A nomogram for predicting high-risk endometrial cancer based on the transvaginal ultrasonography and contrast-enhanced ultrasonography

Introduction

Endometrial cancer (EC) is a prevalent gynecological malignancy that imposes a substantial burden on female health in China (1). Over the past three decades, the overall incidence of EC has risen by 132%, while the associated mortality has shown an average annual increment of 1.9% (2,3). Owing to misdiagnosis or delayed diagnosis, 15–20% patients present with high-risk pathological features such as deep myometrial invasion and lymph node metastasis at diagnosis (4). These high-risk patients typically confront more severe survival challenges, with post-treatment recurrence rates of as high as 50% (5-7). Therefore, the early identification of high-risk EC is crucial for precise prevention and improving patient outcomes.

Currently, endometrial biopsy, transvaginal ultrasonography (TVUS), and imaging techniques constitute the principal diagnostic approaches for EC. Pathological biopsy serves as the diagnostic “gold standard”, enabling direct identification of cancer cell type and invasion degree (8). However, this invasive procedure exposes patients to discomfort and infection risks (9,10). TVUS offers direct visualization of endometrial morphology and internal echoes, making it the preferred initial screening modality (11,12). But the limited specificity may result in misdiagnosis or missed diagnosis at early-stage (13,14). Traditional imaging examinations including magnetic resonance imaging (MRI) and computed tomography (CT) can provide multiperspective assessments for tumor invasion (15,16). However, the elevated costs and radiation exposure may restrict their utility in long-time follow-up.

Contrast-enhanced ultrasonography (CEUS) is an advanced imaging technique, which has gained widespread applications in tumor assessment in recent years. Since the energy of enhanced ultrasound is extremely low, it does not produce ionizing radiation. In addition, CEUS is a preferable choice for patients in whom MRI is contraindicated secondary to metallic implants. Compared with traditional TVUS, CEUS can clearly display tumor boundaries, microvascular distribution, and characteristics of blood perfusion (17-19). Thus, CEUS holds significant potential for early diagnosis and disease assessment in EC, particularly when TVUS findings are inconclusive. However, CEUS remains operator-dependent, with diagnostic accuracy significantly influenced by sonographer expertise and imaging acquisition protocols. Furthermore, microscopic lesions may evade detection due to CEUS’s limited spatial resolution. These limitations underscore the necessity of integrating TVUS and CEUS: TVUS provides high-resolution anatomical mapping, while CEUS adds hemodynamic characterization. Nevertheless, the combined diagnostic value of TVUS and CEUS for high-risk EC remains inadequately explored.

This study aims to construct and validate a diagnostic nomogram for high-risk EC by integrating parameters from both TVUS and CEUS. The findings are expected to guide therapeutic decision-making in gynecologic oncology. We present this article in accordance with the TRIPOD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2344/rc).

Methods

Study population

Female patients from Beijing Shijitan Hospital meeting the following criteria were included in this study: pathologically biopsy-proven EC; having undergone radical resection surgery; availability of complete TVUS and CEUS diagnostic information. Patients meeting the following criteria were excluded: receipt of radiotherapy or chemotherapy prior to surgery; incomplete diagnostic information from TVUS and CEUS; having undergone hysteroscopy or diagnostic curettage prior to ultrasound examination; exceeding a 2-week interval between ultrasound examination and surgery. This study obtained approval from the Ethics Committee of Beijing Shijitan Hospital (approval No. IIT2024-019-002). All participants provided written informed consent. The study protocol and procedures were conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Distinguishing of high-risk EC

Pathological staging was performed according to the International Federation of Gynecology and Obstetrics (FIGO) 2009 staging system for EC (20). The distinction between low-risk and high-risk EC was based on the combination of FIGO stage, histological grade, and histological subtype. Low-risk EC was defined as tumors confined to the uterus (FIGO stage IA) with endometrioid histology and grade G1–G2, without deep myometrial invasion (<50%) or lymphovascular space invasion (LVSI). High-risk EC was defined as tumors with FIGO stage IB–IV, grade G3 endometrioid adenocarcinoma, or any non-endometrioid carcinoma (including serous, clear cell, carcinosarcoma, etc.), regardless of stage. Deep myometrial invasion was defined as tumor infiltration ≥50% of the myometrial thickness, measured on postoperative pathological specimens. Deep myometrial invasion was defined as tumor infiltration exceeding 50% of the myometrial thickness, measured on postoperative pathological specimens. The measurement was performed by experienced pathologists, with the depth of invasion expressed as a percentage of the total myometrial thickness.

