), subjecting\u0026thinsp;\u0026gt;\u0026thinsp;90% of women to unnecessary invasive procedures with significant physical and psychological morbidity(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Population screening remains unfeasible due to lacking cost-effective tools(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the availability of various prediction models (e.g., QCancer(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)), critical limitations persist: risk assessment tools based on routine clinical indicators exhibit suboptimal discriminatory accuracy (AUC 0.64\u0026ndash;0.77)(\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), while advanced models reliant on specialized variables (e.g., molecular markers, MRI)(\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)face implementation challenges in primary care settings due to limited availability of these variables, resulting in a lack of high-precision models suitable for basic healthcare facilities; furthermore, existing models, predominantly constructed from cross-sectional studies, employ static risk stratification methods that fail to effectively capture the dynamic progression from endometrial hyperplasia to carcinoma or assess dynamic risk(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e); moreover, the applicability of current models across heterogeneous patient populations is limited, particularly by insufficient data from Chinese/Asian populations, compounded by epidemiological variations in endometrial cancer across different ethnic groups, thereby restricting their generalizability within China(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Collectively, this highlights the absence of readily implementable dynamic risk assessment models tailored for Chinese populations in primary care.\u003c/p\u003e\u003cp\u003eTo bridge this critical gap in early detection and overcome the persistent limitations of current models\u0026mdash;suboptimal accuracy with routine variables, impracticality of advanced variables in primary care, static risk assessment, and inadequate generalizability to Chinese populations\u0026mdash;we leverage cumulative menstrual years (CMY), a novel, highly predictive biomarker readily available in basic healthcare settings that quantifies cumulative estrogen exposure and exhibits a strong dose-dependent association with AEH/EC risk (e.g., CMY\u0026thinsp;\u0026ge;\u0026thinsp;40 years confers a 5-fold increase) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We developed a clinically interpretable dynamic risk stratification nomogram incorporating this fundamental patient metric alongside other primary care-accessible parameters (postmenopausal bleeding, cycle irregularity, endometrial thickness, vascularity). This tool aims to provide a practical solution for Chinese primary care by enabling longitudinal risk profiling, significantly reducing unnecessary biopsies (\u0026gt;\u0026thinsp;90%) while maintaining high sensitivity (\u0026ge;\u0026thinsp;80%) for detecting AEH/EC, thereby addressing the unmet need for accurate, implementable risk assessment.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Population\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study at the International Peace Maternity and Child Health Hospital, selecting patients who undergone endometrial biopsy between January 2014 and December 2023.The cohort comprised 2,568 female patients aged 40\u0026ndash;78 years. After applying eligibility criteria, 1,490 participants were included. The flowchart of participants is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Comprehensive demographic, clinical, and ultrasonographic data were systematically extracted from electronic medical records (EMR-IPMCH v3.2), supplemented by structured telephone interviews when necessary. All participants received transvaginal ultrasonography; diagnostic curettage, hysteroscopy, or surgery was performed based on clinical indications.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEligibility Criteria\u003c/h3\u003e\n\u003cp\u003eParticipants were included if they: (a) were aged 40 years or older at the time of initial endometrial biopsy; (b) had\u0026thinsp;\u0026ge;\u0026thinsp;2 histologically confirmed endometrial pathology; (c) maintained a minimum 12-month interval between first and last endometrial pathology; and (d) demonstrated non-AEH/EC (non-atypical endometrial hyperplasia and without EC) on initial pathology. Exclusion criteria comprised: (a) atypical endometrial hyperplasia (AEH) or EC on initial pathology; (b) prior cancer diagnosis; or (c)\u0026thinsp;\u0026gt;\u0026thinsp;20% missing key variables. Outcome groups were defined by the last pathology: non-AEH/EC or AEH/EC (endometrial carcinoma or AEH).\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eParticipants were identified from hospital surgical database registries and day-care procedure records, encompassing both inpatient and outpatient cases. Clinical, demographic, and ultrasound data were collected directly from discharge summaries via linkage to the hospital's EMR, with supplementary information acquired through structured telephone interviews when necessary. The collected information included age, occupation, marital status, ethnicity, parity, gravidity, age at first delivery, body mass index (BMI) categorized as underweight and normal (\u0026lt;\u0026thinsp;23kg/m\u0026sup2;), overweight and obesity (\u0026ge;\u0026thinsp;23.0 kg/m\u0026sup2;)(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), age at menarche, current use of hormonal medications (estrogen, progesterone, oral contraceptives), medical history (hypertension, diabetes, cancers ), and gynecological history (uterine fibroids, adenomyosis, endometriosis). Surgical approach, duration of follow-up, and postmenopausal status (defined as \u0026ge;\u0026thinsp;1 year since last menses) were