Machine Learning-Based Nomogram for Predicting Endometrial Lesions after Tamoxifen Therapy in Breast Cancer Patients

Machine Learning-Based Nomogram for Predicting Endometrial Lesions after Tamoxifen Therapy in Breast Cancer Patients | 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 Article Machine Learning-Based Nomogram for Predicting Endometrial Lesions after Tamoxifen Therapy in Breast Cancer Patients Shaoshan Cao, Niannian Chen, Ying Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4715381/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Objective Endometrial lesions is a frequent complication following breast cancer, and current diagnostic tools have limitations. This study aims to develop a machine learning-based nomogram graph prediction model for the early detection of endometrial lesions in patients. The model is intended to provide risk assessment and facilitate personalized treatment strategies for premenopausal breast cancer patients. Method A retrospective study was conducted on 224 patients who underwent diagnostic curettage post-tamoxifen (TAM) therapy between November 2012 and November 2023. These patients exhibited signs of endometrial abnormalities or symptoms such as colporrhagia. Clinical data were collected and analyzed using R software version 4.3.2 to identify factors influencing the occurrence of endometrial lesions and to evaluate their predictive values. Three machine learning methods were employed to develop a risk prediction model, and the performances of these models were compared. The best-performing model was selected to construct a nomogram of endometrial lesions. Internal validation was conducted using the bootstrap method, and the model’s accuracy and fit were assessed using the concordance index (C-index) and calibration curves. Results Independent risk factors for endometrial lesions included ultrasound characteristics, duration of TAM therapy, presence of colporrhagia, and endometrial thickness (P < 0.05). Among the machine learning methods compared, the LASSO regression integrated with a multifactorial logistic regression model demonstrated strong performance, with a concordance index (C-index) of 0.874 and effective calibration (mean absolute error of conformity: 0.014). This model achieved an accuracy of 0.853 and a precision of 0.917, with a training set AUC of 0.874 (95% CI: 0.794–0.831) and a test set AUC of 0.891 (95% CI: 0.777-1.000), closely matching the predicted risk to the actual observed risk. Conclusion The developed prediction model effectively assesses the likelihood of endometrial lesions in premenopausal breast cancer patients. This model offers a theoretical foundation for improving clinical predictions and devising tailored treatment strategies for this patient group. breast cancer tamoxifen endometrial lesions nomogram prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Breast cancer is one of the most prevalent cancers among women worldwide, as reported by the World Health Organization’s International Agency for Research on Cancer (IARC) in 2024, with approximately 310,000 new cases annually 1 . In Asia, breast cancer shows a bimodal distribution, peaking first among women in their late 40s to early 50s, with 60% of these patients being premenopausal 2 . With aggressive treatment, the five-year survival rate exceeds 90%, and the ten-year survival rate surpasses 85%. Approximately 75% of new breast cancer cases are hormone receptor-positive 3 . Endocrine therapy, primarily involving Tamoxifen (TAM), a selective estrogen receptor modulator, is often required for five years or more following surgical chemotherapy in premenopausal breast cancer patients 4 . Due to the local receptor imbalance in endometrial tissues, TAM exerts an estrogen-like effect on the endometrium, leading to endometrial proliferation and glandular hypertrophy. This frequently results in abnormal glandular hyperplasia or structural changes, causing pathologies such as endometrial hyperplasia, polyps, endometrial cancer, and sarcoma 5 . Studies indicate that the incidence of endometrial thickening during endocrine therapy ranges from 5–30%, endometrial polyps from 26–60%, and endometrial cancer from approximately 0.8–8% 67 . These rates are 2–7 times higher than those in the general population 5 . Additionally, Asian populations face a 2.87-fold increased risk of endometrial cancer compared to European and American groups, with about 20% of cases occurring in premenopausal women 3 . Several risk factors for breast cancer and endometrial carcinoma (EC) have been identified, making increased awareness essential for reducing disease burden. Obesity, hypertension, and diabetes are recognized risk factors for both EC and breast cancer. Colporrhagia often serves as an early symptom of endometrial cancer. Consequently, identifying risk factors for endometrial lesions in younger breast cancer patients is crucial for enhancing early detection, diagnosis, and intervention. Currently, no screening method can accurately predict endometrial lesions; ultrasound remains the preferred screening tool and definitive diagnosis is achieved through endometrial biopsy. With the increased use of TAM and longer life expectancy among breast cancer survivors, the risk of endometrial lesions is rising. Repeated ultrasounds and endometrial biopsies, commonly employed to determine the continuation of endocrine therapy, are invasive and pose significant risks of complications. These procedures can adversely affect patients’ mental well-being and quality of life, potentially reducing their willingness to continue clinic visits and adhere to treatment regimens 8 . This may lead to the discontinuation of the medication or even tumor recurrence. Addressing the current research trends, the early prediction of endometrial lesions remains an urgent challenge to overcome. Although many current clinical prediction models rely primarily on simple logistic regression, the advent of artificial intelligence and machine learning has improved prediction accuracy, particularly in medical oncology and surgery. Given the limited use of machine learning for predicting endometrial lesions in premenopausal breast cancer patients, this study aims to identify risk factors for endometrial lesions and develop machine learning models to forecast its likelihood. This approach seeks to enhance preoperative risk assessment and provide a more tailored and theoretically informed basis for endocrine therapy in these patients. 2 Methods 2.1 Dataset This study retrospectively collected data from patients treated with tamoxifen (TAM) following breast cancer surgery who underwent diagnostic curettage at the gynecology department of Mianyang Central Hospital between 2012 and 2023. The ethical approval and participation consent followed the Helsinki Declaration guidelines. The inclusion criteria were: ( 1 ) premenopausal status at the time of breast cancer diagnosis; ( 2 ) before breast cancer, Vaginal ultrasound were normal; ( 3 ) postoperative treatment with TAM at a dosage of 20 mg/day; and ( 4 ) complete clinical case data and follow-up records. Exclusion criteria included: ( 1 ) patients with a history of other cancers; ( 2 ) patients with advanced or recurrent breast cancer at initial diagnosis; ( 3 ) serious underlying diseases affecting patient survival. Patients meeting these criteria were included in the study cohort. Using a nested case-control design, patients who developed endometrial lesions were identified as the case group, while those who did not develop lesions served as the control group. Collected clinical data included age, age at breast cancer diagnosis, family history of cancer, metastasis, number of deliveries and pregnancies, weight, height, body mass index (BMI), age at menarche, breastfeeding history, endometrial thickness, presence of colporrhagia, duration of TAM therapy, previous use of sex hormones, smoking history, hypertension, diabetes mellitus, hyperlipidemia, history of fibroid tumors, endometriosis, benign breast lesions, previous endometrial lesions, ultrasound features, and endometrial biopsy results. 2.2 Development and validation of prediction model This study followed the TRIPOD guidelines 9 . The data were initially cleaned and subjected to dimensionality reduction. The data were initially cleaned and subjected to dimensionality reduction. Univariate and multivariate analyses were performed to identify statistically significant factors between groups and to determine the optimal cut-off value for endometrial thickness. Three machine learning methods were employed to screen the data for high-risk factors: Least Absolute Shrinkage and Selection Operator(LASSO) regression combined with multifactor logistic regression, decision tree, and random forest. The 224 patients were randomly divided into a training set (85%) and a test set (15%). The machine learning model was trained on the training set and validated on the test set. Model performance was assessed using the receiver operating characteristic (ROC) curve and accuracy metrics. The optimal model was selected and internally validated using the Bootstrap resampling method (1,000 iterations). The final model was presented as a nomogram graph nomogram, with the concordance index (C-index) and decision curve analysis (DCA) used to evaluate its clinical application value. 