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Research Article: Observational Study

Diagnostic and prognostic factors, and two nomograms for endometrial cancer patients with bone metastasis

A large cohort retrospective study

Yang, Fengkai MDa; Zhao, Ruhan MDb; Huang, Xiaohui MDc; Wang, Yucheng MDd,∗

Editor(s): Ramchandani., Divya

Author Information
doi: 10.1097/MD.0000000000027185
  • Open

Abstract

1 Introduction

Endometrial cancer (EC) is a group of epithelial malignancies originating in the endometrium and is the fourth most common cancer disease in the United States.[1] According to statistics, the number of new diagnoses and deaths in the United States in 2018 reached 63,230 and 11,350, respectively.[2] EC is usually confined to the uterus at the time of initial diagnosis and can be cured by surgery in some patients. However, there are still patients with advanced stages at diagnosis, or some patients develop extra-pelvic metastases after surgery. The common metastatic sites of EC include the lung, liver, brain, and bone.[3] Although bone metastasis (BM) is a common complication of cancer, they are generally less common in EC than in breast or prostate cancer.[4,5] The occurrence of BM in EC patients means a poor prognosis, with a 5-year survival rate of only 8.7%.[6,7] Hence, it is of great significance to identify risk factors and conditions for BM in EC patients as soon as possible.

It has been reported that histologic type, advanced age, unmarried, black, and uninsured are high-risk factors for BM from EC.[8] Furthermore, in previous studies, several factors have been found to correlate with the prognosis of EC patients with BM, including age at diagnosis, histologic type, tumor grade, marital status, race, insurance status, surgical status, and the number of distant metastatic sites.[9] However, these studies only analyzed different factors separately and did not focus on constructing predictive models of the risk of BM from EC and the prognosis of EC patients with BM, which means that the probability of the outcome is not quantifiable.

Nomogram, a tool that combines multiple biological and clinical variables to predict specific endpoints, has been widely used in recent years to predict the prognosis of cancer patients.[10] The combination of these important variables enables the nomogram to individually estimate the probability of events over time, such as overall survival (OS) and the risk of metastasis in cancer patients.[11] Well-constructed clinical nomograms provide a prediction of individual outcomes, which is beneficial to both patients and clinicians.[12] Therefore, we aimed to use the information in the Surveillance, Epidemiology, and End Results (SEER) database to construct 2 nomograms to predict the risk of BM from EC patients and the OS of EC patients with BM, respectively.

2 Methods

2.1 Patient selection

The workflow of our study is illustrated in Figure 1. With permission from the SEER program of the United States National Cancer Institute, we collected information from patients diagnosed with EC between 2010 and 2015. The SEER program consists of 18 population-based cancer registries that collect statistical, oncological, diagnostic, and treatment information on approximately 28% of the United States population.[13] There is no medical ethics review and no informed consent required for the analysis of unidentified data in the SEER database. The inclusion criteria were as follows: primary EC patients, patients with BM, patients with complete clinicopathologic features, demographic data, and follow information. Finally, a total of 54,077 patients with EC who met the criteria were included to study their risk factors for developing BM. Subsequently, patients with BM with EC survival time ≥1 month and specific treatment information, including surgery, radiotherapy, and chemotherapy, were used to form a new cohort to explore prognostic factors in EC patients with BM. Ultimately, 364 patients were used to study prognostic factors.

Figure 1
Figure 1:
The workflow describing the schematic overview of the project. BM = bone metastasis, DCA = decision curve analysis, EC = endometrial cancer, ROC = receiver operating characteristic.

2.2 Variable definitions

In this study, a total of 11 variables were used to identify risk factors for the development of BM from EC, including age, race, histologic type, grade, T stage, N stage, brain metastasis, liver metastasis, lung metastasis, insurance status, and marital status. There were also 3 treatment variables included in the study of prognostic factors in EC patients with BM, including surgery, chemotherapy, and radiotherapy. In this section, OS was the primary outcome, which was defined as the time interval from the date of diagnosis to death (from any cause).

