Nomogram for predicting cancer-specific mortality risk in endometrial cancer after postoperative radiotherapy
Original Article

Nomogram for predicting cancer-specific mortality risk in endometrial cancer after postoperative radiotherapy

Qingyi Wang1, Ailin Tao2, Yinfeng Tuo2, Jiali Li2, Fanglei Liu2, Zhihui Deng1, Guona Li1

1Shanghai University of Traditional Chinese Medicine, Shanghai, China; 2Department of Gynecology, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China

Contributions: (I) Conception and design: Q Wang, A Tao; (II) Administrative support: Z Deng, G Li; (III) Provision of study materials or patients: Q Wang; (IV) Collection and assembly of data: Q Wang, J Li, F Liu; (V) Data analysis and interpretation: Q Wang, A Tao; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Zhihui Deng, MM; Guona Li, MD. Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai 201203, China. Email: dzh202206@163.com; liguonaaa@163.com.

Background: Endometrial cancer (EC) incidence keeps rising amid population aging and obesity. This study aimed to investigate independent prognostic factors for the overall survival (OS) and cancer-specific survival (CSS) in postmenopausal EC patients treated with surgery and radiotherapy, and to construct a nomogram to predict survival rate.

Methods: We analyzed postmenopausal EC patients who had undergone surgery and radiotherapy between 2010 and 2015, using data from the Surveillance Epidemiology and End Results (SEER) database. The patients were divided into the training and validation cohorts using a ratio of 7:3. Prognostic factors were identified using univariate and multivariate analyses, and a nomogram was developed to predict OS and CSS using Cox proportional hazards and Fine-Gray compete-risk models. We evaluated the nomogram’s performance using C-index, receiver operating characteristic (ROC) curve, and calibration plot, and compared it with American Joint Committee on Cancer (AJCC) staging model to assess the impact of surgical modality, radiotherapy modality, chemotherapy or not, and pathological types on OS in different risk stratifications.

Results: A total of 10,906 EC patients were enrolled in the study and randomly assigned to the training (n=7,633) and validation (n=3,273) cohorts. The competing risk nomogram finalized 13 prognostic variables including age, tissue type, brain metastasis, lung metastasis, marital status, T-stage, N-stage, stage, grade, radiotherapy modality, tumor size, surgical site, and regional lymphadenectomy. The nomogram showed good performance in predicting CSS with C-indexes of 0.800 and 0.755 for the training and validation sets, respectively. The nomogram performed significantly better than AJCC staging model. In addition to the above variables, the Cox proportional hazards model incorporated bone metastasis, chemotherapy, and the total number of in situ/malignant tumors, with the C-index of the training set being 0.753 and 1-, 3-, and 5-year areas under the curve (AUCs) being 0.825, 0.785, and 0.772, respectively. The calibration plots showed good consistency. Subgroup analysis showed that the low-risk and high-risk groups showed that patients receiving radiotherapy had longer survival times compared to those receiving chemoradiotherapy, and patients undergoing brachytherapy had longer survival times compared to those receiving external beam radiotherapy (EBRT).

Conclusions: Our study developed and validated a nomogram for predicting OS and CSS in postmenopausal EC patients treated with surgery and radiotherapy. These findings provide valuable insights for individualized risk assessment and treatment decision.

Keywords: Surveillance Epidemiology and End Results (SEER); overall survival (OS); cancer-specific survival (CSS); endometrial cancer (EC); Fine-Gray

Submitted Nov 28, 2025. Accepted for publication Jan 21, 2026. Published online Mar 24, 2026.

doi: 10.21037/tcr-2025-1-2656

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Key findings

• Based on 10,906 postmenopausal patients with endometrial cancer (EC) who received postoperative radiotherapy from the Surveillance Epidemiology and End Results database, a nomogram model for predicting overall survival (OS) and cancer-specific survival (CSS) was constructed and validated.

What is known and what is new?

• Surgery combined with radiotherapy is the standard adjuvant treatment for EC, but there are controversies regarding the impact of different radiotherapy modalities and whether chemotherapy is combined or not on prognosis; the traditional Cox proportional hazards model does not consider the competing risk of non-cancer deaths, which may overestimate the specific mortality rate of EC.

• This study is the first to target postoperative radiotherapy patients with EC after menopause, and simultaneously constructs a CSS nomogram based on the Fine-Gray competing risk model and an OS nomogram based on the Cox model to achieve individualized risk stratification; it confirms that the competing risk model has a greater predictive advantage for long-term survival diseases; it is found that brachytherapy and simple radiotherapy have survival benefits in this population, providing evidence-based basis for optimizing treatment decisions.

What is the implication, and what should change now?

• This nomogram can integrate multiple clinical and pathological factors, providing a more accurate prediction of patient prognosis compared to a single American Joint Committee on Cancer staging system, and assisting clinicians in conducting individualized risk assessment and treatment decision-making.