2.3 Statistical Methods. Data analysis and graphing were performed using SPSS and R version 4.3.2 software. All data were statistically described: normally distributed measurements were expressed as mean ± standard deviation (Χ̅± s) and compared using the t-test; non-normally distributed measurements were expressed as median [P25, P75] and compared using the rank-sum test. Categorical variables were described as constituent ratios and compared using the chi-square test or Fisher's exact test. Statistical significance was set at p < 0.05. 3 Results 3.1 Patient characteristics and endometrial biopsy results A total of 224 premenopausal patients undergoing postoperative endocrine therapy for breast cancer were included in this study. Endometrial biopsies confirmed endometrial lesions in 98 cases, while the remaining 126 cases served as the control group, resulting in a prevalence rate of 43.75% (98/224). Detailed patient characteristics are presented in Table 1 . Table 1 Pathological characteristics of the endometrium [cases (%)] physiology control group(n = 126) Lesion group(n = 98) normal 126 (100) 0 (0) Endometrial polyp 0 (0) 72 (73.5) Endometrial hyperplasia without atypia 0 (0) 15 (15.3) Endometrial polyp with EH 0 (0) 6 (6.1) Atypical hyperplasia 0 (0) 2 ( 2 ) Atypical polypoid adenomyoma 0 (0) 1 ( 1 ) Endometrioid adenocarcinoma 0 (0) 2 ( 2 ) In the lesion group, 39 cases (39.8%) had a medication duration of 2 years, 12 cases (12.2%) had a medication duration of 2–5 years, and 47 cases (48.0%) had a medication duration of more than 5 years. The mean endometrial thickness in the case group was 1.20 cm, while it was 0.76 cm in the control group, as shown in Table 2 . Table 2 Baseline characteristics of premenopausal breast cancer patients [cases (%)] control group Lesion group P-value 126 98 age(year) 51.68 ± 5.30 50.97 ± 5.28 0.318 Age at diagnosis of breast cancer(year) 43.91 ± 4.39 43.15 ± 5.13 0.234 gravidity 2.73 ± 1.56 2.74 ± 1.59 0.945 parity 1.00[1.00,1.00] 1.00[1.00,2.00] 0.342 menophania(year) 13.00[12.25,14.00] 13.00[12.00,14.00] 0.679 weight(kg) 57.63 ± 6.76 56.85 ± 7.54 0.416 height(m) 1.57 ± 0.05 1.57 ± 0.05 0.589 Endometrial thickness(cm) 0.76 ± 0.37 1.20 ± 0.53 < 0.001 BMI(kg/m2) 23.39 ± 2.60 23.16 ± 2.78 0.533 Ultrasonic characteristics < 0.001 Normal 92(73.0) 28(28.6) Uneven echo 32(25.4) 31(31.6) Uterine cavity occupation 2(1.6) 35(35.7) Endometrium heterogeneity combined with uterine cavity occupation 0(0.0) 4(4.1) duration of tamoxifen therapy 0.002 Within 2 years 70(55.6) 39(39.8) 2–5 years 24(19.0) 12(12.2) More than 5 years 32(25.4) 47(48.0) metastatic 0.061 yes 41(32.5) 20(20.4) no 85(67.5) 78(80.6) Family history of cancer 0.302 yes 18(14.3) 20(20.4) no 108(85.7) 78(80.6) breastfeeding 0.455 yes 121(96.0) 91(92.9) no 5(4.0) 7(7.1) diabetes 0.463 yes 4(3.2) 6(6.1) no 122(96.8) 92(93.9) hypertension 0.496 yes 12(9.5) 6(6.1) no 114(90.5) 92(93.9) smoking 1 yes 1(0.8) 1(1.0) no 125(99.2) 97(99.0) hyperlipemia 0.256 yes 16(12.7) 7(7.1) no 110(87.3) 91(92.9) colporrhagia 0.001 yes 30(23.8) 44(44.9) no 96(76.2) 54(55.1) hormoneuse 0.193 yes 7(5.6) 11(11.2) no 119(94.4) 87(88.8) leiomyoma 0.092 yes 44(34.9) 46(46.9) no 82(65.1) 52(53.1) endometriosis 0.826 yes 1(0.8) 2(2.0) no 125(99.2) 96(98.0) endometrialdisease 0.008 yes 3(2.4) 12(12.2) no 123(97.6) 86(87.8) benignlesion 1 yes 47(37.3) 36(36.7) no 79(62.7) 62(63.3) BMI group 0.843 underweight 1(0.8) 2(2.0) Normal 89(70.6) 66(67.3) overweight 32(25.4) 27(27.6) obesity 4(3.2) 3(3.1) Note: a. BMI refers to body mass index, calculation formula: BMI(kg/m2) = weight(kg)/(height×height(m2)), group: underweight < 18.5, normal:18.5–23.9, overweight:24-27.9, obesity:≥28; b. Data were expressed as (s) or n (%) or M (P25, P75), p < 0.05 statistically significant; nonnorm refers to data being non-normally distributed. 3.2 Correlation of clinical factors on endometrial lesions The analysis revealed no statistically significant differences between the groups for age, age at diagnosis, presence of metastasis, hypertension, diabetes mellitus, history of endometriosis, benign breast lesions, age at menarche, number of pregnancies, number of deliveries, smoking, hyperlipidemia, history of breastfeeding, family history of cancer, pathological type of breast cancer, height, weight, BMI, hormone use, history of uterine fibroids, and previous benign breast lesions (P > 0.05). However, significant differences were found between the groups for ultrasound characteristics, duration of TAM therapy, presence of colporrhagia, previous endometrial lesions, and endometrial thickness (P < 0.05). The case group showed significantly higher rates of colporrhagia, greater endometrial thickness, and longer duration of TAM therapy compared to the control group. The mean endometrial thickness was greater in the case group (1.20 ± 0.53 cm) compared to the control group (0.76 ± 0.37 cm, P < 0.001), and a higher proportion of colporrhagia symptoms was observed in the case group (44.9% vs. 23.8%, P < 0.001). The area under the ROC curves (AUC) for each factor was as follows: ultrasound characteristics (0.770), endometrial thickness (0.749), duration of TAM (0.608), colporrhagia (0.605), and history of leiomyoma (0.560). The ROC curve for endometrial thickness had the largest Youden index with a cut-off value of 0.438, a sensitivity of 71.4%, and a specificity of 72.4%, corresponding to an optimal ultrasonographic diagnostic threshold of 0.825 cm for abnormal endometrium. Detailed results are presented in Table 2 and illustrated in Figs. 1 . 4 Machine Learning The heat map of correlation revealed many linear correlations between variables(Figs. 2 ). To minimize interference, clinical factors were included as predictors input machine learning model. 4.1 LASSO Regression with Logistic Regression Algorithm In the LASSO regression to select the most predictive features. A 10-fold cross-validation was performed, resulting in the selection of four variables as independent predictors, with endometrial lesions occurrence as the dependent variable. A multifactorial binary logistic regression analysis showed that previous endometrial lesions was not statistically significant (P > 0.05). However, ultrasound characteristics, duration of TAM therapy, presence of colporrhagia symptoms, and endometrial thickness were identified as independent risk factors for endometrial lesions (P < 0.05). These factors were included in the multifactorial logistic regression model. The dataset was split into a training set (n = 190) and a validation set (n = 34) in an 8.5:1.5 ratio. After 1,000 Bootstrap self-samplings for internal validation, the results showed a C-index of 0.874, indicating excellent model discrimination. The model demonstrated an accuracy of 0.853, a precision of 0.917, with a training set AUC of 0.874 (95% CI: 0.794–0.831), and a test set AUC of 0.891 (95% CI: 0.777-1.000). Detailed results are shown in Figs. 3 , 6 a, and Table 3 . Table 3 Multifactorial Logistic Regression Analysis of Independent Risk Factors Based on Lasso Regression Coef S.E. Wald Z Df P -value OR 95%CI Constant -3.851 0.637 -6.04 7 < 0.001 - - colporrhagia 1.385 0.422 3.28 7 0.001 3.996 1.746–9.145 Endometrial thickness 1.887 0.457 4.13 7 < 0.001 3.747 2.002–7.014 duration of tamoxifen therapy 7 2-5years: Within 2 years 0.35 0.56 0.63 - 0.532 1.419 0.474–4.253 More than 5 years: Within 2 years 0.946 0.432 2.19 - 0.029 2.575 1.104–6.006 Ultrasonic characteristics 7 Uneven echo 1.491 0.434 3.43 - 0.001 4.44 1.897–10.394 Uterine cavity occupation 3.349 0.802 4.17 - < 0.001 28.475 5.908–137.250 Endometrium heterogeneity combined with uterine cavity occupation 7.192 21.794 0.33 - 0.741 1328.2 3.733E-16-4.726E + 21 Note: OR: ratio of ratios; CI: confidence interval. 4.2 Decision Tree Algorithm A prediction model for endometrial lesions was constructed using a decision tree. The decision tree was built through recursive partitioning and pruned to avoid overfitting, with the cp value adjusted to 0.036 to improve the model's generalization ability. The average accuracy of the training set was 0.800, with an AUC of 0.888 (95% CI: 0.840–0.937). The average accuracy of the test set was 0.740 (95% CI: 0.533–0.947), with an AUC of 0.800 (95% CI: 0.640–0.960), demonstrating good prediction performance. Detailed result are illustrated in Figs. 4 , 6 b. 4.3 Random Forest Algorithm A prediction model for endometrial lesions was also constructed using a random forest approach. Five hundred trees were established, and as the number of trees increased, the out-of-bag (OOB) error rate decreased and eventually stabilized, indicating a relatively stable model. Factors were ranked in order of importance, with ultrasound characteristics, endometrial thickness, duration of TAM therapy, and colporrhagia symptoms being the most influential. The OOB error rate for the random forest training set was approximately 23.68%, with an average accuracy of 100% (95% CI: 0.981-1.000), and an AUC of 1.000 (95% CI: 1.000–1.000). The test set had an average accuracy of 0.735 (95% CI: 0.556–0.871), with an AUC of 0.784 (95% CI: 0.632–0.867), indicating good prediction performance. Detailed results are shown in Figs. 5 , 6 c. 5 Model comparison and visualization A comparison of the accuracy and AUC of the models determined that the LASSO regression combined with multifactorial logistic regression (LR) was the optimal model (see Fig. 6 , Table 4 ). Table 4 Comparison of models model accuracy AUC sensitivity specificity LR 0.853 0.891 1.000 0.733 Decision tree 0.740 0.800 0.947 0.667 Random forest 0.735 0.784 0.632 0.867 Consequently, a nomogram graph model was constructed based on this combination for predicting the occurrence of endometrial lesions (see Figs. 7 ).For example, a female patient with more than five years of TAM therapy, colporrhagia, and an endometrial thickness of 1.6 cm would have corresponding scores of approximately 22.5, 17.5, and 60, respectively, for a total score of 100. This total score corresponds to an estimated probability of developing endometrial lesions of 80%. The application of the nomogram graph provides a clear and concise explanation of the model's individualized prediction for the patient. The calibration curve suggests that the mean absolute error between predicted and actual values is 0.014, indicating that the predicted risk closely aligns with the actual risk. The Decision Curve Analysis (DCA) curve evaluates the predictive model and the concordance diagnostic test, calculating the clinical "net benefit" of the predictive model. The results show that using a column chart for predictions is valuable within a threshold probability range of 5–90% (see Figs. 8 , 9 ). 