2.3 Statistical analysis

The optimal cutoff value of age in terms of OS was determined by the X-tile software. To process the data conveniently, we divided the patients into 2 groups (<67 and ≥67).[14] Patients were randomized in a 7:3 ratio into a training cohort and a validation cohort, respectively, and the classification process was performed in the R software. Univariate and multivariate logistic regression analyses were performed to identify risk factors for BM from EC. Meanwhile, univariate and multivariate Cox regression analyses were used to identify independent prognostic factors in EC patients with BM. Diagnostic and prognostic nomograms were constructed separately based on corresponding risk factors and independent prognostic factors. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to evaluate the discrimination of the nomogram. The calibration curve was used to measure the agreement of predicted probabilities with actual survival outcomes. The clinical application value of the nomograms was evaluated by decision curve analysis (DCA). In addition, we divided all patients into high-risk, middle-risk, and low-risk groups according to the best cutoff value of the risk score, and validated the prognostic value of the nomogram using Kaplan-Meier survival curves analysis and log-rank test. This study used SPSS 25.0 (NY, USA) and R software (version 4.0.3, Shanghai Jiao Tong University, Shanghai, China) for statistical analysis. In the present study, a P value < .05 was identified as statistical significance.

3 Results

3.1 Baseline characteristics of EC patients

A total of 54,077 EC patients from the SEER database were included. Furthermore, 37,856 and 16,221 patients were included in the training and validation cohorts, respectively. The baseline characteristics of 54,077 patients with EC were shown in Table 1.

Table 1 - Baseline demographics and clinical characteristics of patients with endometrial cancer patients.
Training cohort Validation cohort
N = 37,856 N = 16,221
Variables n % n %
Age
 <67 25,103 66.0 10,662 65.7
 ≥67 128,843 34.0 5559 34.3
Race
 Black 3684 9.7 1605 9.9
 Other 3587 9.5 1500 9.2
 White 30,585 80.8 13,116 80.9
Histological types
 Endometrioid 25,915 68.4 11,132 68.6
 Non-endometrioid 8884 23.5 3776 23.3
 Sarcomas 3057 8.1 1313 8.1
Grade
 I 15,950 42.1 6784 41.8
 II 10,020 26.5 4435 27.4
 III 8174 21.6 3440 21.2
 IV 3712 9.8 1562 9.6
T stage
 T1 29,586 78.2 12,771 78.7
 T2 2667 7.0 1164 7.2
 T3 4194 11.1 1681 10.4
 T4 667 1.8 295 1.8
 Tx 742 1.9 310 1.9
N stage
 No 33,074 87.4 14,257 87.9
 N1 2478 6.6 1033 6.4
 N2 1690 4.4 666 4.1
 Nx 614 1.6 265 1.6
Brain metastasis
 No 37,787 99.8 16,189 99.8
 Yes 69 0.2 32 0.2
Liver metastasis
 No 37,504 99.1 16,071 99.1
 Yes 352 0.9 150 0.9
Lung metastasis
 No 37,099 98.0 15,896 98.0
 Yes 757 2.0 325 2.0
Insurance status
 No 1182 3.1 518 3.2
 Yes 36,674 96.9 15,703 96.8
Marital status
 No 17,682 46.7 7715 47.6
 Yes 20,174 53.3 8506 52.4

3.2 Development and validation of a diagnostic nomogram for BM from EC

Univariate and multivariate logistic regression analyses were performed to determine the risk factors for BM from EC. The results showed that 7 predictors were independent predictors of BM from EC, including race, grade, T stage, N stage, brain metastasis, liver metastasis, and lung metastasis (Table 2). Based on the independent predictors selected in the training cohort, the diagnostic nomogram was constructed for the risk assessment of BM in EC patients (Fig. 2). The AUCs of the nomogram were 0.943 and 0.954 in the training and validation cohorts, respectively, showing good discrimination (Fig. 3). Furthermore, the ROC curves and AUC of each independent risk factor were also generated (Fig. 4). The results suggested that the discrimination of any single risk factor was lower than that of the nomogram in either the training or validation cohort. In both the training and validation cohorts, the calibration curves exhibit a high degree of agreement between observations and predictions (Fig. 5A and B). The DCA showed that the prognostic nomogram had a wider range of practical threshold probabilities, significantly increasing the net benefit and suggesting a high clinical value of the diagnostic nomogram (Fig. 5C and D).