• For high-risk patients, more aggressive strategies such as combined brachytherapy can be considered; for low-risk patients, excessive treatment can be avoided; it is recommended to prefer radiotherapy alone rather than blindly combining chemotherapy, especially in elderly or patients with multiple comorbidities who are postmenopausal.

Introduction

As cancer treatment methods continue to diversify and improve (1), the cancer mortality rate is steadily declining, with an estimated 2,001,140 new cancer cases and 611,720 cancer deaths in the United States in 2024 (2). Endometrial cancer (EC) is a group of epithelial malignancies that occur in the endometrium. A total of 420,368 new cases were reported globally in 2022 (3). About 75% of these patients were postmenopausal women with a median age of 61 years at diagnosis (4). In the United States, EC is the most common gynecological malignancy, with an estimated 67,880 new cases in 2024. The current recommended treatment for EC is surgery combined with radiotherapy ± chemotherapy (5-7). The choice of adjuvant therapy after surgery for EC in postmenopausal patients remains controversial (8-11). Numerous studies have shown that surgery combined with radiotherapy in high-risk EC patients improves patient survival (12,13). Some studies also concluded that surgery combined with radiotherapy does not improve 5-year survival and disease-free survival (DFS) is generally defined as the recurrence of EC lesions at any (site >6 months later) in EC patients (10), and 7–15% of patients with early EC and 40% of patients with advanced EC will still have recurrence or metastasis after treatment.

Currently, predictive models are widely used to study the prognostic outcome of tumors. Most studies have mainly used traditional survival analysis methods such as Cox proportional hazards model (14-16). A large proportion of postmenopausal EC patients still die from causes other than EC-related specific deaths, which are difficult to predict and can compete with tumor cancer-specific survival (CSS). This can, to some extent, overestimate the mortality of tumor patients and thus reduce the predictive value of death in EC patients. Based on the presence of competing factors, we used a competing risk model to distinguish tumor CSS from death from other causes. Competing risk models have demonstrated benefits in various studies focused on tumor prognosis. For example, in a study focused on metastatic colorectal cancer (mCRC), Qiu et al. predicted the impact of a history of malignancy on the survival of patients with mCRC through competing risk analysis (17). In the field of prostate cancer, Yang’s team established a prognostic competing risk model for elderly patients with transitional cell bladder carcinoma (18). This indicates that when there is a significant risk of death, competing risk models can provide more accurate prognostic assessments.

EC is one of the few cancers for which survival has not improved significantly since the mid-1970s, reflecting the fact that few advances have been made in the treatment of this cancer. In this regard, it is critical to examine which pathologic indicators effectively predict whether each patient will benefit from surgery combined with radiotherapy, whether radiotherapy plus chemotherapy is superior to radiotherapy alone, and which radiotherapy modality achieves the highest survival rate. Therefore, we constructed a prediction model to predict CSS and overall survival (OS) in postmenopausal EC patients treated with surgery combined with radiotherapy based on the Surveillance Epidemiology and End Results (SEER) database. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2656/rc).

Methods

Patient selection

The data for this article were obtained from the U.S. cancer statistics database—SEER detailing approximately 34% of U.S. demographics, cancer pathology, treatment, and prognosis since 1973. The SEER database has large, comprehensive, and specific information on clinical cases, and this study was based on SEER*Stat 8.4.0.1 software to collect information on patients from 18 registries. Inclusion criteria: patients diagnosed with EC between 2010 and 2015 were included with reference to the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3); EC included the following pathological type classifications: Endometrioid (ICD-0-3 codes 8140, 8210, 8260, 8262, 8380–8383, 8440, 8480–8482, 8560, 8570), Serous (8441, 8450, 8460, 8461), uterine carcinosarcomas (UCSs) (8950, 8951, 8980, 8981), Clear cell (8310, 8313), age at diagnosis ≥50 years, and patients who had undergone surgical treatment and radiotherapy. Exclusion criteria: patients lacking information on race, marital status, tumor-node-metastasis (TNM) stage, metastasis, tumor size, lymphadenectomy, distant metastatic surgery, and follow-up.

Referring to the American Joint Committee on Cancer (AJCC) clinical guidelines and expert opinions, the following 24 predictor variables were selected: basic patient information: age at diagnosis, marital status at diagnosis, and race; tumor-related information: grade, histology, AJCC stage (7th), AJCC T (7th), AJCC N (7th), AJCC M (7th), brain metastasis, liver metastasis, bone metastasis, and lung metastasis, tumor size, first malignant primary indicator, total number of in situ/malignant tumors for patient, sequence number, total number of benign/borderline tumors for patient; treatment information: surgery of primary site, scope regional lymph node surgery, surgery of other regional sites/distant sites, radiation recode, chemotherapy recode, regional nodes positive; and outcome variables: survival months, specific death classification. The outcome events in this study were CSS and OS, with CSS defined as the duration from diagnosis to death from EC and OS defined as the duration from diagnosis to the last follow-up visit, regardless of the cause of death. In the competing risk model, patient death from EC is defined as an event of interest; death from any other cause is defined as a competing event, and if the patient is alive at the end of follow-up, it is defined as a censored event.