6 Discussion In this study, we developed and evaluated three machine learning prediction models to accurately predict the risk of endometrial lesions in premenopausal breast cancer patients undergoing TAM therapy. The best predictive performance was achieved by LASSO regression combined with logistic regression, which demonstrated an accuracy of 0.853 and precision of 0.917 using four easily accessible patient features. This model had high diagnostic performance, with an AUC of 0.891 (95% CI: 0.777-1.000). The findings confirm that ultrasonographic features, duration of TAM administration, endometrial thickness, and colporrhagia symptoms are clear predictors of endometrial lesions. A national retrospective study of 102 breast cancer patients treated with TAM postoperatively found that the duration of TAM use and symptoms of abnormal colporrhagia were significant risk factors for developing endometrial lesions, consistent with our study. Additionally, a large body of epidemiologic evidence suggests that TAM is associated with an increased risk of endometrial lesions, with the risk of developing endometrial carcinoma (EC) being 1.5–6.9 times higher in a dose- and time-dependent manner 10 . The ATLAS study found that patients using TAM for 10 years had a higher cumulative risk of endometrial cancer compared to those using it for 5 years 4 . However, only 10% of the patients in the ATLAS study were premenopausal, which may limit the generalizability of these findings. Our study showed that the duration of TAM was an independent risk factor for developing endometrial lesions, aligning with previous studies 11 . However, the cumulative dose of the drug was not clarified. Choi et al. demonstrated that benign endometrial disease incidence was highest in subjects under 40 years of age treated with TAM, significantly increasing the risk of endometrial cancer 12 . Similarly, Liu et al. found that the standardized incidence of endometrial cancer was elevated in breast cancer patients diagnosed after the age of 40 13 . Younger patients treated with TAM have a higher risk of subsequent endometrial cancer, particularly those aged 40-49 14 . Bergman’s study also indicated that endometrial cancers caused by TAM were more malignant and aggressive 11 . Some studies have shown no correlation between TAM and endometrial lesions. For instance, Takashima 15 found no significant association between shorter TAM therapy duration and endometrial lesions. Chiofalo and Chu also reported no correlation between TAM and endometrial cancer development 141617 . In our study, ultrasound characteristics were the most important factor in predicting endometrial lesions, consistent with previous studies. Ultrasound is the preferred monitoring tool, and abnormal occupancy or heterogeneous endometrial echogenicity on ultrasound increases the likelihood of developing endometrial lesions and necessitating endometrial biopsy. Previous NSABP studies, which included mainly postmenopausal women, suggested no additional monitoring for asymptomatic women to avoid unnecessary invasive procedures. However, this may underestimate the risk in premenopausal patients 1819 . Young breast cancer patients undergoing prolonged TAM therapy require more attention. Endometrial screening and evaluation should be performed before TAM treatment, with regular transvaginal ultrasound monitoring to detect and manage endometrial lesions early. Endometrial thickness was also a significant factor in endometrial lesion occurrence, with the optimal diagnostic threshold being 0.825 cm, similar to previous findings by Zhouqi and Burkart 220 . Since TAM stimulates endometrial gland hypertrophy, leading to pharmacological thickening, establishing a TAM-related endometrial thickness threshold in young breast cancer patients is challenging. Colporrhagia was identified as an important risk factor. Patients with colporrhagia are more likely to develop endometrial lesions, and this symptom serves as a warning for early hospital visits, improving detection rates. However, Maria et al. found no difference in abnormal colporrhagia between the case group and patients with normal endometrium, highlighting the need for further research 21 . This study demonstrates that machine learning approaches can achieve high accuracy in predicting endometrial lesions. Most current clinical prediction models rely on linear relationships between variables, often resulting in poor predictive ability. Machine learning applications in medicine are becoming widespread, offering effective tools for clinical diagnosis and prediction. Our study visualized and predicted endometrial lesions incidence using machine learning, providing valuable insights for gynecologists evaluating premenopausal breast cancer patients during endocrine therapy. The LASSO regression combined with multifactorial logistic regression prevented overfitting, with validation results showing an average absolute error of 0.014 between predicted and actual values, suggesting clinical diagnostic significance for endometrial lesion prognosis. However, this study has limitations. It was a single-center retrospective study with limited data collection and a small sample size, which could introduce selection and recall biases. More objective indicators and larger sample sizes are needed to clarify endometrial thickness criteria. The model also lacks external validation. Future studies should incorporate comprehensive factors and utilize joint neural network prediction models to provide a basis for individualized endocrine therapy treatment in premenopausal breast cancer patients. In conclusion, our study identified ultrasound characteristics, TAM duration, colporrhagia, and endometrial thickness as independent risk factors for endometrial lesions in premenopausal breast cancer patients. We developed a predictive risk model using machine learning, with LASSO regression combined with multifactorial logistic regression showing the best performance. Regular monitoring of these factors can aid in early detection and reduction of endometrial lesions, providing a basis for evaluating endocrine therapy, endometrial monitoring during treatment, and individualized therapeutic strategies for breast cancer patients. Abbreviations TAM Tamoxifen EC endometrial carcinoma IARC the World Health Organization’s International Agency for Research on Cancer BMI body mass index LASSO Least Absolute Shrinkage and Selection Operator ROC curve Reiver Operating Characteristic Curve C-index the Consistency index DCA decision curve analysis AUC Area Under Curve OOB error out-of-bag error rate Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Mianyang Central Hospital (No. S20240332-01). Due to the retrospective nature of the study, IRB waived the need of obtaining informed consent. Author contributions All authors were involved in writing, all authors were involved in developing the methodology employed in the project, reviewing and editing the final draft. Conflict of interest statement None declared. Funding information No. Data availability statement All data are fully available without restriction. 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Cureus . 2021;13(11):e20030. doi:10.7759/cureus.20030 Fisher B, Costantino JP, Redmond CK, Fisher ER, Wickerham DL, Cronin WM. Endometrial cancer in tamoxifen-treated breast cancer patients: findings from the National Surgical Adjuvant Breast and Bowel Project (NSABP) B-14. J Natl Cancer Inst . 1994;86(7):527-537. doi:10.1093/jnci/86.7.527 Fisher B, Costantino JP, Wickerham DL, et al. Tamoxifen for prevention of breast cancer: report of the National Surgical Adjuvant Breast and Bowel Project P-1 Study. J Natl Cancer Inst . 1998;90(18):1371-1388. doi:10.1093/jnci/90.18.1371 Burkart C, Wight E, Pók J, et al. [Ultrasound endometrium follow-up during tamoxifen treatment: Really not reliable or useful after all?]. Ultraschall Med . 2001;22(3):136-142. doi:10.1055/s-2001-15243 Jeon J, Kim SE, Lee DY, Choi D. Factors associated with endometrial pathology during tamoxifen therapy in women with breast cancer: a retrospective analysis of 821 biopsies. Breast Cancer Res Treat . 