recorded. Age at menopause was categorized as no menopause, \u0026lt;\u0026thinsp;55 years, or \u0026ge;\u0026thinsp;55 years(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) with mode of menopause documented as natural or iatrogenic. Menstrual history included age at menarche (\u0026lt;\u0026thinsp;12 years / \u0026ge;12 years (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) ), menstrual regularity (regular/irregular), duration of menstrual life(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), (\u0026le;\u0026thinsp;30 years; \u0026gt;30 to \u0026le;\u0026thinsp;40 years; \u0026gt;40 years ), history of abnormal uterine bleeding (present/absent) and postmenopausal bleeding (none; within 5 years of menopause; \u0026gt;5 years after menopause). Endometrial thickness (ET) was classified as \"abnormal\" if\u0026thinsp;\u0026ge;\u0026thinsp;13 mm (premenopausal)(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) or \u0026ge;\u0026thinsp;5 mm (postmenopausal) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eUltrasound and Pathological Evaluation\u003c/h3\u003e\n\u003cp\u003eTransvaginal ultrasound was performed using Samsung WS80A or GE Voluson E8/E10 systems (3\u0026ndash;12 MHz probes) following International Endometrial Tumor Analysis criteria(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Two independent sonographers (\u0026gt;\u0026thinsp;5 years' experience) conducted blinded assessments, with discrepancies resolved by a senior investigator. Pathological diagnoses were rendered by board-certified gynecological pathologists using WHO 2014 classification(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), with the last endometrial pathology defining the final outcome.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethics Committee of the International Peace Maternity and Child Health Hospital (Approval No. GKLW-A-2024-107-01). Informed consent was waived for this retrospective analysis, with all patient identifiers removed prior to data processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R software (version 4.2.2). Continuous variables are expressed as mean ± standard deviation (SD) for normally distributed data or median with interquartile range (IQR) for non-normally distributed data, while categorical variables are reported as frequencies and percentages. Normality was evaluated using Shapiro-Wilk tests. Group comparisons between non-AEH/EC and AEH/EC cohorts utilized independent samples t-tests for normally distributed continuous variables, Wilcoxon rank-sum tests for non-normally distributed continuous variables, and chi-square or Fisher’s exact tests for categorical variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior to modeling, categorical variables (e.g., occupation, hypertension status) were converted to factors, and continuous variables (e.g., age) to numeric format. Missing data (\u0026lt;20% missingness) were handled via multiple imputation with chained equations (MICE package; m=5 imputations, seed=6666), with pooled estimates derived from the imputed datasets. Univariate screening identified candidate predictors (P\u0026lt;0.05) using chi-square tests for categorical variables and t-tests/Wilcoxon tests for continuous variables. Class imbalance was addressed by synthetic oversampling (ROSE package). A multivariable logistic regression model was constructed through backward elimination of significant univariate predictors. The cohort was stratified into training (70%) and validation (30%) sets using the createDataPartition() function. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel performance was assessed through three primary metrics: discrimination quantified by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals, calibration evaluated via Hosmer-Lemeshow goodness-of-fit tests and calibration plots, and clinical utility measured by decision curve analysis to estimate net benefit across threshold probabilities.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e1. Cohort Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study enrolled 1,490 women treated at the International Peace Maternity and Child Health Hospital (2014-2023), comprising 1,424 non-AEH/EC cases and 66 AEH/EC cases. Statistically significant intergroup differences (P\u0026lt;0.05) were observed in key parameters (Table 1). The AEH/EC group exhibited an older endpoint age (P\u0026lt;0.05) and higher prevalence of postmenopausal status (59.1% vs. 35.6%; P\u0026lt;0.05). Notably, 28.8% of AEH/EC patients experienced postmenopausal bleeding \u0026ge;5 years after menopause versus 3.0% in non-AEH/EC controls (P\u0026lt;0.05). Abnormal endometrial thickness was more frequent in the AEH/EC group (74.2% vs. 49.6%; P\u0026lt;0.05), while progestin therapy usage was lower (51.5% vs. 72.8%; P\u0026lt;0.05). Additional differences included higher rates of menstrual irregularity (13.6% vs. 3.9%; P\u0026lt;0.05) and prolonged menstruation duration \u0026gt;40 years (15.2% vs. 4.8%; P\u0026lt;0.05) in the AEH/EC cohort (Table1）.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCohort Characteristics Stratified by Endometrial Pathology\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003elevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eNon-AEH/EC group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eAEH/EC Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eFollow-up period (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e3.00 [ 2.00, 5.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e4.00 [ 3.00, 7.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eEndpoint age (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e50.00 [ 46.00, 54.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e55.50 [ 50.00, 65.