Development and validation of OS and CSS nomogram models

Patients extracted from the SEER database were randomly divided into training and validation cohorts in a ratio of 7:3. The training cohort was used to develop nomograms for predicting 1-, 3-, and 5-year OS and CSS probabilities in EC patients. The validation cohort was used to validate the model constructed from the training cohort. Univariate and multivariate stepwise regression analyses using Cox were used to identify influencing factors affecting the prognosis of EC patients, and variables with P<0.05 were identified as independent risk factors associated with OS in EC patients. Nomograms were constructed to predict the 1-, 3-, and 5-year survival of EC. A risk classification system was established based on the total nomogram score of each patient, and patients were divided into two groups according to their prognosis using the median as the cut-off value: low-risk group and high-risk group. Kaplan-Meier curve and log-rank test were used to describe and compare the effects of surgical modality, radiotherapy modality, presence of chemotherapy, and type of pathology on patient OS in the different risk groups.

In this study, EC-induced death and non-EC-induced death were regarded as two competing events. Fine-Gray test and cumulative incidence function (CIF) were used to identify the independent influencing factors affecting the CSS of EC patients, and a nomogram was constructed accordingly. The performance of the model was measured using the concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, and calibration plot. The model constructed in this study was also compared with the model based on AJCC staging model. In addition, we compared the mortality rates predicted by traditional survival analysis with those predicted by the competing risk model. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Statistical analysis

R 4.2.2 and SPSS 26.0 were used. We used Chi-squared test for statistical analysis of categorical variables and t-test for continuous variables. Bilateral P values less than 0.05 were considered to be statistically different. Multicollinearity among all continuous and categorical variables was assessed by calculating the variance inflation factor (VIF); the majority of VIFs were below 10, indicating that collinearity was not a substantial concern.

Results

Population baseline characteristics

The 10,906 patients who met the inclusion criteria were randomized into the training cohort (N=7,633) and the validation cohort (N=3,273) with a mean age of 65.61±8.79 and 65.79±8.69 years, respectively. A total of 4,310 patients received chemotherapy; 3,487 (31.9%) received beam radiation; 1,965 (18.1%) received combination of beam with implants or radioisotopes; 5,454 (50.0%) received radioactive implants (includes brachytherapy); 9,547 (87.5%) underwent hysterectomy with bilateral salpingectomy and ovariectomy oophorectomy (Hys + BSO); 1,093 (10.0%) underwent radical hysterectomy; 147 (1.35%) underwent hysterectomy without bilateral salpingectomy and ovariectomy oophorectomy (Hys − BSO). In terms of survival, the mean length of follow-up was 56.6 (range, 1–107) months. Mortality events occurred in 2,794 patients, of which 2,003 (71.7%) patients died from EC and 791 (28.3%) patients died from other causes. The demographic and clinicopathological characteristics of all patients were shown in Table 1.

Table 1

The demographic and clinicopathological characteristics of all patients in the training and validation cohorts