2020;179(1):125-130. doi:10.1007/s10549-019-05448-w Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Oct, 2024 Reviews received at journal 12 Aug, 2024 Reviews received at journal 12 Aug, 2024 Reviewers agreed at journal 12 Aug, 2024 Reviewers agreed at journal 12 Aug, 2024 Reviewers invited by journal 12 Aug, 2024 Editor assigned by journal 12 Aug, 2024 Editor invited by journal 15 Jul, 2024 Submission checks completed at journal 13 Jul, 2024 First submitted to journal 10 Jul, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4715381","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334174974,"identity":"bd58b822-e764-4d34-ab5e-238f2d0888f6","order_by":0,"name":"Shaoshan Cao","email":"","orcid":"","institution":"Mianyang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shaoshan","middleName":"","lastName":"Cao","suffix":""},{"id":334174975,"identity":"3a7f44ed-f9eb-4bd5-9801-f6d2c824908f","order_by":1,"name":"Niannian Chen","email":"","orcid":"","institution":"Southwest University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Niannian","middleName":"","lastName":"Chen","suffix":""},{"id":334174976,"identity":"87173716-25f6-4c4d-b0a9-af988b375018","order_by":2,"name":"Ying Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPmYQWWEjx8/e2PjgAzFa2MBazqQZS/YcbjacQZQWEMHYdjhxw430NmkOorSwsz/+8IONmbHh5sMGaQYGOzndBsIOSzDs4QHqmZ3YYFzAkGxsdoCwlgMJPBJAPdKJDckzGA4kbiOshbHh4B8DoB7Jgw2HeYjTwszYzJMA1CPB2NhMpBagJpkDID2JzYwzDIjwCz//8ccf3/77X7//+PHnPz5U2MkR1IIGDEhTPgpGwSgYBaMABwAAO5g8XBugecoAAAAASUVORK5CYII=","orcid":"","institution":"Mianyang Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-07-10 04:03:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4715381/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4715381/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-82373-z","type":"published","date":"2025-01-06T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62323202,"identity":"1ba8867f-9a40-4fd9-9e58-8781204bc8cc","added_by":"auto","created_at":"2024-08-13 02:10:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45711,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for monofactor analysis of endometrial thickness\u003c/p\u003e\n\u003cp\u003eNote: Vertical coordinate: sensitivity, horizontal coordinate: specificity\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/f1141a2de3fc7c1b797f6273.png"},{"id":62323205,"identity":"bcdc6d6d-9fc6-4886-ad24-142f5822c15e","added_by":"auto","created_at":"2024-08-13 02:10:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103550,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of correlation between related data\u003c/p\u003e\n\u003cp\u003eNote: * indicates correlation between data, *** means significant correlation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/7ef00107c5cd5d5b1bf751f6.png"},{"id":62323211,"identity":"dfbf73d4-c3ab-495d-8981-c80305ff2c7f","added_by":"auto","created_at":"2024-08-13 02:10:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299710,"visible":true,"origin":"","legend":"\u003cp\u003eVariable filtering process for Lasso regression analysis\u003c/p\u003e\n\u003cp\u003eNote: A: Cross-validation plots for selecting the optimal lambda (λ) in LASSO regressions use 10-fold cross-validation. The binomial deviation is plotted against log(λ). The left vertical dashed line indicates the value of λ associated with the minimum deviation, and the right vertical dashed line indicates the optimal value of λ determined by the minimum deviation and 1 standard deviation of the minimum deviation; B:25 characteristics were included in the LASSO regression, and a coefficient distribution plot was generated based on the log(λ) series, showing that regression coefficient estimates evolve with increasing regularization. Four non-zero coefficient variables, drug duration, endometrial thickness, and colporrhagia, were selected from the 25 variables to derive the optimal lambda.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/7a8866a716af0be15e9bcb12.png"},{"id":62323679,"identity":"da3c8ef9-0716-4872-b33b-41e5a9ac8ece","added_by":"auto","created_at":"2024-08-13 02:18:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79665,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Tree Prediction Model after Pruning\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/93d677e1a94a881a55283455.png"},{"id":62323681,"identity":"84c0e382-3bfd-43a4-b893-9798c75c60f8","added_by":"auto","created_at":"2024-08-13 02:18:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":166986,"visible":true,"origin":"","legend":"\u003cp\u003eRandom Forest Prediction Model\u003c/p\u003e\n\u003cp\u003eNote: A: random forest model; B: feature importance ranking\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/e3047c8965dbed2ee7be5e04.png"},{"id":62323207,"identity":"d4e06a9a-705b-47f3-85a8-a7137baa6e16","added_by":"auto","created_at":"2024-08-13 02:10:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":152443,"visible":true,"origin":"","legend":"\u003cp\u003eModel ROC Curve\u003c/p\u003e\n\u003cp\u003eNote: a: ROC Curve of LASSO Regression with Logistic Regression Algorithm; b: ROC curves for decision tree prediction model after pruning; c: ROC curves for the Random Forest prediction model.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/cc7699a803e277fb8c2f8ac6.png"},{"id":62323680,"identity":"b4f3f829-b8ea-4598-86b4-81001e33e13b","added_by":"auto","created_at":"2024-08-13 02:18:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":80266,"visible":true,"origin":"","legend":"\u003cp\u003eColumn line diagram for predicting endometrial pathology\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/a6efbe57d8ff874d79de5efe.png"},{"id":62323210,"identity":"f405630f-8fda-4d49-9823-bcd4b0cce215","added_by":"auto","created_at":"2024-08-13 02:10:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":140629,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for the column-line diagram model\u003c/p\u003e\n\u003cp\u003eNote: Horizontal coordinate: predicted incidence of column-line plots, vertical coordinate: actual incidence. The solid black line represents the performance after internal validation by self-sampling 1000 times, the thin black dashed line represents the performance of the column-line graph, and the thick black dashed line represents the perfect prediction of the ideal model. The prediction accuracy of the column-line diagram is better when the line is closer to the thick black dashed line.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/bbf4519bf4553825ecc75392.png"},{"id":62323682,"identity":"38396e19-cb82-456d-8825-df9145afb49c","added_by":"auto","created_at":"2024-08-13 02:18:43","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":107345,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Curve Analysis for Column Line Charts\u003c/p\u003e\n\u003cp\u003eNote: The horizontal coordinate is the risk threshold probability and the vertical coordinate is the net benefit after pros and cons. Blue represents the training set, red represents the test set, and gray and black represent the hypothesis that all patients have or do not develop endometriosis, respectively.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/eac77e29557515c077e9f8d7.png"},{"id":73694173,"identity":"d7b419f3-f0d5-43e1-841d-b8be86db3038","added_by":"auto","created_at":"2025-01-13 16:11:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2112732,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4715381/v1/45e4ad84-87cc-41df-9eb3-0a6662ab8ab8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning-Based Nomogram for Predicting Endometrial Lesions after Tamoxifen Therapy in Breast Cancer Patients","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBreast cancer is one of the most prevalent cancers among women worldwide, as reported by the World Health Organization\u0026rsquo;s International Agency for Research on Cancer (IARC) in 2024, with approximately 310,000 new cases annually\u003csup\u003e1\u003c/sup\u003e. In Asia, breast cancer shows a bimodal distribution, peaking first among women in their late 40s to early 50s, with 60% of these patients being premenopausal\u003csup\u003e2\u003c/sup\u003e. With aggressive treatment, the five-year survival rate exceeds 90%, and the ten-year survival rate surpasses 85%. Approximately 75% of new breast cancer cases are hormone receptor-positive\u003csup\u003e3\u003c/sup\u003e. Endocrine therapy, primarily involving Tamoxifen (TAM), a selective estrogen receptor modulator, is often required for five years or more following surgical chemotherapy in premenopausal breast cancer patients\u003csup\u003e4\u003c/sup\u003e. Due to the local receptor imbalance in endometrial tissues, TAM exerts an estrogen-like effect on the endometrium, leading to endometrial proliferation and glandular hypertrophy. This frequently results in abnormal glandular hyperplasia or structural changes, causing pathologies such as endometrial hyperplasia, polyps, endometrial cancer, and sarcoma\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudies indicate that the incidence of endometrial thickening during endocrine therapy ranges from 5\u0026ndash;30%, endometrial polyps from 26\u0026ndash;60%, and endometrial cancer from approximately 0.8\u0026ndash;8%\u003csup\u003e67\u003c/sup\u003e. These rates are 2\u0026ndash;7 times higher than those in the general population\u003csup\u003e5\u003c/sup\u003e. Additionally, Asian populations face a 2.87-fold increased risk of endometrial cancer compared to European and American groups, with about 20% of cases occurring in premenopausal women\u003csup\u003e3\u003c/sup\u003e. Several risk factors for breast cancer and endometrial carcinoma (EC) have been identified, making increased awareness essential for reducing disease burden. Obesity, hypertension, and diabetes are recognized risk factors for both EC and breast cancer. Colporrhagia often serves as an early symptom of endometrial cancer. Consequently, identifying risk factors for endometrial lesions in younger breast cancer patients is crucial for enhancing early detection, diagnosis, and intervention.