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eOccupation ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eOffice worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e964 ( 67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e25 ( 37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e266 ( 18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e28 ( 42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e85 ( 6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e5 (\u0026nbsp;7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eFactory worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e12 ( 0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0 (\u0026nbsp;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eTechnical W\u003c/p\u003e\n \u003cp\u003eWorker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e34 ( 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e2 (\u0026nbsp;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1 ( 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1 (\u0026nbsp;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eOther / Unemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e62 ( 4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e5 (\u0026nbsp;7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eMarital( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1396 ( 98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e65 ( 98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e15 ( 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0 (\u0026nbsp;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e13 ( 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e1 (\u0026nbsp;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eEthnicity( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eHan Chinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1415 ( 99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e66 ( 100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eOther ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e9 ( 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e0 (\u0026nbsp;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eMenstrual regularity\u0026nbsp;( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1369 ( 96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e57 ( 86.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e55 ( 3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e9 ( 13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eMenopausal status( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003ePremenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e917 ( 64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e27 ( 40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMenopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e507 ( 35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e39 ( 59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eAge at menarche\u0026nbsp;( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026gt;= 12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1356 ( 95.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e62 ( 93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt; 12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e68 ( 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e4 (\u0026nbsp;6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eAge at menopause\u0026nbsp; ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt; 55years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1358 ( 95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e56 ( 84.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026gt;= 55years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e66 ( 4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e10 ( 15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eCumulative menstrual years( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt; 30years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e251 ( 17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e5 (\u0026nbsp;7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e30-40years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1104 ( 77.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e51 ( 77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026gt;40years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e69 ( 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e10 ( 15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eAge at first delivery( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNulliparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e100 ( 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e8 ( 12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1292 ( 90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e55 ( 83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eAdvanced maternal age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e32( 2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e3\u0026nbsp;( 4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eGravidity\u0026nbsp;( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e69 ( 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e7 ( 10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eEver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1355 ( 95.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e59 ( 89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eParity( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNulliparity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e100 ( 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e8 ( 12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMultiparity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1324 ( 93.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e58 ( 87.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eBMI( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eUnderweight\u0026amp;Normal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e657( 46.