Factors Train (N=7,633) Test (N=3,273) All (N=10,906) t/χ2 P
Grade 5.50 0.24
   I 1,226 [16] 524 [16] 1,750 [16]
   II 2,078 [27] 828 [25] 2,906 [27]
   III 1,950 [26] 880 [27] 2,830 [26]
   IV 696 [9] 316 [10] 1,012 [09]
   Unknown 1,683 [22] 725 [22] 2,408 [22]
Behavior 1.67 0.64
   Endometrioid 5,973 [78] 2,535 [78] 8,508 [78]
   Serous 833 [11] 378 [12] 1,211 [11]
   Carcinoma 669 [9] 298 [9] 967 [9]
   Clear 158 [2] 62 [2] 220 [2]
Stage 5.25 0.15
   I 4,707 [62] 2,061 [63] 6,768 [62]
   II 875 [12] 360 [11] 1,235 [11]
   III 1,810 [24] 730 [22] 2,540 [23]
   IV 241 [3] 122 [4] 363 [3]
T stage 1.39 0.50
   T1 5,381 [70] 2,344 [72] 7,725 [70]
   T2 1,125 [15] 464 [14] 1,589 [15]
   T3/4 1,127 [15] 465 [14] 1,592 [15]
N stage 7.87 0.02
   N0 6,322 [82] 2,719 [83] 9,041 [83]
   N1 810 [11] 380 [12] 1,190 [11]
   N2 501 [7] 174 [5] 675 [6]
M stage 2.18 0.14
   M0 7,423 [97] 3,166 [97] 10,589 [97]
   M1 210 [3] 107 [3] 317 [3]
Surgery of primary site 0.66 0.42
   ≤50 6,843 [90] 2,951 [90] 9,794 [90]
   >50 790 [10] 322 [10] 1,112 [10]
Regional lymph node surgery 8.80 0.03
   None 1,240 [16] 466 [14] 1,706 [16]
   1–3 391 [5] 178 [5] 569 [5]
   ≥4 5,770 [76] 2,512 [77] 8,282 [76]
   Other 232 [3] 117 [4] 349 [3]
Surgery of other regional sites/distant sites 3.38 0.07
   None 7,203 [94] 3,059 [94] 10,262 [94]
   Other 430 [6] 214 [6] 644 [6]
Radiation 0.26 0.88
   Beam 2,451 [32] 1,036 [32] 3,487 [32]
   Combination 1,369 [18] 596 [18] 1,965 [18]
   Brachytherapy 3,813 [50] 1,641 [50] 5,454 [50]
Chemotherapy 0.63 0.43
   Yes 2,998 [39] 1,312 [40] 4,310 [40]
   No/unknown 4,635 [61] 1,961 [60] 6,596 [60]
Bone 4.35 0.04
   Yes 17 [0.2] 15 [0.2] 32 [0.2]
   No 7,616 [99.8] 3,258 [99.8] 10,874 [99.8]
Brain 1.98 0.16
   Yes 8 [0.1] 7 [0.1] 15 [0.1]
   No 7,625 [99.9] 3,266 [99.9] 10,891 [99.9]
Liver 0.00 0.96
   Yes 12 [0.2] 5 [0.2] 17 [0.2]
   No 7,621 [99.8] 3,268 [99.8] 10,889 [99.8]
Lung 5.03 0.03
   Yes 29 [0.3] 23 [1] 52 [0.5]
   No 7,604 [99.7] 3,250 [99] 10,854 [99.5]
Indicator 1.03 0.31
   Yes 6,725 [88] 2,906 [89] 9,631 [88]
   No 908 [12] 367 [11] 1,275 [12]
Marriage 5.38 0.50
   Married 4,059 [53] 1,745 [53] 5,804 [53]
   Divorced 945 [12] 435 [13] 1,380 [13]
   Single 1,412 [19] 582 [18] 1,994 [18]
   Widowed 1,217 [16] 511 [16] 1,728 [16]
Race 0.31 0.58
   White 6,206 [81] 2,676 [82] 8,882 [81]
   Other 1427 [19] 597 [18] 2,024 [19]
Sequence number 0.03 0.87
   1 primary 5,952 [78] 2,557 [78] 8,509 [78]
   More 1,681 [22] 716 [22] 2,397 [22]
Age (years) 65.61±8.79 65.79±8.69 – −1.02 0.31
Nodes (n) 16.37±35.88 14.30±33.98 – 2.87 0.01
Tumor size (mm) 47.35±33.58 49.18±38.23 – −2.38 0.01
Time (months) 56.50±25.41 56.59±25.25 – −0.18 0.86
Number 1.26±0.57 1.26±0.26 – 0.16 0.87

Data are presented as n [%] or mean ± standard deviation. M, metastasis; N, node; T, tumor.

Parameters associated with CSS and OS

In the competing risk model, influencing factors with P<0.05 in the univariate analysis were included in the multivariate analysis, and 13 independent factors influencing the prognosis of postmenopausal EC patients treated with surgery + radiotherapy were eventually identified: age, tissue type, brain metastasis, lung metastasis, marital status, T-stage, N-stage, stage, grade, radiotherapy modality, tumor size, surgical site, and regional lymphadenectomy (Table 2). Sixteen factors influencing OS were identified using univariate and multivariate Cox proportional hazards models, and another three factors were added: bone metastasis, the presence of chemotherapy, and the total number of in situ/malignant tumors for patients. In addition, the CIFs of the significant influencing factors included in the model were plotted in Figure S1.

Table 2

Univariate and multivariate competing risk model analyzes of variables for predicting CSS in the training cohort (N=7,633)