\u003c/p\u003e \u003cp\u003eCurrently, no screening method can accurately predict endometrial lesions; ultrasound remains the preferred screening tool and definitive diagnosis is achieved through endometrial biopsy. With the increased use of TAM and longer life expectancy among breast cancer survivors, the risk of endometrial lesions is rising. Repeated ultrasounds and endometrial biopsies, commonly employed to determine the continuation of endocrine therapy, are invasive and pose significant risks of complications. These procedures can adversely affect patients\u0026rsquo; mental well-being and quality of life, potentially reducing their willingness to continue clinic visits and adhere to treatment regimens\u003csup\u003e8\u003c/sup\u003e. This may lead to the discontinuation of the medication or even tumor recurrence. Addressing the current research trends, the early prediction of endometrial lesions remains an urgent challenge to overcome.\u003c/p\u003e \u003cp\u003eAlthough many current clinical prediction models rely primarily on simple logistic regression, the advent of artificial intelligence and machine learning has improved prediction accuracy, particularly in medical oncology and surgery. Given the limited use of machine learning for predicting endometrial lesions in premenopausal breast cancer patients, this study aims to identify risk factors for endometrial lesions and develop machine learning models to forecast its likelihood. This approach seeks to enhance preoperative risk assessment and provide a more tailored and theoretically informed basis for endocrine therapy in these patients.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Dataset\u003c/h2\u003e \u003cp\u003eThis study retrospectively collected data from patients treated with tamoxifen (TAM) following breast cancer surgery who underwent diagnostic curettage at the gynecology department of Mianyang Central Hospital between 2012 and 2023. The ethical approval and participation consent followed the Helsinki Declaration guidelines. The inclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) premenopausal status at the time of breast cancer diagnosis; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) before breast cancer, Vaginal ultrasound were normal; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) postoperative treatment with TAM at a dosage of 20 mg/day; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) complete clinical case data and follow-up records. Exclusion criteria included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients with a history of other cancers; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) patients with advanced or recurrent breast cancer at initial diagnosis; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) serious underlying diseases affecting patient survival.\u003c/p\u003e \u003cp\u003ePatients meeting these criteria were included in the study cohort. Using a nested case-control design, patients who developed endometrial lesions were identified as the case group, while those who did not develop lesions served as the control group. Collected clinical data included age, age at breast cancer diagnosis, family history of cancer, metastasis, number of deliveries and pregnancies, weight, height, body mass index (BMI), age at menarche, breastfeeding history, endometrial thickness, presence of colporrhagia, duration of TAM therapy, previous use of sex hormones, smoking history, hypertension, diabetes mellitus, hyperlipidemia, history of fibroid tumors, endometriosis, benign breast lesions, previous endometrial lesions, ultrasound features, and endometrial biopsy results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Development and validation of prediction model\u003c/h2\u003e \u003cp\u003eThis study followed the TRIPOD guidelines\u003csup\u003e9\u003c/sup\u003e. The data were initially cleaned and subjected to dimensionality reduction. The data were initially cleaned and subjected to dimensionality reduction. Univariate and multivariate analyses were performed to identify statistically significant factors between groups and to determine the optimal cut-off value for endometrial thickness. Three machine learning methods were employed to screen the data for high-risk factors: Least Absolute Shrinkage and Selection Operator(LASSO) regression combined with multifactor logistic regression, decision tree, and random forest.\u003c/p\u003e \u003cp\u003eThe 224 patients were randomly divided into a training set (85%) and a test set (15%). The machine learning model was trained on the training set and validated on the test set. Model performance was assessed using the receiver operating characteristic (ROC) curve and accuracy metrics. The optimal model was selected and internally validated using the Bootstrap resampling method (1,000 iterations). The final model was presented as a nomogram graph nomogram, with the concordance index (C-index) and decision curve analysis (DCA) used to evaluate its clinical application value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Methods.\u003c/h2\u003e \u003cp\u003eData analysis and graphing were performed using SPSS and R version 4.3.2 software. All data were statistically described: normally distributed measurements were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Χ̅\u0026plusmn; s) and compared using the t-test; non-normally distributed measurements were expressed as median [P25, P75] and compared using the rank-sum test. Categorical variables were described as constituent ratios and compared using the chi-square test or Fisher's exact test. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient characteristics and endometrial biopsy results\u003c/h2\u003e \u003cp\u003eA total of 224 premenopausal patients undergoing postoperative endocrine therapy for breast cancer were included in this study. Endometrial biopsies confirmed endometrial lesions in 98 cases, while the remaining 126 cases served as the control group, resulting in a prevalence rate of 43.75% (98/224). Detailed patient characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathological characteristics of the endometrium [cases (%)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ephysiology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group(n\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLesion group(n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial polyp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (73.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial hyperplasia without atypia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (15.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial polyp with EH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtypical hyperplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtypical polypoid adenomyoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the lesion group, 39 cases (39.8%) had a medication duration of 2 years, 12 cases (12.2%) had a medication duration of 2\u0026ndash;5 years, and 47 cases (48.0%) had a medication duration of more than 5 years. The mean endometrial thickness in the case group was 1.20 cm, while it was 0.76 cm in the control group, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of premenopausal breast cancer patients [cases (%)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLesion group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.68\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis of\u003c/p\u003e \u003cp\u003ebreast cancer(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egravidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eparity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00[1.00,1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00[1.00,2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emenophania(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.00[12.25,14.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00[12.00,14.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eweight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.63\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheight(m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial thickness(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltrasonic characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUneven echo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine cavity occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrium heterogeneity\u003c/p\u003e \u003cp\u003ecombined with uterine cavity occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eduration of tamoxifen therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetastatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78(80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108(85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78(80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebreastfeeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121(96.