Variables

This study systematically collected patient information, TVUS parameters, and CEUS parameters. Patient information included age, lymphatic vessel space infiltration (LVSI, yes/no), abnormal uterine bleeding (AUB, yes/no), and menopausal status (premenopausal/postmenopausal). TVUS parameters included tumor echogenicity (TE, hypoechoic/isoechoic and hyperechoic), endometrial-myometrial border (EMB, regular/irregular), color score (CS, stage 1–2/2–3), perfusion mode (PM, diffuse/focal), vessel morphology (VM, normal/abnormal), endometrial thickness (ET), longitudinal diameter of the uterus (UTL), uterine width (UTW), tumor length (TL), tumor anterior-posterior diameter (TAP), and blood flow resistance index (RI). CEUS parameters included CEUS diagnostic accuracy (consistent/inconsistent with pathology), wash in (early/synchronous/late), enhancement degree (ED, high/iso-enhancement/low), wash out (early/synchronous/late), ascent time (AT), time to peak (TTP), peak intensity (PI), ascent slope (AS), and area under the curve (AUC). Wash-in refers to the time interval from the initiation of contrast agent injection to the arrival of contrast agent in the endometrial lesion. Wash-out refers to the time interval from the peak enhancement to the clearance of contrast agent from the lesion. The measurement of ET was performed using sagittal scanning, selecting the plane of maximum ET, covering both bilateral glands and stromal tissue of the endometrium. The measurement range required continuous observation of the complete endometrial canal from the internal os of the cervical canal to the top of the uterine cavity to ensure no omission of the measurement area.

All image acquisition and parameter measurement for TVUS and CEUS were performed by Ultrasound Physician with extensive experience in gynecological ultrasound diagnosis. The entire operation strictly followed the standardized procedures of the International Endometrial Tumor Analysis (IETA) consensus. During TVUS examination, morphological parameters such as TE and EMB, as well as hemodynamic parameters such as CS and RI, were recorded. Each parameter was measured repeatedly three times, and the average value was used for subsequent analysis. For CEUS examination, dynamic scanning of the sagittal and transverse sections of the uterus was first conducted to identify the endometrial lesion area. The image was then magnified to clearly display the region of interest. After that, the imaging mode was activated and the storage button was pressed; simultaneously, 2.0–2.4 mL of contrast agent was administered via peripheral vein bolus injection. The entire dynamic process, from the initiation of contrast agent perfusion to its washout, was recorded for a duration of more than 90 seconds.

Statistical analysis

Categorical variables were expressed as frequencies and percentages (%), using the chi-square test for intergroup comparisons. Continuous variables that followed a normal distribution were expressed as the mean ± standard deviation (SD), using the independent samples t-test for intergroup comparisons. Continuous variables that did not follow a normal distribution were expressed as medians (interquartile), using the Mann-Whitney U test for intergroup comparisons. Variables were initially selected via univariate analysis, followed by collinearity assessment retaining those with variance inflation factors (VIF). The final feature set was determined using least absolute shrinkage and selection operator (LASSO) regression. A nomogram predictive model was developed based on the selected features. The performance of the nomogram-based prediction model was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and internal validation methods. A two-sided P value <0.05 served as the prespecified threshold for statistical significance, with no adjustment for multiple comparisons unless otherwise stated.