Variables Univariate analysis Multivariate analysis
HR (95% CI) P HR (95% CI) P
Age 1.02 (1.02–1.03) <0.001 1.02 (1.01–1.03) <0.001
Behavior
   Endometrioid Reference Reference
   Carcinoma 4.27 (3.72–4.91) <0.001 2.43 (2.04–2.90) <0.001
   Clear 2.11 (1.52–2.93) <0.001 1.05 (0.73–1.50) 0.80
   Serous 2.85 (2.48–3.26) <0.001 1.45 (1.22–1.73) <0.001
Benign and borderline tumor number 1.13 (0.62–2.06) 0.70 NA NA
Bone metastasis
   Yes Reference Reference
   No 0.18 (0.08–0.42) <0.001 0.77 (0.33–1.78) 0.54
Brain metastasis
   Yes Reference Reference
   No 0.01 (0.00–0.03) <0.001 0.12 (0.04–0.30) <0.001
Chemotherapy
   Yes Reference Reference
   No/unknown 0.4 (0.36–0.44) <0.001 1.01 (0.86–1.19) 0.86
Grade
   I Reference Reference
   II 2.22 (1.70–2.89) <0.001 2.03 (1.56–2.65) <0.001
   III 5.81 (4.53–7.44) <0.001 3.67 (2.83–4.77) <0.001
   IV 7.43 (5.69–9.72) <0.001 3.75 (2.80–5.04) <0.001
   Unknown 3.09 (2.38–4.03) <0.001 2.4 (1.84–3.13) <0.001
First malignant primary indicator
   Yes Reference Reference
   No 1.19 (1.02–1.39) 0.03 1.01 (0.86–1.20) 0.87
Liver
   Yes Reference Reference
   No 0.13 (0.06–0.28) <0.001 0.6 (0.29–1.22) 0.16
Lung
   Yes Reference Reference
   No 0.09 (0.05–0.17) <0.001 0.47 (0.23–0.96) 0.04
Marriage
   Married Reference Reference
   Divorced 1.16 (0.98–1.36) 0.08 1.09 (0.92–1.30) 0.32
   Single 1.04 (0.90–1.21) 0.58 1.05 (0.90–1.23) 0.55
   Widowed 1.59 (1.38–1.82) <0.001 1.28 (1.10–1.48) 0.002
M stage
   M0 Reference Reference
   M1 5.63 (4.62–6.85) <0.001 1.28 (0.75–2.18) 0.36
Nodes 1.00 (1.00–1.00) <0.001 1 (1.00–1.01) 0.11
N stage
   N0 Reference Reference
   N1 2.36 (2.05–2.71) <0.001 1.36 (1.09–1.7) 0.007
   N2 2.9 (2.47–3.4) <0.001 1.28 (1.01–1.63) 0.04
Tumor number 1.01 (0.93–1.11) 0.76 NA NA
Surgery of other regional sites/distant sites
   None Reference Reference
   Other 1.84 (1.53–2.22) <0.001 1.09 (0.89–1.34) 0.40
Race
   White Reference Reference
   Other 1.48 (1.31–1.67) <0.001 1.09 (0.96–1.25) 0.19
Radiation
   Beam Reference Reference
   Brachytherapy 0.35 (0.31–0.40) <0.001 0.64 (0.55–0.74) <0.001
   Combination 0.71 (0.62–0.82) <0.001 0.80 (0.69–0.92) 0.002
Regional lymph node surgery
   None Reference Reference
   <4 1.26 (0.99–1.60) 0.06 1.89 (1.13–3.16) 0.02
   ≥4 0.82 (0.71–0.94) 0.004 1.19 (0.74–1.92) 0.46
   Other 0.89 (0.64–1.25) 0.51 1.40 (0.82–2.41) 0.22
Sequence number
   1 primary Reference Reference
   More 0.99 (0.88–1.13) 0.92 NA NA
Size 1.00 (1.00–1.01) <0.001 1.00 (1.00–1.00) 0.002
Stage
   I Reference Reference
   II 1.62 (1.36–1.94) <0.001 1.03 (0.74–1.42) 0.88
   III 2.95 (2.62–3.33) <0.001 1.44 (1.09–1.91) 0.01
   IV 8.01 (6.58–9.74) <0.001 1.82 (1.04–3.19) 0.04
Surgery of primary site
   ≤50 Reference Reference
   >50 1.70 (1.47–1.96) <0.001 1.38 (1.18–1.61) <0.001
T stage
   T1 Reference Reference
   T2 1.79 (1.55–2.07) <0.001 1.43 (1.09–1.86) 0.009
   T3/4 3.48 (3.08–3.93) <0.001 1.45 (1.16–1.80) <0.001

CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; M, metastasis; N, node; NA, not applicable; T, tumor.

Nomograms and validation of CSS and OS

Nomograms predicting CSS and OS for surgery combined with radiotherapy in postmenopausal EC patients were constructed using Fine-Gray and Cox, respectively (Figures 1,2). In the competing risk nomogram, the C-indexes were 0.800 [standard error (SE): 0.002] and 0.775 (SE: 0.003) for the training and validation cohorts, respectively; the 1-, 3-, and 5-year areas under the curve (AUCs) for the training cohort were 0.861 [95% confidence interval (CI): 0.839–0.884], 0.805 (95% CI: 0.790–0.820), and 0.791 (95% CI: 0.775–0.806); the 1-, 3-, and 5-year AUCs for the validation cohort were 0.810 (95% CI: 0.762–0.857), 0.785 (95% CI: 0.761–0.809), and 0.775 (95% CI: 0.751–0.798), respectively (Figure 3A,3B). It indicated that the nomogram constructed based on the competing risk model in this study possessed good accuracy and predictive power in predicting patient CSS.