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122(96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92(93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92(93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125(99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97(99.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehyperlipemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110(87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecolporrhagia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehormoneuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119(94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87(88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eleiomyoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82(65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eendometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125(99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96(98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eendometrialdisease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123(97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebenignlesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79(62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: a. BMI refers to body mass index, calculation formula: BMI(kg/m2)\u0026thinsp;=\u0026thinsp;weight(kg)/(height\u0026times;height(m2)), group: underweight\u0026thinsp;\u0026lt;\u0026thinsp;18.5, normal:18.5\u0026ndash;23.9, overweight:24-27.9, obesity:\u0026ge;28; b. Data were expressed as (s) or n (%) or M (P25, P75), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistically significant; nonnorm refers to data being non-normally distributed.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation of clinical factors on endometrial lesions\u003c/h2\u003e \u003cp\u003eThe analysis revealed no statistically significant differences between the groups for age, age at diagnosis, presence of metastasis, hypertension, diabetes mellitus, history of endometriosis, benign breast lesions, age at menarche, number of pregnancies, number of deliveries, smoking, hyperlipidemia, history of breastfeeding, family history of cancer, pathological type of breast cancer, height, weight, BMI, hormone use, history of uterine fibroids, and previous benign breast lesions (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, significant differences were found between the groups for ultrasound characteristics, duration of TAM therapy, presence of colporrhagia, previous endometrial lesions, and endometrial thickness (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The case group showed significantly higher rates of colporrhagia, greater endometrial thickness, and longer duration of TAM therapy compared to the control group.\u003c/p\u003e \u003cp\u003eThe mean endometrial thickness was greater in the case group (1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 cm) compared to the control group (0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 cm, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a higher proportion of colporrhagia symptoms was observed in the case group (44.9% vs. 23.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The area under the ROC curves (AUC) for each factor was as follows: ultrasound characteristics (0.770), endometrial thickness (0.749), duration of TAM (0.608), colporrhagia (0.605), and history of leiomyoma (0.560). The ROC curve for endometrial thickness had the largest Youden index with a cut-off value of 0.438, a sensitivity of 71.4%, and a specificity of 72.4%, corresponding to an optimal ultrasonographic diagnostic threshold of 0.825 cm for abnormal endometrium. Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"4 Machine Learning","content":"\u003cp\u003eThe heat map of correlation revealed many linear correlations between variables(Figs. \u003cspan\u003e2\u003c/span\u003e). To minimize interference, clinical factors were included as predictors input machine learning model.\u003c/p\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e4.1 LASSO Regression with Logistic Regression Algorithm\u003c/h2\u003e\n \u003cp\u003eIn the LASSO regression to select the most predictive features. A 10-fold cross-validation was performed, resulting in the selection of four variables as independent predictors, with endometrial lesions occurrence as the dependent variable. A multifactorial binary logistic regression analysis showed that previous endometrial lesions was not statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, ultrasound characteristics, duration of TAM therapy, presence of colporrhagia symptoms, and endometrial thickness were identified as independent risk factors for endometrial lesions (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These factors were included in the multifactorial logistic regression model. The dataset was split into a training set (n\u0026thinsp;=\u0026thinsp;190) and a validation set (n\u0026thinsp;=\u0026thinsp;34) in an 8.5:1.5 ratio. After 1,000 Bootstrap self-samplings for internal validation, the results showed a C-index of 0.874, indicating excellent model discrimination. The model demonstrated an accuracy of 0.853, a precision of 0.917, with a training set AUC of 0.874 (95% CI: 0.794\u0026ndash;0.831), and a test set AUC of 0.891 (95% CI: 0.777-1.000). Detailed results are shown in Figs. \u003cspan\u003e3\u003c/span\u003e, \u003cspan\u003e6\u003c/span\u003ea, and Table \u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultifactorial Logistic Regression Analysis of Independent Risk Factors Based on Lasso Regression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWald Z\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecolporrhagia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.746\u0026ndash;9.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndometrial thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.002\u0026ndash;7.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eduration of tamoxifen therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-5years:\u003c/p\u003e\n \u003cp\u003eWithin 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.474\u0026ndash;4.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than 5 years:\u003c/p\u003e\n \u003cp\u003eWithin 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.104\u0026ndash;6.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUltrasonic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUneven echo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.897\u0026ndash;10.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUterine cavity occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.908\u0026ndash;137.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndometrium heterogeneity combined with uterine cavity occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1328.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.733E-16-4.726E\u0026thinsp;+\u0026thinsp;21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: OR: ratio of ratios; CI: confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e4.2 Decision Tree Algorithm\u003c/h2\u003e\n \u003cp\u003eA prediction model for endometrial lesions was constructed using a decision tree. The decision tree was built through recursive partitioning and pruned to avoid overfitting, with the cp value adjusted to 0.036 to improve the model\u0026apos;s generalization ability. The average accuracy of the training set was 0.800, with an AUC of 0.888 (95% CI: 0.840\u0026ndash;0.937). The average accuracy of the test set was 0.740 (95% CI: 0.533\u0026ndash;0.947), with an AUC of 0.800 (95% CI: 0.640\u0026ndash;0.960), demonstrating good prediction performance. Detailed result are illustrated in Figs. \u003cspan\u003e4\u003c/span\u003e,\u003cspan\u003e6\u003c/span\u003eb.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e4.3 Random Forest Algorithm\u003c/h2\u003e\n \u003cp\u003eA prediction model for endometrial lesions was also constructed using a random forest approach. Five hundred trees were established, and as the number of trees increased, the out-of-bag (OOB) error rate decreased and eventually stabilized, indicating a relatively stable model. Factors were ranked in order of importance, with ultrasound characteristics, endometrial thickness, duration of TAM therapy, and colporrhagia symptoms being the most influential. The OOB error rate for the random forest training set was approximately 23.68%, with an average accuracy of 100% (95% CI: 0.981-1.000), and an AUC of 1.000 (95% CI: 1.000\u0026ndash;1.000). The test set had an average accuracy of 0.735 (95% CI: 0.556\u0026ndash;0.871), with an AUC of 0.784 (95% CI: 0.632\u0026ndash;0.867), indicating good prediction performance. Detailed results are shown in Figs. \u003cspan\u003e5\u003c/span\u003e, \u003cspan\u003e6\u003c/span\u003ec.