Results

Basic characteristics of the study population

This study included a total of 128 patients with EC, of whom 62 were classified as high-risk cases. As shown in Table 1, significant differences were observed between the two groups in multiple clinical and imaging parameters. The TL was significantly longer in the high-risk group than in the low-risk group (Figure 1). Compared with the low-risk cohort, the proportion of postmenopausal cases, hypoechoic, early wash in, high ED, and early wash out were higher in the high-risk EC group (all P<0.05). The diagnostic accuracy of TVUS, the ET, UTL, TL, TAP, AT, TTP, PI, AS, and AUC were also higher in the high-risk EC patients (all P<0.05). In addition, high-risk EC patients exhibited lower LVSI positivity rates, more irregular EMB, higher CS (richer blood flow), more focal perfusion mode (PM), and more abnormal VM (all P<0.05) (Figure 2).

Figure 1 Differences in tumor length between high-risk EC and low-risk EC. EC, endometrial cancer; TL, tumor length.

Figure 2 Typical ultrasonic manifestations of high- and low-risk endometrial carcinoma. (A) Transvaginal ultrasound: a large lesion is observed in the uterine cavity, with ill-defined boundary between the lesion and the posterior uterine wall. (B) Color Doppler flow imaging: the lesion presents a high color score with abundant blood flow signals. (C) CEUS: the mass shows overall hyperenhancement. (D) CEUS: the contrast agent in the lesion washes out rapidly, earlier than that in the myometrium. (E) Transvaginal ultrasound: the lesion of low-risk endometrial carcinoma manifests as hyperechoic with clear demarcation from the uterine myometrium. (F) Color Doppler flow imaging: regular strip-like blood flow signals are detected inside the lesion, with a low color score. (G) CEUS: the mass exhibits hyperenhancement. (H) CEUS: the contrast agent in the lesion washes out synchronously with the normal uterine myometrium. CEUS, contrast-enhanced ultrasonography.

Univariate logistic regression analysis

All the baseline variables were entered into a univariate logistic regression analysis. The results revealed that 18 variables were significantly associated with high-risk EC (Table 2). Among these, TE, wash in, ED, wash out, AT, and TTP were identified as protective factors for high-risk EC (P<0.05). Menopausal status, EMB, CS stage, PM, VM, ET, UTL, TL, TAP, PI, AS, and AUC were identified as risk factors for high-risk EC (P<0.05).

Predictor screening

Multicollinearity assessment (VIF threshold: 5.0) identified 6 variables with severe collinearity (Table 3), notably TTP (VIF =32.7), AT (VIF =17.1) and AS (VIF =16.2). LASSO regression at optimal λ.1se [log(λ) =−2.6979] further reduced predictors to 6 non-zero coefficients, including AUC, ED, PM, EMB, TAP and menopause (Figure 3). These six variables were incorporated into the final nomogram predictive model.

Figure 3 LASSO regression of 12 related predictors. (A) Distribution of LASSO coefficients for 12 related predictors. (B) Partial likelihood bias of the LASSO coefficient distribution. The vertical dashed line indicates the minimum partial likelihood deviation plus one standard error. AUC, area under the curve; ED, enhancement degree; EMB, endometrial-myometrial border; ET, endometrial thickness; LASSO, least absolute shrinkage and selection operator; PM, perfusion mode; TAP, tumor anterior-posterior diameter; TE, tumor echogenicity; TL, tumor length; UTL, longitudinal diameter of the uterus.

Predictive performance of the nomogram model

A nomogram was developed based on these six independent predictors, ranked by their relative importance (Figure 4). The total score is derived from the summation of the corresponding point values assigned by the nomogram for each covariate. The model demonstrated excellent discriminative ability, with an AUC of 0.9213 in ROC analysis (Figure 5A). DCA revealed that within the threshold probability range of 0.1–0.5, this nomogram provided a significantly greater clinical net benefit compared to alternative diagnostic approaches (Figure 5B). Internal validation indicated that the calibration curve closely approximated the ideal curve, reflecting the good stability of model (Figure 5C).