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e21 ( 31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eOverweight \u0026amp;Obesity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e767( 53.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e45( 68.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eHypertension ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1151 ( 80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e45 ( 68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e273 ( 19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e21 ( 31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eDiabetes Mellitus ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1384 ( 97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e60 ( 90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e40 ( 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e6 (\u0026nbsp;9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eGynecological history( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e521 ( 36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e31 ( 47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e903 ( 63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e35 ( 53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eIndications for estrogen/progestin therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eEstrogen( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1267 ( 89.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e62 ( 93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e157 ( 11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e4\u0026nbsp;( 6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eProgestin ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e388 ( 27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e32 ( 48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1036 ( 72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e34 ( 51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eOral contraceptives ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1272 ( 89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e63 ( 95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e152 ( 10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e3\u0026nbsp;( 4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eAbnormal uterine bleeding ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1093 ( 76.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e50 ( 75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e331 ( 23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e16 ( 24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003ePostmenopausal uterine bleeding Onset Interval( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1284 ( 90.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e39 ( 59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt; 5years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e97 ( 6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e8 ( 12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026gt;=5years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e43 ( 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e19 ( 28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eUltrasound imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eEndometrial thickness ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e717 ( 50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e17 ( 25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e707 ( 49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e49 ( 74.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eEndometrial mass/lesion( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e659 ( 46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e29 ( 43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e765 ( 53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e37 ( 56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eEndometrial and mass blood flow ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e929 ( 65.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e37 ( 56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e495 ( 34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e29 ( 43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eEndometrial echo ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eRegular and homogeneous endometrial echo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e398 ( 27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e14 ( 21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eIrregular and heterogeneous endometrial echo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1026 ( 72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e52 ( 78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003ePathological Sampling Methods ( % )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eHysteroscopic curettage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1337 ( 93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e54 ( 83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eFractional curettage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e87 ( 6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e11 ( 16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNon-normally distributed continuous variables are described using the median(IQR).\u003c/p\u003e\n\u003cp\u003eValues are presented as number (%).\u003c/p\u003e\n\u003cp\u003eContinuous variables use the Wilcoxon rank-sum test, and categorical variables use the Chi-square test.