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5 Model comparison and visualization","content":"\u003cp\u003eA comparison of the accuracy and AUC of the models determined that the LASSO regression combined with multifactorial logistic regression (LR) was the optimal model (see Fig. \u003cspan\u003e6\u003c/span\u003e, Table \u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparison of models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emodel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eaccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003especificity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecision tree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eConsequently, a nomogram graph model was constructed based on this combination for predicting the occurrence of endometrial lesions (see Figs. \u003cspan\u003e7\u003c/span\u003e).For example, a female patient with more than five years of TAM therapy, colporrhagia, and an endometrial thickness of 1.6 cm would have corresponding scores of approximately 22.5, 17.5, and 60, respectively, for a total score of 100. This total score corresponds to an estimated probability of developing endometrial lesions of 80%. The application of the nomogram graph provides a clear and concise explanation of the model\u0026apos;s individualized prediction for the patient.\u003c/p\u003e\n\u003cp\u003eThe calibration curve suggests that the mean absolute error between predicted and actual values is 0.014, indicating that the predicted risk closely aligns with the actual risk. The Decision Curve Analysis (DCA) curve evaluates the predictive model and the concordance diagnostic test, calculating the clinical \u0026quot;net benefit\u0026quot; of the predictive model. The results show that using a column chart for predictions is valuable within a threshold probability range of 5\u0026ndash;90% (see Figs. \u003cspan\u003e8\u003c/span\u003e, \u003cspan\u003e9\u003c/span\u003e).\u003c/p\u003e"},{"header":"6 Discussion","content":"\u003cp\u003eIn this study, we developed and evaluated three machine learning prediction models to accurately predict the risk of endometrial lesions in premenopausal breast cancer patients undergoing TAM therapy. The best predictive performance was achieved by LASSO regression combined with logistic regression, which demonstrated an accuracy of 0.853 and precision of 0.917 using four easily accessible patient features. This model had high diagnostic performance, with an AUC of 0.891 (95% CI: 0.777-1.000). The findings confirm that ultrasonographic features, duration of TAM administration, endometrial thickness, and colporrhagia symptoms are clear predictors of endometrial lesions.\u003c/p\u003e \u003cp\u003eA national retrospective study of 102 breast cancer patients treated with TAM postoperatively found that the duration of TAM use and symptoms of abnormal colporrhagia were significant risk factors for developing endometrial lesions, consistent with our study. Additionally, a large body of epidemiologic evidence suggests that TAM is associated with an increased risk of endometrial lesions, with the risk of developing endometrial carcinoma (EC) being 1.5\u0026ndash;6.9 times higher in a dose- and time-dependent manner \u003csup\u003e10\u003c/sup\u003e. The ATLAS study found that patients using TAM for 10 years had a higher cumulative risk of endometrial cancer compared to those using it for 5 years\u003csup\u003e4\u003c/sup\u003e. However, only 10% of the patients in the ATLAS study were premenopausal, which may limit the generalizability of these findings.\u003c/p\u003e \u003cp\u003eOur study showed that the duration of TAM was an independent risk factor for developing endometrial lesions, aligning with previous studies\u003csup\u003e11\u003c/sup\u003e. However, the cumulative dose of the drug was not clarified. Choi et al. demonstrated that benign endometrial disease incidence was highest in subjects under 40 years of age treated with TAM, significantly increasing the risk of endometrial cancer\u003csup\u003e12\u003c/sup\u003e. Similarly, Liu et al. found that the standardized incidence of endometrial cancer was elevated in breast cancer patients diagnosed after the age of 40\u003csup\u003e13\u003c/sup\u003e. Younger patients treated with TAM have a higher risk of subsequent endometrial cancer, particularly those aged 40-49\u003csup\u003e14\u003c/sup\u003e. Bergman\u0026rsquo;s study also indicated that endometrial cancers caused by TAM were more malignant and aggressive\u003csup\u003e11\u003c/sup\u003e. Some studies have shown no correlation between TAM and endometrial lesions. For instance, Takashima\u003csup\u003e15\u003c/sup\u003e found no significant association between shorter TAM therapy duration and endometrial lesions. Chiofalo and Chu also reported no correlation between TAM and endometrial cancer development \u003csup\u003e141617\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, ultrasound characteristics were the most important factor in predicting endometrial lesions, consistent with previous studies. Ultrasound is the preferred monitoring tool, and abnormal occupancy or heterogeneous endometrial echogenicity on ultrasound increases the likelihood of developing endometrial lesions and necessitating endometrial biopsy. Previous NSABP studies, which included mainly postmenopausal women, suggested no additional monitoring for asymptomatic women to avoid unnecessary invasive procedures. However, this may underestimate the risk in premenopausal patients \u003csup\u003e1819\u003c/sup\u003e. Young breast cancer patients undergoing prolonged TAM therapy require more attention. Endometrial screening and evaluation should be performed before TAM treatment, with regular transvaginal ultrasound monitoring to detect and manage endometrial lesions early.\u003c/p\u003e \u003cp\u003eEndometrial thickness was also a significant factor in endometrial lesion occurrence, with the optimal diagnostic threshold being 0.825 cm, similar to previous findings by Zhouqi and Burkart \u003csup\u003e220\u003c/sup\u003e. Since TAM stimulates endometrial gland hypertrophy, leading to pharmacological thickening, establishing a TAM-related endometrial thickness threshold in young breast cancer patients is challenging.\u003c/p\u003e \u003cp\u003eColporrhagia was identified as an important risk factor. Patients with colporrhagia are more likely to develop endometrial lesions, and this symptom serves as a warning for early hospital visits, improving detection rates. However, Maria et al. found no difference in abnormal colporrhagia between the case group and patients with normal endometrium, highlighting the need for further research \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study demonstrates that machine learning approaches can achieve high accuracy in predicting endometrial lesions. Most current clinical prediction models rely on linear relationships between variables, often resulting in poor predictive ability. Machine learning applications in medicine are becoming widespread, offering effective tools for clinical diagnosis and prediction. Our study visualized and predicted endometrial lesions incidence using machine learning, providing valuable insights for gynecologists evaluating premenopausal breast cancer patients during endocrine therapy. The LASSO regression combined with multifactorial logistic regression prevented overfitting, with validation results showing an average absolute error of 0.014 between predicted and actual values, suggesting clinical diagnostic significance for endometrial lesion prognosis.\u003c/p\u003e \u003cp\u003eHowever, this study has limitations. It was a single-center retrospective study with limited data collection and a small sample size, which could introduce selection and recall biases. More objective indicators and larger sample sizes are needed to clarify endometrial thickness criteria. The model also lacks external validation. Future studies should incorporate comprehensive factors and utilize joint neural network prediction models to provide a basis for individualized endocrine therapy treatment in premenopausal breast cancer patients.\u003c/p\u003e \u003cp\u003eIn conclusion, our study identified ultrasound characteristics, TAM duration, colporrhagia, and endometrial thickness as independent risk factors for endometrial lesions in premenopausal breast cancer patients. We developed a predictive risk model using machine learning, with LASSO regression combined with multifactorial logistic regression showing the best performance. Regular monitoring of these factors can aid in early detection and reduction of endometrial lesions, providing a basis for evaluating endocrine therapy, endometrial monitoring during treatment, and individualized therapeutic strategies for breast cancer patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTAM \u0026nbsp; Tamoxifen\u003c/p\u003e\n\u003cp\u003eEC \u0026nbsp; endometrial carcinoma\u003c/p\u003e\n\u003cp\u003eIARC \u0026nbsp; the World Health Organization\u0026rsquo;s International Agency for Research on Cancer\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; body mass index\u003c/p\u003e\n\u003cp\u003eLASSO \u0026nbsp; Least Absolute Shrinkage and Selection Operator\u003c/p\u003e\n\u003cp\u003eROC curve \u0026nbsp; Reiver Operating Characteristic Curve\u003c/p\u003e\n\u003cp\u003eC-index \u0026nbsp; the Consistency index\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; decision curve analysis\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAUC\u003c/em\u003e\u0026nbsp; \u0026nbsp;Area Under Curve\u003c/p\u003e\n\u003cp\u003eOOB error \u0026nbsp; out-of-bag error rate\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Mianyang Central Hospital (No.\u003c/p\u003e\n\u003cp\u003eS20240332-01). Due to the retrospective nature of the study, IRB waived the need of obtaining informed consent.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eAll authors were involved in writing, all authors were involved in developing the methodology employed in the project, reviewing and editing the final draft.