Figure 1 Prognostic nomogram for predicting 1-, 3-, and 5-year CSS in postmenopausal EC patients undergoing surgery combined with radiotherapy. CSS, cancer-specific survival; EC, endometrial cancer; N, node; T, tumor.
Figure 2 Prognostic nomogram for predicting 1-, 3-, and 5-year OS in postmenopausal EC patients undergoing surgery combined with radiotherapy. EC, endometrial cancer; M, metastasis; N, node; OS, overall survival; T, tumor.
Figure 3 ROC curves of competing risk nomograms for CSS in postmenopausal EC patients undergoing surgery combined with radiotherapy for (A) training cohort and (B) validation cohort. ROC curves for Cox nomograms of OS in postmenopausal EC patients undergoing surgery combined with radiotherapy for (C) the training cohort and (D) the validation cohort. AUC, area under the curve; CI, confidence interval; CSS, cancer-specific survival; EC, endometrial cancer; OS, overall survival; ROC, receiver operating characteristic.

In the nomogram constructed by Cox, C-indexes were 0.753 (SE: 0.006) and 0.726 (SE: 0.009) for the training and validation cohorts, respectively; the 1-, 3-, and 5-year AUCs for the training cohort were 0.828 (95% CI: 0.801–0.855), 0.788 (95% CI: 0.773–0.802), and 0.772 (95% CI: 0.758–0.787), respectively; the 1-, 3-, and 5-year AUCs for the validation cohort were 0.809 (95% CI: 0.768–0.851), 0.758 (95% CI: 0.735–0.781), and 0.736 (95% CI: 0.714–0.758), respectively (Figure 3C,3D). Again, this indicated that the model constructed in this study had good accuracy in predicting OS in EC patients.

Calibration plot showed that nomograms had good agreement between predicted survival and observed survival in the training set and validation set (Figure S2). In estimating cancer-specific mortality (CSM) in EC patients, the 1-, 3-, and 5-year CSMs estimated by competing risk models were 3.13%, 12.85%, and 18.25%, and the 1-, 3-, and 5-year CSMs estimated by Cox proportional hazards model were 3.37%, 13.87%, and 19.77%. The mortality estimated by traditional survival analysis at different time points is higher than that by competitive risk model (Table 3).

Table 3

The estimated mortality at different time points by Fine-Gray and Cox

Time (months) Cox (%) Fine-Gray (%)
CSM Death from other causes
12 3.37 3.13 0.77
24 9.08 8.41 1.87
36 13.87 12.85 3.31
48 17.22 15.94 4.69
60 19.77 18.25 6.09
72 21.64 19.93 7.80
84 23.22 21.32 9.49

CSM, cancer-specific mortality.

Comparison with the AJCC staging model

The model AUCs for the training set of the prediction model constructed according to AJCC staging (7th) at 12, 36, and 60 months were 0.715 (95% CI: 0.681–0.749), 0.679 (95% CI: 0.661–0.697), and 0.667 (95% CI: 0.650–0.684); the model AUCs for the validation set of the prediction model constructed according to AJCC staging (7th) at 12, 36, and 60 months were 0.695 (95% CI: 0.640–0.749), 0.660 (95% CI: 0.633–0.688), and 0.652 (95% CI: 0.627–0.677) (Figure 4). The performance of the final model we constructed was much better than that of the AJCC staging model, which had insufficient predictive power in the training and validation sets.

Figure 4 ROC curves of AJCC staging model for postmenopausal EC patients undergoing surgery combined with radiotherapy. (A) Training cohort. (B) Validation cohort. AJCC, American Joint Committee on Cancer; AUC, area under the curve; CI, confidence interval; EC, endometrial cancer; ROC, receiver operating characteristic.

Risk stratification

We calculated individual scores for each patient for risk stratification and classified them into high- and low-risk groups based on the median (72.954). According to the Kaplan-Meier curve, the risk stratification system could accurately identify OS in the high- and low-risk groups. The 1-, 3-, and 5-year probabilities of OS for the low-risk group were 99.1%, 94.5%, and 90.6%, respectively, while the 1-, 3-, and 5-year probabilities of OS for the high-risk group were 93.3%, 73.6%, and 61.8%, respectively. The difference in survival rates between the high- and low-risk groups was statistically significant (P<0.0001) (Figure 5A,5B).

Figure 5 Risk stratification of Kaplan-Meier curves for OS in postmenopausal EC patients undergoing surgery combined with radiotherapy in the training cohort (A) and validation cohort (B). (C) Kaplan-Meier curves of patients receiving different radiotherapy methods for evaluating the benefits of different radiotherapy methods on OS. (D) Kaplan-Meier curves of chemotherapy or not. (E) Kaplan-Meier curves of different pathological types. (F) Kaplan-Meier curves of different surgical methods. EC, endometrial cancer; OS, overall survival.