\u003c/p\u003e\n\u003cp\u003eStatistical significance was set at p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003eGynecological history: Uterine fibroids, Adenomyosis, Endometriosis\u003c/p\u003e\n\u003cp\u003ePostmenopausal uterine bleeding Onset Interval: Interval between menopause onset and lastest postmenopausal uterine bleeding\u003c/p\u003e\n\u003cp\u003eOther ethnicity: Korean Chinese, Hui Chinese, Manchu Chinese, Mongol Chinese\u003c/p\u003e\n\u003cp\u003eAdditionally, the median follow-up was 3.0 years (IQR 2.0\u0026ndash;5.0), with 92% of participants completing \u0026ge;2 biopsies during this period. The AEH/EC group had a higher proportion of retirement as occupation (42.4% vs. 18.7%; P\u0026lt;0.05), increased menopause at \u0026ge;55 years (15.2% vs. 4.6%; P\u0026lt;0.05), reduced menstruation duration \u0026lt;30 years (7.6% vs. 17.6%; P\u0026lt;0.05), greater overweight/obesity prevalence (68.3% vs. 53.9%; P=0.031), higher hypertension (31.8% vs. 19.2%; P\u0026lt;0.05) and diabetes mellitus (9.1% vs. 2.8%; P\u0026lt;0.05), and more frequent use of fractional curettage for pathological sampling (16.9% vs. 6.1%; P\u0026lt;0.05). No significant differences were observed in marital status, ethnicity, age at menarche, gynecological history (uterine fibroids/adenomyosis/endometriosis), or most ultrasound parameters (endometrial mass/lesion, blood flow, echo pattern).\u003c/p\u003e\n\u003ch3\u003e2.\u0026nbsp; Variable Selection Results\u003c/h3\u003e\n\u003cp\u003eMultivariable logistic regression identified five independent predictors of AEH/EC (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePostmenopausal uterine bleeding occurring \u0026ge; 5 years after menopause demonstrated the strongest association (OR=14.55, 95% CI: 7.67\u0026ndash;27.04; P\u0026lt;0.05), followed by menstruation duration \u0026gt;40 years (OR=7.28, 95% CI: 2.50\u0026ndash;24.01; P\u0026lt;0.05). Menstrual irregularity significantly increased risk (OR=3.93, 95% CI: 1.74\u0026ndash;7.99; P\u0026lt;0.05), as did abnormal endometrial thickness (OR=2.92, 95% CI: 1.70\u0026ndash;5.27; P\u0026lt;0.05). Diabetes mellitus was associated with reduced risk (OR=0.40, 95% CI: 0.24\u0026ndash;0.66; P\u0026lt;0.05). Postmenopausal bleeding within 5 years of menopause also conferred elevated risk (OR=2.72, 95% CI: 1.15\u0026ndash;5.68; P\u0026lt;0.05), while menstruation duration of 30\u0026ndash;40 years showed non-significant association (OR=2.32, 95% CI: 1.01\u0026ndash;6.72; P=0.076) compared to \u0026lt;30 years.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMultivariable Predictors of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAEH/EC\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003ePostmenopausal uterine bleeding Onset Interval\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 5years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.15-5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026gt;= 5years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e14.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e7.67-27.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eEndometrial thickness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.7-5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eCumulative menstrual years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 30years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e30-40years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.01-6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026gt; 40years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e2.5-24.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eDiabetes Mellitus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.24-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMenstrual regularity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.74-7.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eStatistical significance was set at p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; Nomogram Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA clinical prediction nomogram integrating these nine predictors was developed (Figure 2). The tool quantifies risk through a point system (0\u0026ndash;350 points), with total scores converting to AEH/EC probabilities (0.1\u0026ndash;0.99). Key predictors contributing maximal points included menstrual irregularity, prolonged menstruation (\u0026gt; 40 years), and late postmenopausal bleeding. Risk stratification was defined as: low-risk (\u0026lt; 40 points), medium-risk (40\u0026ndash;70 points), and high-risk (\u0026gt;70 points). For example, a postmenopausal patient with menstrual irregularity (100 points), \u0026gt;40 years menstruation (67.5 points), and abnormal endometrial thickness (27.5 points) accumulates 195 points, corresponding to 90% AEH/EC risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;Model Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nomogram demonstrated robust discrimination and calibration performance during validation. ROC analysis yielded AUCs of 0.82 (95% CI: 0.80\u0026ndash;0.85) in the training cohort and 0.83 (95% CI: 0.73\u0026ndash;0.93) in the validation cohort (both P\u0026lt;0.05; Figure 3a). Calibration plots showed excellent agreement between predicted and observed outcomes, with a calibration slope of 1.000 and intercept of \u0026minus;0.177 (Figure 3b). The model achieved a Brier score of 0.172, indicating low overall prediction error. Decision curve analysis further confirmed the model\u0026apos;s clinical utility across threshold probabilities of 10% to 50%, particularly showing significantly greater standardized net benefit compared to the \u0026quot;intervene-all\u0026quot; or \u0026quot;intervene-none\u0026quot; strategies within the critical 20%-40% high-risk threshold range(Figure 3c).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study developed a nomogram demonstrating significant advantages in predicting AEH/EC risk among women who are over 40 years old and undergoing endometrial biopsy. Key findings identified Cumulative Menstrual Years (CMY \u0026gt;40 years) as a strong independent risk factor (increasing risk by 7.28-fold) and confirmed the critical predictive value of Postmenopausal Bleeding (PMB) timing, particularly PMB occurring ≥5 years postmenopause (OR=14.55). By menstrual irregularity, integrating CMY, BMI status, parity, diabetes status, \u0026nbsp;AUB, stratified PMB, endometrial thickness, and duration of progesterone administration, the model achieved exceptional predictive performance (AUC 0.82–0.83), significantly outperforming existing models based on conventional indicators.