\u003c/p\u003e\n\u003cp\u003eConflict of interest statement\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003eFunding information\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eAll data are fully available without restriction. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 2024;74(1):12-49. doi:10.3322/caac.21820\u003c/li\u003e\n\u003cli\u003eZHOU Qi, ZHANG Shiqian, WANG Xiaohong, et al. Guidelines for the management of endometriosis associated with adjuvant endocrine therapy for breast cancer (2021 edition)[J]. Chinese Journal of Practical Gynaology and Obstetrics, 2021, 37(8): 815-820. doi:10.19538/j.fk2021080108\u003c/li\u003e\n\u003cli\u003eGhanavati M, Khorshidi Y, Shadnoush M, et al. 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Interpretation of the Chinese expert consensus on the diagnosis and treatment of endometrial polyps (2022 edition)[J]. Journal of Obstetrics and Gynaology, 2023, 39(1): 29-33.\u003c/li\u003e\n\u003cli\u003ePark C, Heo JH, Mehta S, Han S, Spencer JC. Adherence to Adjuvant Endocrine Therapy and Survival Among Older Women with Early-Stage Hormone Receptor-Positive Breast Cancer. \u003cem\u003eClin Drug Investig\u003c/em\u003e. 2023;43(3):167-176. doi:10.1007/s40261-023-01247-w\u003c/li\u003e\n\u003cli\u003eCollins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. \u003cem\u003eBMJ\u003c/em\u003e. 2015;350:g7594. doi:10.1136/bmj.g7594\u003c/li\u003e\n\u003cli\u003eWijayabahu AT, Egan KM, Yaghjyan L. Uterine cancer in breast cancer survivors: a systematic review. \u003cem\u003eBreast Cancer Res Treat\u003c/em\u003e. 2020;180(1):1-19. doi:10.1007/s10549-019-05516-1\u003c/li\u003e\n\u003cli\u003eBergman L, Beelen ML, Gallee MP, Hollema H, Benraadt J, van Leeuwen FE. Risk and prognosis of endometrial cancer after tamoxifen for breast cancer. Comprehensive Cancer Centres\u0026rsquo; ALERT Group. Assessment of Liver and Endometrial cancer Risk following Tamoxifen. \u003cem\u003eLancet\u003c/em\u003e. 2000;356(9233):881-887. doi:10.1016/s0140-6736(00)02677-5\u003c/li\u003e\n\u003cli\u003eChoi S, Lee YJ, Jeong JH, et al. Risk of Endometrial Cancer and Frequencies of Invasive Endometrial Procedures in Young Breast Cancer Survivors Treated With Tamoxifen: A Nationwide Study. \u003cem\u003eFront Oncol\u003c/em\u003e. 2021;11:636378. doi:10.3389/fonc.2021.636378\u003c/li\u003e\n\u003cli\u003eLiu J, Jiang W, Mao K, et al. Elevated risks of subsequent endometrial cancer development among breast cancer survivors with different hormone receptor status: a SEER analysis. \u003cem\u003eBreast Cancer Res Treat\u003c/em\u003e. 2015;150(2):439-445. doi:10.1007/s10549-015-3315-5\u003c/li\u003e\n\u003cli\u003eChu SC, Hsieh CJ, Wang TF, Hong MK, Chu TY. Younger tamoxifen-treated breast cancer patients also had higher risk of endometrial cancer and the risk could be reduced by sequenced aromatase inhibitor use: A population-based study in Taiwan. \u003cem\u003eCi Ji Yi Xue Za Zhi\u003c/em\u003e. 2020;32(2):175-180. doi:10.4103/tcmj.tcmj_17_19\u003c/li\u003e\n\u003cli\u003eMatsuyama Y, Tominaga T, Nomura Y, et al. Second cancers after adjuvant tamoxifen therapy for breast cancer in Japan. \u003cem\u003eAnn Oncol\u003c/em\u003e. 2000;11(12):1537-1543. doi:10.1093/oxfordjournals.annonc.a010406\u003c/li\u003e\n\u003cli\u003eChiofalo B, Mazzon I, Di Angelo Antonio S, et al. Hysteroscopic Evaluation of Endometrial Changes in Breast Cancer Women with or without Hormone Therapies: Results from a Large Multicenter Cohort Study. \u003cem\u003eJ Minim Invasive Gynecol\u003c/em\u003e. 2020;27(4):832-839. doi:10.1016/j.jmig.2019.08.007\u003c/li\u003e\n\u003cli\u003eAlZaabi A, AlAmri H, ALAjmi G, et al. Endometrial Surveillance in Tamoxifen and Letrozole Treated Breast Cancer Patients. \u003cem\u003eCureus\u003c/em\u003e. 2021;13(11):e20030. doi:10.7759/cureus.20030\u003c/li\u003e\n\u003cli\u003eFisher B, Costantino JP, Redmond CK, Fisher ER, Wickerham DL, Cronin WM. Endometrial cancer in tamoxifen-treated breast cancer patients: findings from the National Surgical Adjuvant Breast and Bowel Project (NSABP) B-14. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e. 1994;86(7):527-537. doi:10.1093/jnci/86.7.527\u003c/li\u003e\n\u003cli\u003eFisher B, Costantino JP, Wickerham DL, et al. Tamoxifen for prevention of breast cancer: report of the National Surgical Adjuvant Breast and Bowel Project P-1 Study. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e. 1998;90(18):1371-1388. doi:10.1093/jnci/90.18.1371\u003c/li\u003e\n\u003cli\u003eBurkart C, Wight E, P\u0026oacute;k J, et al. [Ultrasound endometrium follow-up during tamoxifen treatment: Really not reliable or useful after all?]. \u003cem\u003eUltraschall Med\u003c/em\u003e. 2001;22(3):136-142. doi:10.1055/s-2001-15243\u003c/li\u003e\n\u003cli\u003eJeon J, Kim SE, Lee DY, Choi D. Factors associated with endometrial pathology during tamoxifen therapy in women with breast cancer: a retrospective analysis of 821 biopsies. \u003cem\u003eBreast Cancer Res Treat\u003c/em\u003e. 2020;179(1):125-130. doi:10.1007/s10549-019-05448-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, tamoxifen, endometrial lesions, nomogram, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-4715381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4715381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEndometrial lesions is a frequent complication following breast cancer, and current diagnostic tools have limitations. This study aims to develop a machine learning-based nomogram graph prediction model for the early detection of endometrial lesions in patients. The model is intended to provide risk assessment and facilitate personalized treatment strategies for premenopausal breast cancer patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA retrospective study was conducted on 224 patients who underwent diagnostic curettage post-tamoxifen (TAM) therapy between November 2012 and November 2023. These patients exhibited signs of endometrial abnormalities or symptoms such as colporrhagia. Clinical data were collected and analyzed using R software version 4.3.2 to identify factors influencing the occurrence of endometrial lesions and to evaluate their predictive values. Three machine learning methods were employed to develop a risk prediction model, and the performances of these models were compared. The best-performing model was selected to construct a nomogram of endometrial lesions. Internal validation was conducted using the bootstrap method, and the model\u0026rsquo;s accuracy and fit were assessed using the concordance index (C-index) and calibration curves.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIndependent risk factors for endometrial lesions included ultrasound characteristics, duration of TAM therapy, presence of colporrhagia, and endometrial thickness (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among the machine learning methods compared, the LASSO regression integrated with a multifactorial logistic regression model demonstrated strong performance, with a concordance index (C-index) of 0.874 and effective calibration (mean absolute error of conformity: 0.014). This model achieved an accuracy of 0.853 and a precision of 0.917, with a training set AUC of 0.874 (95% CI: 0.794\u0026ndash;0.831) and a test set AUC of 0.891 (95% CI: 0.777-1.000), closely matching the predicted risk to the actual observed risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe developed prediction model effectively assesses the likelihood of endometrial lesions in premenopausal breast cancer patients. This model offers a theoretical foundation for improving clinical predictions and devising tailored treatment strategies for this patient group.\u003c/p\u003e","manuscriptTitle":"Machine Learning-Based Nomogram for Predicting Endometrial Lesions after Tamoxifen Therapy in Breast Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-13 02:10:37","doi":"10.21203/rs.3.rs-4715381/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-24T16:11:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-12T10:13:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-12T10:11:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4573135920938180849636374651032855598","date":"2024-08-12T10:11:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172359334416680463645980584081694785688","date":"2024-08-12T10:08:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-12T10:05:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-12T08:48:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-16T02:18:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-13T04:20:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-10T04:02:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b16749f4-1cdd-4fe7-8571-7fac7c50173b","owner":[],"postedDate":"August 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T16:06:17+00:00","versionOfRecord":{"articleIdentity":"rs-4715381","link":"https://doi.org/10.1038/s41598-024-82373-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-06 15:57:28","publishedOnDateReadable":"January 6th, 2025"},"versionCreatedAt":"2024-08-13 02:10:37","video":"","vorDoi":"10.1038/s41598-024-82373-z","vorDoiUrl":"https://doi.org/10.1038/s41598-024-82373-z","workflowStages":[]},"version":"v1","identity":"rs-4715381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4715381","identity":"rs-4715381","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}