Figure 4 A nomogram for predicting high-risk EC. A postmenopausal patient exhibited a regular EMB, an ED value of 1, a PM value of 1, of TAP value of 2.3, a status of after menopause, and a CEUS AUC value of 931.8. The vertical red line was projected upward from each covariate axis to determine its corresponding point value. The summed point total [111] was then positioned on the total points axis. A vertical line was drawn downward from this total to intersect the predicted-probability scale, yielding an individual risk of 19.6% for high-risk EC. *, P<0.05; **, P<0.01; ***, P<0.001. ED: 1= high enhancement, 2= iso-enhancement, 3= low enhancement; PM: 1= diffuse, 1.6= mixed pattern, 2= focal. AUC, area under the curve; CEUS, contrast-enhanced ultrasonography; EC, endometrial cancer; ED, enhancement degree; EMB, endometrial-myometrial border; PM, perfusion mode; TAP, tumor anterior-posterior diameter.

Figure 5 Performance analysis of the nomogram model. (A) ROC analysis. (B) DCA for different diagnostic methods. (C) Internal validation analysis. AUC, area under the curve; DCA, decision curve analysis; EMB, endometrial-myometrial border; ROC, receiver operating characteristic; TE, tumor echogenicity.

Discussion

This study combined multidimensional parameters from TVUS and CEUS examinations to establish a nomogram model predicting high-risk EC. The findings indicated that CEUS-derived AUC, ED, PM, EMB, TAP, and menopausal status were significantly associated with the diagnosis of high-risk EC. The nomogram achieved an AUC of 0.9213 and conferred substantial clinical net benefit.

Low-risk EC is more prevalent in perimenopausal women, closely associating with long-term estrogen exposure (21). While high-risk EC predominantly affects older women, it shows no direct dependence on estrogen exposure (21). Some high-risk patients are associated with genetic factors such as Lynch syndrome (22). Among the 128 patients enrolled in this study, the proportion of postmenopausal cases was significantly higher in the high-risk EC group. The results of logistic regression analysis showed that menopausal status was significantly associated with the high-risk EC. Consequently, the inclusion of menopausal status in the nomogram model aligns with established clinical evidence.

In addition to imaging parameters, this study also incorporated multiple clinical risk factors for analysis. AUB is the most common clinical manifestation of EC, presenting as the initial symptom in over 90% of patients (23). In this study cohort, baseline analysis showed no significant difference in AUB distribution between low-risk and high-risk groups, which may be attributed to the universal nature of this symptom in confirmed EC patients, limiting its inter-group variability for risk stratification. Nevertheless, as the “first gateway” for disease screening, the clinical value of AUB remains critical—postmenopausal women presenting with AUB face significantly increased EC risk, necessitating timely ultrasound and pathological evaluation. Age is a well-established risk factor for EC. Epidemiological data indicate that EC incidence increases with age, particularly rising significantly after age 50, with global burden studies confirming that risk in postmenopausal women gradually increases with advancing age (24,25). In our study, although the high-risk group had higher mean age than the low-risk group, age was not retained in the final predictive model, likely due to high collinearity with menopausal status—postmenopausal status was retained as an independent predictor, partially accounting for age effects. Diabetes and hypertension, as core components of metabolic syndrome, have been widely confirmed to be associated with EC risk. A study in the Vietnamese population found that diabetes significantly increases the risk of EC in women with postmenopausal bleeding (26). Another study among the Chinese population demonstrated that a body mass index (BMI) ≥25 kg/m² is associated with an elevated risk of EC in postmenopausal women (27). A meta-analysis demonstrated that metabolic syndrome is associated with a significantly increased risk of EC [relative risk (RR): 1.89, 95% confidence interval (CI): 1.34–2.67], with hypertension showing a risk estimate of 1.81 (P=0.024) (28). The underlying mechanisms include hyperinsulinemia from insulin resistance promoting endometrial cell proliferation, increased aromatase activity in adipose tissue creating a hyperestrogenic state, and chronic inflammatory microenvironments associated with hypertension contributing to tumor development. However, our study did not systematically collect medication history and disease duration for these conditions, which may explain their lack of significant contribution to the model. In summary, although our final predictive model primarily incorporated ultrasound parameters and menopausal status, AUB, age, diabetes, and hypertension—as classic clinical risk factors for EC—play fundamental roles in disease screening and risk assessment. Integrating these factors with imaging features holds promise for further enhancing the identification of high-risk populations.