\u003c/p\u003e\n\u003cp\u003eRegarding risk factors, the key findings of this study show both consensus and differences with previous research. Points of consensus include: CMY\u0026gt;40 years, as a quantifiable indicator of cumulative estrogen exposure, significantly increasing AEH/EC risk (OR=7.28) aligns with numerous epidemiological studies\u0026nbsp;\u003cem\u003e(24, 25)\u003c/em\u003e; the strongest risk associated with late PMB (≥5 years) confirmed by stratified analysis (OR=14.55) is consistent with clinical guidelines and the majority of academic viewpoints(35); significantly increased risk associated with thickened endometrium (≥13mm premenopause, ≥5mm postmenopause; OR=2.92) also matches classical research and guideline recommendations(31, 32); and menstrual irregularity (OR=3.93) as an independent risk factor likely reflects anovulation or endocrine dysfunction(36).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary difference lies in this study’s observed inverse association between diabetes and endometrial cancer risk with an odds ratio of 0.40, contrasting with most studies reporting positive associations. (37, 38) This paradox may stem from insufficient control of BMI confounding, as adjustment occurred only via dichotomization at the Asian standard of 23 kg/m², lacking fine stratification or continuous variable analysis; whereas literature identifies BMI as a key shared risk factor and potent confounder—evidenced by Luo et al(39).and Lucenteforte et al(40). showing attenuated associations after BMI adjustment. Other factors include surveillance bias, where increased gynecological examinations in diabetics may enable earlier precancerous intervention; metformin’s potential influence despite the WHI finding no protective effect with a hazard ratio of 1.00, while lab studies suggest antitumor mechanisms; and population heterogeneity, where differing Asian BMI thresholds of 23 kg/m² versus Western standards of ≥25 kg/m² may introduce bias. Thus, the true diabetes-endometrial cancer relationship requires validation through larger prospective studies such as WHI-style extensions; rigorous confounder control including BMI modeled as a continuous variable or finely stratified into six tiers, with waist-hip ratio inclusion; obesity-diabetes interaction assessment; and stratification by diabetes duration and treatment per the WHI’s time-dependent analysis showing residual risk in new-onset diabetes.\u003c/p\u003e\n\u003cp\u003eCompared to models reported in previous literature, the nomogram constructed in this study possesses significant advantages and unique value. The primary advantage is a markedly improved prediction accuracy (AUC 0.82-0.83), significantly better than prior models relying on conventional indicators (age, BMI, bleeding history; AUC 0.64-0.77). This is mainly attributed to the innovative integration of CMY as a quantifiable indicator of estrogen exposure and the critical stratification of PMB timing (\u0026lt;5 years vs ≥5 years). Secondly, this model addresses the limitation of \"dynamic risk assessment\" in existing models. Existing models often use static stratification based on cross-sectional data, whereas this model, based on a retrospective cohort (≥2 biopsies, median follow-up 3 years), inherently allows its predictors (such as continuously accumulating CMY or newly occurring late PMB) to be suitable for dynamically monitoring changes in individual risk over time (e.g., CMY extension, new onset of late PMB). This enables assessment of risk evolution from benign/hyperplastic conditions to AEH/EC, thereby achieving more precise timing for follow-up and intervention. Thirdly, the model boasts high clinical practicality and applicability in primary care settings. All included variables (CMY, PMB timing, ET, menstrual regularity, diabetes status) can be easily obtained at the primary care level through history taking, basic physical examination, and transvaginal ultrasound. This overcomes the major obstacle faced by models relying on advanced imaging (e.g., MRI texture analysis) or expensive molecular markers, which are difficult to implement in resource-limited areas. Its intuitive risk score and stratification (low/medium/high risk) facilitate rapid clinical decision-making.\u003c/p\u003e\n\u003cp\u003eThe limitations of this study include its single-center retrospective design with inherent selection bias from including only a biopsy cohort; a limited number of AEH/EC cases (n = 66), affecting precision despite synthetic oversampling; a highly homogeneous cohort (99.4% Han Chinese), reducing generalizability; incomplete adjustment for BMI (a key confounder in the diabetes-EC association); and lack of data on genetic syndromes, molecular markers, and diabetes-specific variables (such as type, duration, and treatment). Future directions include: external validation in multi-ethnic, geographically diverse cohorts; prospective evaluation of biopsy reduction rates and impact on early detection; model refinement through incorporating BMI as a continuous variable or finer strata (e.g., WHO Asian BMI categories); development of a digital calculator for point-of-care use; and larger studies to clarify diabetes’ independent role through rigorous confounder adjustment. The model is developed based on data from a Chinese population (99.4% Han Chinese cohort) and employs BMI cutoffs suitable for Asian populations. This partially addresses the issue of insufficient generalizability of existing mainstream models (mostly built on European and American population data) to the Chinese population, providing a practical tool for primary healthcare in China that is more aligned with local epidemiological characteristics. Its clear potential for clinical benefit lies in effectively identifying low-risk individuals (score \u0026lt;40 points), potentially substantially reducing (\u0026gt;90%) unnecessary invasive endometrial biopsies and their associated physical and psychological burdens and economic costs, while maintaining a high detection rate (sensitivity ≥80%) for AEH/EC. This ability to reduce unnecessary biopsies and optimize resource utilization in primary care and resource-limited settings lacking advanced diagnostic equipment is one of its most significant value propositions.