Table 2 - Univariate and multivariate logistic regression analyses of risk factor of bone metastasis in patients with endometrial cancer.
Univariate Cox analysis Multivariate Cox analysis
HR 95%CI P HR 95%CI P
Age
 <67 1
 ≥67 1.245 0.964 1.607 .093
Race
 Black 1 1
 Other 0.941 0.585 1.512 .801 1.855 1.109 3.102 .019
 White 0.600 0.419 0.859 .005 1.198 0.812 1.768 .363
Histologic type
 Endometrioid 1
 Non-endometrioid 3.731 2.809 4.955 .000
 Sarcomas 5.401 3.835 7.606 .000
Grade
 I 1 1
 II 4.307 2.084 8.901 .000 3.145 1.495 6.617 .003
 III 27.172 14.291 51.663 .000 7.582 3.826 15.028 .000
 IV 32.870 16.975 63.649 .000 8.291 4.091 16.804 .000
T stage
 T1 1 1
 T2 7.473 4.770 11.709 .000 2.965 1.839 4.781 .000
 T3 14.877 10.523 21.033 .000 3.226 2.158 4.822 .000
 T4 22.969 13.985 37.725 .000 3.473 1.992 6.054 .000
 TX 40.671 26.956 61.366 .000 7.592 4.539 12.696 .000
N stage
 No 1 1
 N1 8.655 6.311 11.870 .000 2.162 1.516 3.082 .000
 N2 10.776 7.715 15.051 .000 2.667 1.828 3.890 .000
 NX 16.770 11.067 25.414 .000 2.029 1.212 3.397 .007
Brain metastasis
 No 1 1
 Yes 57.381 33.017 99.724 .000 7.588 3.941 14.610 .000
Liver metastasis
 No 1 1
 Yes 33.740 24.401 46.655 .000 3.433 2.332 5.055 .000
Lung metastasis
 No 1 1
 Yes 44.406 34.167 57.715 .000 7.515 5.505 10.258 .000
Insurance status
 No 1
 Yes 0.538 0.313 0.925 .025
Marital status
 No 1
 Yes 0.715 0.557 0.918 .009

Figure 2
Figure 2:
Nomogram to estimate the risk of BM in patients with EC. BM = bone metastasis, EC = endometrial cancer.
Figure 3
Figure 3:
The ROC curves of the diagnostic nomogram in the training cohort (A) and the validation cohort (B). AUC = area under the curve, ROC = receiver operating characteristic.
Figure 4
Figure 4:
Comparison of AUC between diagnostic nomogram and each independent predictor in the training cohort (A) and the validation cohort (B). AUC = area under the curve.
Figure 5
Figure 5:
Calibration curves and DCA of the diagnostic nomogram for estimating the risk of BM in patients with EC. BM = bone metastasis, DCA = decision curve analysis, EC = endometrial cancer.

3.3 Development and validation of a prognostic nomogram for EC patients with BM

A total of 364 EC patients with BM were used to identify independent prognostic factors, as shown in Table 3. Meanwhile, 256 patients were incorporated into the training cohort, and the remaining 108 patients were incorporated into the validation cohort. Of the total patients included, 220 (60.4%) were aged less than 67 years. At the same time, the majority of patients were white (71.4%). A total of 175 (48.1%) patients had lung metastases, 30 (8.2%) patients had brain metastasis and 89 (24.5%) patients had liver metastases. As for treatment, nearly half of the patients received surgery (49.7%), 213 (58.5%) had chemotherapy and 159 (43.7%) had radiotherapy.

Table 3 - Baseline demographics and clinical characteristics of endometrial cancer patients with bone metastasis.
Training cohort Validation cohort
N = 256 N = 108
Variables n % n %
Age
 <67 159 62.1 61 56.4
 ≥67 97 37.9 47 43.5
Race
 Black 42 16.4 20 18.5
 Other 25 9.8 17 15.7
 White 189 73.8 71 65.8
Histological types
 Endometrioid 82 32.0 34 31.5
 Non-endometrioid 110 42.9 53 49.1
 Sarcoma 64 25.1 21 19.4
Grade
 I 9 3.5 7 6.5
 II 30 11.7 13 12.0
 III 134 52.3 59 54.6
 IV 83 32.5 29 26.9
T stage
 T1 49 19.2 18 16.7
 T2 30 11.7 12 11.1
 T3 104 40.6 46 42.6
 T4 28 10.9 9 8.3
 Tx 45 17.6 23 21.3
N stage
 No 105 41.1 42 38.9
 N1 60 23.4 29 26.8
 N2 60 23.4 22 20.4
 Nx 31 12.1 15 13.9
Surgery
 No 129 50.4 54 50.0
 Yes 127 49.6 54 50.0
Radiotherapy
 No 146 57.0 59 54.6
 Yes 110 43.0 49 45.4
Chemotherapy
 No 110 43.0 41 38.0
 Yes 146 57.0 67 62.0
Brain metastasis
 No 233 91.0 101 93.5
 Yes 23 9.0 7 6.5
Liver metastasis
 No 197 77.0 78 72.2
 Yes 59 23.0 30 27.8
Lung metastasis
 No 146 57.0 43 39.8
 Yes 110 43.0 65 60.2
Insurance status
 No 15 5.9 3 2.8
 Yes 241 94.1 105 97.2
Marital status
 No 135 52.7 69 63.9
 Yes 121 47.3 39 36.1