Subgroup analysis

To evaluate the survival benefit of different radiotherapy modalities, different pathological staging, the presence of chemotherapy, and different surgical modalities in EC patients in different risk groups, we compared the OS of patients in different groups. The radiotherapy modalities included beam radiation; combination of beam with implants or radioisotopes; and radioactive implants (including brachytherapy). Surgical modalities included local surgery, exenteration, or other surgery types (L/E/OTH) and Hys ± BSO; radical hysterectomy. KM curve showed that Brachytherapy significantly prolonged OS in both the high- and low-risk groups, so Brachytherapy had the greatest survival advantage (Figure 5C). Subgroup analysis of surgery combined with radiotherapy ± chemotherapy showed that surgery without chemotherapy had a survival advantage compared with surgery combined with chemotherapy (Figure 5D). Subgroup analysis of pathological staging showed that Endo had the greatest survival advantage in both the low- and high-risk groups, while UCSs had the worst survival prognosis (Figure 5E). In the high-risk group (nomogram score >72.954), patients treated with L/E/OTH/Hys ± BSO had significantly higher OS than those treated with radical hysterectomy; while in the low-risk group, neither L/E/OTH/Hys ± BSO was significantly associated with OS (Figure 5F).

Discussion

We constructed nomograms of CSS and OS in postmenopausal EC patients treated with radiotherapy combined with surgery by competing risk model and Cox proportional hazards model, and both had high accuracy in predicting mortality. There was an overall increasing trend in EC morbidity and mortality, which may be attributed to the global increase in both obesity rate and population aging. Approximately 70% of EC recurrences occurred within the first 3 years after the initial surgery (19-21). To our knowledge, this is the first study to assess the CSS and OS prognosis of postmenopausal EC patients undergoing surgery combined with radiotherapy using competing risk model and Cox proportional hazards model.

As expected, Grade and Stage of EC were the two prominent predictors of competing risk nomograms. Consistent with previous studies, higher Grade and Stage suggested a worse EC prognosis (22). This was because higher Grade and Stage indicated a high degree of malignancy in EC, which implied that the tumor was prone to metastasis and spread. Patients with UCSs and uterine serous carcinoma (USC) had a worse prognosis, with a 141% survival risk increased by UCSs and 44% by USC. The occurrence of distant metastasis (i.e., brain, lung, liver, bone metastasis, etc.) remains an important cause of poor survival outcomes (23). In our CSS analysis, patients without brain metastasis had an 88% lower risk of survival; those without lung metastasis had a 53% lower risk of survival, which is consistent with the findings of Li (24). We found that age played a key role in the total score. Every 1-year increase in patient age was associated with a 2% increase in survival risk. It was shown that age >65 years was a strong independent poor-prognosis factor (25) and that old women with EC had higher relapse and mortality rates compared with young women (22,25-30). Regardless of whether they underwent lymphadenectomy and the presence of lymph node metastasis, old women faced a worse prognosis for survival (31) and were more likely to have non-endometrioid tumor histologic lesions. It suggested a significant positive correlation between age and genomic instability (GI) in EC patients, with GI levels increasing with the age of cancer patients (32). It seemed that with aging, EC exhibited a more aggressive tumor phenotype characterized by mutant p53 expression and downregulation of E-cadherin expression, which in turn led to tumors being diagnosed more advanced in old patients (33), all of which are associated with a worse prognosis.

In risk-stratified subgroup analysis, UCSs had lower survival rates than the other 3 pathological types in both the high- and low-risk groups. Although UCSs are rare, they account for 16.4% of all uterine cancer-related deaths (34,35), which may be related to the heterogeneous component of UCSs. UCSs include both epithelial and mesenchymal malignant cell components. The former may present with endometrioid, clear cell, or serous features; the latter is both “homologous” in that it resembles endometrial stromal sarcoma or leiomyosarcoma, and “heterologous” in that it has features of extrauterine specialized connective tissue (muscle, cartilage, and bone) (36-38), so the invasion of vascular lymphatic vessels is very common (39). Lymph nodes are positive at diagnosis in 30–40% of UCSs, and 10% of patients also show visceral metastasis, especially in the lungs (40). Thus, the 5-year survival rate of advanced or metastatic UCSs does not exceed 10–30% (41). We found that the adoption of combination and brachytherapy was a protective factor for EC. In the risk-stratified subgroup analysis, the low-risk and high-risk groups showed that patients receiving radiotherapy had longer survival times compared to those receiving chemoradiotherapy, and patients undergoing brachytherapy had longer survival times compared to those receiving external beam radiotherapy (EBRT). This finding was consistent with the results of the GOG-249 trial (42): stage I and II EC patients with high or intermediate risk factors were randomly assigned to the group receiving pelvic radiotherapy alone or to the group receiving chemotherapy (three cycles of carboplatin and paclitaxel) followed by vaginal brachytherapy; progression-free survival (PFS) and OS were not found to be superior in the chemoradiotherapy group than in the group receiving external radiation alone, and the recurrence in the pelvic cavity and para-aortic region increased significantly in the radiochemotherapy group. Another PORTEC-3 trial demonstrated a significant increase in the incidence of adverse events and a decrease in health-related quality of life in EC patients during and after treatment with chemoradiotherapy (9). In contrast, one study showed that patients receiving chemotherapy alone reported two to seven times the probability of vaginal recurrence or pelvic recurrence (35%, 18%) than patients receiving radiotherapy (18%, 9%) or chemoradiotherapy (5%, 7%) (43). Radiotherapy delays pelvic recurrence and chemotherapy delays distant metastasis, but whether chemoradiotherapy is superior to radiotherapy alone in improving OS and failure-free survival remains controversial and still needs to be further investigated in larger prospective trials. However, the observed differences in survival may be due in part to these unmeasured differences in baseline risk rather than to differential efficacy of treatment modalities alone. The survival advantage of brachytherapy needs to be further validated in prospective studies or in more detailed individual patient data.