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study developed and validated a practical nomogram that significantly improves prediction of AEH/EC risk in women undergoing endometrial biopsy. Its superior performance (AUC 0.82-0.83) stems from integrating two key innovations: cumulative menstrual years (CMY \u0026gt;40 years; OR=7.28) as a novel measure of estrogen exposure, and stratified PMB timing (≥5 years postmenopause; OR=14.55) as a critical high-risk indicator. Relying solely on accessible clinical/ultrasound parameters, the model enables dynamic risk monitoring and effectively identifies low-risk women (score \u0026lt;40 points), while maintaining high sensitivity. Optimized for the Han Chinese population, this tool offers substantial clinical utility for resource-limited settings.Future work must: (1) validate the model in multi-ethnic cohorts, (2) refine BMI adjustment using WHO Asian classifications, and (3) prospectively quantify biopsy reduction rates and cost savings. Development of an open-access digital calculator is prioritized for point-of-care use.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAEH/EC, Atypical endometrial hyperplasia or Endometrial carcinoma\u003c/p\u003e\n\u003cp\u003eAEH, Atypical endometrial hyperplasia\u003c/p\u003e\n\u003cp\u003eAUB, abnormal uterine bleeding\u003c/p\u003e\n\u003cp\u003eAUC, area under the ROC curve\u003c/p\u003e\n\u003cp\u003eBMI, body mass index\u003c/p\u003e\n\u003cp\u003eCI, Confidence Interval\u003c/p\u003e\n\u003cp\u003eCMY, cumulative menstrual years\u003c/p\u003e\n\u003cp\u003eEC, Endometrial carcinoma\u003c/p\u003e\n\u003cp\u003eEMR-IPMCH, Electronic Medical Record-International Peace Maternity and Child Health Hospital\u003c/p\u003e\n\u003cp\u003eET, Endometrial thickness\u003c/p\u003e\n\u003cp\u003eIQR, interquartile range\u003c/p\u003e\n\u003cp\u003eOR, Odds Ratio\u003c/p\u003e\n\u003cp\u003ePMB, postmenopausal bleeding (defined as vaginal bleeding \u0026ge;12 months after cessation of menses)\u003c/p\u003e\n\u003cp\u003ePMB Onset Interval, Age at PMB diagnosis - Age at menopause\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSD, standard deviation\u003c/p\u003e\n\u003cp\u003eWHO, World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethics Committee of the International Peace Maternity and Child Health Hospital (Approval No. GKLW-A-2024-107-01). The ethics committee of the International Peace Maternity and Child Health Hospital waived the requirement for informed consent due to the retrospective nature of the study and all data being anonymized. All methods were carried out in accordance with relevant guidelines and regulations (e.g., Helsinki Declaration).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest relevant to this article was reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Decision Consulting Project of 2025 China Hospital Development Institute, Shanghai Jiao Tong University (CHDI-2025-Z-39), the Collaborative Guidance Project of Traditional Chinese and Western Medicine in General Hospitals of Shanghai Municipal Health Commission (ZXXT-202315), the major difficult diseases of Chinese and Western clinical cooperation construction project of the National Health Commission of the People's Republic of China (ZXXTQJ-2024), the Fundamental Research Funds for the Central Universities (YG2025QNA31)and Three-Year Action Plan for Improving Clinical Research Capability of International Peace Maternity and Child Health Hospital (IPMCH2024CR02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.M. and H.Z. contributed equally to this work as co-first authors, with S.M. listed first. J.F. and W.Y. are co-corresponding authors, with J.F. as the lead. S.M., H.Z. and J.F. conceived and designed the study. S.M., G.Z., W.Y. and J.F. acquired funding. S.M., H.Z. and W.Y. developed the methodology. S.M., H.Z. and G.Z. curated the data. S.M. and H.Z. wrote the original draft. All authors reviewed, edited, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the Gynecology Department for their meticulous documentation of patients' daily data, which was fundamental to ensuring the completeness of the data collection. We would also like to thank the Information Department for their invaluable support in the data acquisition process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGu B, Shang X, Yan M, Li X, Wang W, Wang Q, Zhang C. 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Discov Oncol. 2025;16(1):280.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe Y, Yingmei W, Yindan W, Wenxin M, Wenyan T, Fengxia X. Incidence and mortality of endometrial cancer in China, data from China Cancer Registry Annual Report, 2004\u0026ndash;2017. Zhonghua Yi Xue Za Zhi. 2024;104(10):721-8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGavrilyuk O, Braaten T, Weiderpass E, Licaj I, Lund E. Lifetime number of years of menstruation as a risk index for postmenopausal endometrial cancer in the Norwegian Women and Cancer Study. Acta Obstet Gynecol Scand. 2018;97(10):1168\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCramer DW. Incessant ovulation: a review of its importance in predicting cancer risk. Front Oncol. 2023;13:1240309.