A cross-sectional study demonstrated that ET was effective in differentiating malignant from benign endometrial lesions (29). A cross-sectional study reported that the AUC of ET for predicting EC in women with postmenopausal bleeding was 0.89 (95% CI: 79.5–99.5%), while the AUC values of RI, pulsatility index (PI), and peak systolic velocity (PSV) for the prediction were 0.88 (0.78–0.98), 0.85 (0.74–0.97), and 0.72 (0.58–0.85), respectively (30). In the present cohort, low-risk patients exhibited a mean ET of 1.40 cm, whereas high-risk patients displayed 2.32 cm. Despite this significant inter-group difference, the data-driven shrinkage property of LASSO objectively deselected ET based on its limited contribution to prediction accuracy. This discrepancy is likely attributable to the homogeneous characteristics of our study cohort. As all enrolled patients had pathologically confirmed endometrial carcinoma with ET exceeding the diagnostic threshold of ≥4 mm (31), ET lost inter-group variability critical for risk stratification. Moreover, the possibility of sampling error cannot be excluded as a confounding factor influencing these findings.

The key predictors in the nomogram constructed in this study can be systematically elucidated from three dimensions: angiogenesis, tumor invasion and proliferation, and hormone-dependent characteristics. Firstly, the AUC and ED of CEUS reflect the total perfusion volume of contrast agent and the ED of tumor, respectively, and both jointly account for the abnormal vascular features of high-risk EC (32). High-risk EC induces abnormal neovascularization due to rapid cell proliferation and vigorous metabolism (33). Such newly formed blood vessels not only have high density, but also their endothelial cells are loosely connected owing to excessive rapid formation, enabling ultrasound contrast agents to more easily penetrate the vessel wall and retain in the tumor interstitium, thus resulting in elevated AUC and ED. Secondly, the differences in TVUS-related indicators stem from the invasive and proliferative properties of high-risk EC. High-risk EC cells exhibit high clonal heterogeneity, with dense blood vessels in proliferatively active regions and no perfusion in necrotic areas, so they mostly present focal perfusion (34). The normal EMB is composed of collagen fibers, smooth muscle cells, and extracellular matrix in the basal layer of the endometrium, acting as a natural barrier against tumor invasion. High-risk EC cells possess a stronger invasive phenotype and can secrete matrix metalloproteinases to degrade the collagen and extracellular matrix in the basal layer, impairing the integrity of the border and causing blurred or irregular EMB under TVUS (35). In contrast, low-risk EC has weak invasive ability, and the basal layer remains undamaged, so the EMB stays clear and regular. The increase in TAP is directly associated with the abnormally elevated proliferative activity of high-risk EC cells. In addition, high-risk EC is mostly non-estrogen-dependent and more prone to occur in postmenopausal women. The decline of ovarian function after menopause leads to a reduction in estrogen levels, and the capacity for cell DNA repair diminishes with increasing age, raising the probability of gene mutation accumulation. Meanwhile, metabolic disorders after menopause can further promote tumor cell proliferation. These factors collectively contribute to a significant increase in the risk of high-risk EC in postmenopausal women (36,37).