Nomograms are widely used in many fields, and nomograms with individualized predictive power can be used to identify and stratify patients. Mortality rates estimated by traditional survival analysis were higher than those estimated by competing risk model in either time period. This suggests that traditional survival analysis may overestimate mortality from disease. This may be due to the fact that competitive risk model is more suitable for predictive studies of diseases with long survival, whereas traditional survival analysis is more suitable for etiological studies (44). Currently, some studies have also developed predictive models for different phenotypes of EC with good predictive performance. Wu et al. developed a nomogram for predicting OS in patients with low-grade endometrial stromal sarcoma (LG-ESS) with the stepwise regression based on the Akaike information criterion (AIC) minimum, which has a better identification ability to identify high-risk patients (45). Yan et al. constructed a survival nomogram of EC patients with lung metastasis using the Cox model to analyze the association between clinical characteristics of EC with lung metastasis and OS (46). The nomogram was validated to have stable identification ability. Yang et al. developed and validated a nomogram based on log odds of positive lymph nodes (LODDS) to predict postoperative OS in stage IIIC EC patients, and the nomogram showed good predictive ability with C-index: 0.742; AIC: 8228.95 (47).

Using C-index, ROC, and calibration plot, we validated that the OS and CSS prediction models are highly predictive. Compared with simple AJCC staging, we also considered demographic and tumor-related information to quantify the effects of these factors and to more comprehensively and specifically predict CSS and OS in postmenopausal EC patients receiving post-surgical radiotherapy. The predictive performance of our model is much better than that of AJCC staging. Also, the results of internal validation showed good agreement between the predicted and actual observed values of survival. Our predictive model can be integrated into clinical workflows for risk stratification and treatment decisions. In clinical practice, physicians can utilize this model for individualized risk assessment of patients, thus optimizing treatment plans. For example, for high-risk patients, more aggressive treatment strategies, such as combined radiotherapy and brachytherapy, can be considered; for low-risk patients, overtreatment can be avoided. Additionally, this model can serve as a tool for clinical research to aid in designing more precise treatment trials.

Nevertheless, several limitations should be acknowledged. Our model was derived from the SEER database, which—while extensive—contains incomplete records and lacks external validation. Such omissions can introduce selection and information bias; for example, SEER does not fully capture the biological behavior of rare subtypes such as UCS or provide molecular or genomic profiles. Prospective, multicenter studies are therefore warranted to externally validate the model, ensure complete pathologic and treatment data collection, and integrate molecular biomarkers (48), thereby further enhancing predictive accuracy and clinical utility.

Conclusions

Our study successfully developed and validated a nomogram for predicting OS and CSS in postmenopausal EC patients treated with surgery and radiotherapy, which outperform existing risk stratification tools. The nomogram demonstrated superior performance compared to the AJCC staging model and identified key prognostic variables influencing survival outcomes. It also revealed that chemoradiotherapy was associated with lower survival rates than radiotherapy alone in both low- and high-risk groups, and brachytherapy showed a survival advantage over EBRT. These findings provide valuable insights for individualized risk assessment and treatment decision.

Acknowledgments

We thank the SEER public database for for providing the data used in this study.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2656/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2656/prf

Funding: This study was supported by the National Administration of Traditional Chinese Medicine’s “High-Level Key Discipline of Traditional Chinese Medicine” Project (No. zyyzdxk-2023068).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2656/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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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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Cite this article as: Wang Q, Tao A, Tuo Y, Li J, Liu F, Deng Z, Li G. Nomogram for predicting cancer-specific mortality risk in endometrial cancer after postoperative radiotherapy. Transl Cancer Res 2026;15(4):311. doi: 10.21037/tcr-2025-1-2656

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