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConsultation WHOE. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDossus L, Allen N, Kaaks R, Bakken K, Lund E, Tjonneland A, et al. Reproductive risk factors and endometrial cancer: the European Prospective Investigation into Cancer and Nutrition. Int J Cancer. 2010;127(2):442\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eColombo N, Creutzberg C, Amant F, Bosse T, Gonzalez-Martin A, Ledermann J et al. ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer. International Journal of Gynecological Cancer. 2016;26(1):2\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin Q, Zhang J, Liu X, Zheng Q, Lin D, Pan M. Association between Healthy Eating Index-2015 total and component food scores with reproductive lifespan among postmenopausal women: a population-based study from NHANES 2005\u0026ndash;2016. BMC Public Health. 2024;24(1):2631.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOu R, Wei Q, Hou Y, Zhang L, Liu K, Lin J et al. Reproductive Lifespan and Motor Progression of Parkinson\u0026rsquo;s Disease. J Clin Med. 2022;11(20).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAytekin O, Karag\u0026ouml;z \u0026Ccedil;, G\u0026ouml;ktaş E, Tokalıoğlu AA, Tiryaki G\u0026uuml;ner G, \u0026Ouml;zkaya U\u0026ccedil;ar Y, et al. Preoperative predictors of concurrent endometrial carcinoma in patients with endometrial intraepithelial neoplasia: the role of HALP score and other inflammatory markers. J Turkish-German Gynecol Association. 2025;26(1):34\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRebecca Smith-Bindman MKK, Vickie MD, Feldstein A, Leslee Subak MD, Juergen Scheidler MD. PhD; Richard Brand, PhD; Deborah Grady, MD. Review Endovaginal Ultrasound to Exclude Endometrial Cancer and Other Endometrial Abnormalities. 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Br J Cancer. 2014;111(7):1432\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLucenteforte E, Bosetti C, Talamini R, Montella M, Zucchetto A, Pelucchi C, et al. Diabetes and endometrial cancer: effect modification by body weight, physical activity and hypertension. Br J Cancer. 2007;97(7):995\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endometrial Neoplasms, Endometrial Hyperplasia, Predictive Value of Tests, Perimenopause, Menstrual Cycle, Nomograms","lastPublishedDoi":"10.21203/rs.3.rs-7598063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7598063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEndometrial cancer poses a significant global health burden with rising mortality. Current diagnostics for women ≥40 with abnormal uterine bleeding or imaging abnormalities detect malignancy in \u0026lt;10% of biopsies, subjecting over 90% to unnecessary invasive procedures. Existing prediction models have suboptimal accuracy.To develop and validate a clinically practical nomogram incorporating the novel biomarker cumulative menstrual years, quantifying estrogen exposure, for predicting atypical endometrial hyperplasia or endometrial cancer risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study included 1,490 women (aged \u0026gt;40 years) who underwent ≥ 2 endometrial biopsies at the International Peace Maternity and Child Health Hospital between 2014- 2023. Univariable and multivariable logistic regression were used to identify potential independent predictors of atypical endometrial hyperplasia or endometrial carcinoma ( AEH/EC ). A nomogram prediction model was developed using significant predictors, with its performance internally validated through AUC analysis (discrimination) and decision curve analysis (clinical utility).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependt Risk factors were postmenopausal bleeding ≥5 years postmenopause (OR=14.55, 95% CI: 7.67–27.04), cumulative menstrual years\u0026gt;40 years (OR=7.28, 95% CI: 2.50–24.01), menstrual irregularity (OR=3.93, 95% CI: 1.74–7.99), abnormal endometrial thickness (OR=2.92, 95% CI: 1.70–5.27), and diabetes mellitus (paradoxical OR=0.40, 95% CI: 0.24–0.66). The nomogram demonstrated robust performance (training AUC=0.82; validation AUC=0.83), excellent calibration (slope=1.000), and clinical utility across thresholds (10–50%). Risk stratification thresholds: low (\u0026lt;40 points), medium (40–70 points), high (\u0026gt;70 points).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cumulative menstrual years integrated nomogram provides a practical, high-performance tool for dynamic AEH/EC risk stratification using routine parameters, while maintaining high sensitivity, particularly in resource-limited settings. The paradoxical protective association of diabetes (OR=0.40) requires cautious interpretation owing to incomplete BMI adjustment (dichotomized at 23 kg/m² without obesity stratification); prospective validation with granular metabolic profiling is warranted.\u003c/p\u003e","manuscriptTitle":"Clinicopathological Model for Predicting Endometrial Cancer and Atypical Hyperplasia in Women Aged \u0026gt;40 Years: Development and Evaluation in a Single-Institution Retrospective Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 19:30:45","doi":"10.21203/rs.3.rs-7598063/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-10T10:24:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-07T11:25:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-19T09:45:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-19T08:10:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-09-19T08:05:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f42b5385-1db7-413a-9f6a-22053a066b81","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-23T19:30:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 19:30:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7598063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7598063","identity":"rs-7598063","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}