The nomogram constructed in this study takes TVUS and CEUS as the core technical supports to achieve accurate risk stratification for patients with EC. When TVUS is difficult to differentiate the benign and malignant nature of lesions, or the indications for CEUS examination are uncertain, this nomogram can serve as a supplementary tool. The results of DCA showed that when the threshold probability for predicting high-risk EC was in the range of 10–50%, the clinical net benefit of this nomogram was significantly better than that of a single indicator. This result suggests that within this threshold range, the nomogram can not only effectively identify truly high-risk EC patients and avoid adverse outcomes caused by missed diagnosis, but also reduce excessive invasive interventions for low-risk patients. Previous models mostly relied on single-dimensional indicators, such as those only based on clinical features (age, menopausal status, CA125 level) (38) or solely TVUS morphological parameters (39), and thus struggled to comprehensively capture the complex biological characteristics of high-risk EC. In contrast, this nomogram innovatively integrates CEUS, TVUS, and clinical features, achieving accurate identification of high-risk EC from multiple dimensions, and is more capable of meeting the urgent clinical needs for refined and personalized risk stratification of EC.

In summary, TVUS delivers morphological insights into EC via gray-scale ultrasound and color Doppler imaging. CEUS supplements functional specifics through real-time dynamic blood flow perfusion analysis. By integrating parameters from these two ultrasound examinations, the nomogram model provides a non-invasive and efficient tool for precise preoperative prediction of high-risk EC.

The nomogram developed in this study integrates TVUS and CEUS parameters with menopausal status to provide a non-invasive, preoperative tool for identifying high-risk EC. Its clinical utility can be understood in the context of current management pathways: For patients predicted as high-risk (e.g., with a nomogram score above a predefined threshold), the model supports the need for comprehensive surgical staging, including pelvic and para-aortic lymphadenectomy, and referral to gynecologic oncologists. This aligns with guidelines recommending extensive surgery for high-risk EC to ensure accurate staging and guide adjuvant therapy. For patients predicted as low-risk, the model may help avoid overtreatment, such as unnecessary lymph node dissection, and could support consideration of less invasive surgical approaches (e.g., sentinel lymph node biopsy alone) or fertility-sparing options in selected cases, provided that pathological confirmation is obtained.

The DCA demonstrated that within a threshold probability range of 10–50%, the nomogram provides a higher net clinical benefit than any single parameter or a “treat-all” strategy. This range corresponds to clinically relevant scenarios where the decision to perform extensive surgery or refer to a specialist is most uncertain. By reducing unnecessary interventions in low-risk patients and ensuring timely aggressive management in high-risk patients, this nomogram has the potential to improve patient outcomes and optimize healthcare resource utilization. However, it is important to emphasize that this nomogram is intended as a decision-support tool, not a substitute for pathological diagnosis or clinical judgment. Its integration into clinical workflows should be validated in prospective multicenter studies.

Limitations

Although this study has obtained preliminary findings, it has several limitations. First, the single-center design with a sample size of 128 may limit the external validity of the constructed model. Second, external validation using an independent cohort was not performed, and the established nomogram remains preliminary and not yet suitable for direct clinical application. In addition, ET and Doppler parameters were not included in the nomogram model, which may be attributed to the fact that patients with EC commonly present with diffusely thickened endometrium and low RI values, resulting in numerical but statistically nonsignificant differences between the two groups. Furthermore, the model did not incorporate features of proliferative vascular patterns, and MRI was not routinely used to exclude uterine sarcoma. Moreover, the model did not integrate molecular biological markers or gene expression profiles. Therefore, future studies should expand the sample size, conduct external validation, include more comprehensive ultrasound and MRI indicators, and enroll more clinical subtypes to further verify the generalizability and clinical applicability of the model.

Conclusions

This study represents the first effort to construct a nomogram model for predicting high-risk EC based on ultrasound parameters. By integrating these parameters, the model enables accurate preoperative identification of high-risk EC, offering a novel and effective tool for preoperative risk stratification. Through risk stratification prior to surgery, this nomogram has the potential to guide personalized treatmFent decisions—including the extent of surgical staging and referral patterns—ultimately facilitating risk-appropriate care and improving patient outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2344/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2344/dss

Funding: This study was supported by Beijing Municipal Hospital Scientific Research Cultivation Program (No. PX2023027), and China State Railway Group Science and Technology Research Program (No. J2024Z605).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2344/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Beijing Shijitan Hospital (approval No. IIT2024-019-002). All participants provided written informed consent.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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