The results of the Cox regression analysis performed on all patients are shown in Table 4. The results of univariate Cox regression analysis indicated that age, race, histological type, T stage, surgery, chemotherapy, brain metastasis, and liver metastasis were correlates of OS. After controlling for confounding variables with multivariate Cox regression analysis, histologic type, surgery, chemotherapy, brain metastasis, and liver metastasis were identified as independent prognostic factors (Table 4). Then, the above-mentioned independent predictors were incorporated to construct the prognostic nomogram for predicting 1-, 2-, and 3-year OS (Fig. 6). ROC curves showed the AUCs of this prognostic nomogram at 1-, 2-, and 3-year OS reached 0.756, 0.788, and 0.775, respectively, in the training cohort (Fig. 7A) and 0.765, 0.779, and 0.808, respectively, in the validation cohort (Fig. 7B). The time-dependent ROCs showed that the nomogram has a higher prediction accuracy than a single independent prognostic factor (Fig. 8). The calibration curves of 1-, 2-, and 3-years showed significant consistency between the predictive survival and actual survival in both cohorts (Fig. 9). Moreover, the DCA also demonstrated the strong clinical applicability of the prognostic nomogram (Fig. 10). Interestingly, as shown in Figure 11, we found that as with the subgroup analysis of patients, when patients were classified in the low mortality risk subgroup, it always meant a better prognosis.

Table 4 - Univariate and multivariate Cox analysis for endometrial cancer patients with bone metastasis.
Univariate Cox analysis Multivariate Cox analysis
HR 95%CI P HR 95%CI P
Age
 <67 1
 ≥67 1.321 1.003 1.739 .048
Race
 Black 1
 Other 0.539 0.311 0.935 .028
 White 0.706 0.497 1.004 .053
Histological types
 Endometrioid 1 1
 Non-endometrioid 1.379 1.004 1.895 .047 1.437 1.043 1.980 .027
 Sarcoma 1.428 0.996 2.047 .053 1.974 1.356 2.874 .000
Grade
 I 1
 II 0.734 0.325 1.657 .457
 III 1.366 0.666 2.800 .395
 IV 1.183 0.569 2.461 .653
T stage
 T1 1
 T2 1.384 .831 2.305 .212
 T3 1.433 0.976 2.104 .066
 T4 1.825 1.103 3.018 .019
 Tx 1.467 0.937 2.297 .094
N stage
 No 1
 N1 1.094 0.776 1.543 .608
 N2 1.039 0.735 1.469 .827
 Nx 1.094 0.710 1.687 .683
Surgery
 No 1 1
 Yes 0.592 0.451 0.776 .000 0.543 0.409 0.720 .000
Radiotherapy
 No 1
 Yes 0.829 0.634 1.085 .173
Chemotherapy
 No 1 1
 Yes 0.438 0.334 0.575 .000 0.391 0.295 0.518 .000
Brain metastasis
 No 1 1
 Yes 1.979 1.253 3.126 .003 1.905 1.193 3.042 .007
Liver metastasis
 No 1 1
 Yes 1.518 1.120 2.057 .007 1.665 1.219 2.273 .001
Lung metastasis
 No 1
 Yes 1.098 0.840 1.435 .494
Insurance status
 No 1
 Yes 0.903 0.515 1.582 .720
Marital status
 No 1
 Yes 0.892 0.683 1.165 .402

Figure 6
Figure 6:
Nomogram to predict the OS of EC patients with BM. BM = bone metastasis, EC = endometrial cancer, OS = overall survival.
Figure 7
Figure 7:
The ROC curves of nomogram at 1-, 2-, and 3-years in the training cohort and validation cohort. AUC = area under the curve, ROC = receiver operating characteristic.
Figure 8
Figure 8:
ROC curves of the prognostic nomogram and each independent predictor in predicting prognosis at the 1-, 2-, and 3-year points in the training cohort (A–C), validation cohort (D–F). AUC = area under the curve, ROC = receiver operating characteristic.
Figure 9
Figure 9:
(A–C) The calibration curves of the prognostic nomogram in the training cohort; (D–F) The calibration curves of the prognostic nomogram in the validation cohort. OS = overall survival.
Figure 10
Figure 10:
(A–C) The DCA of the prognostic nomogram in the training cohort; (D–F) The DCA of the prognostic nomogram in the validation cohort. DCA = decision curve analysis.
Figure 11
Figure 11:
The Kaplan-Meier survival curve analysis of the training cohort (A) and validation cohort (B).

4 Discussion

EC is one of the most common gynecologic malignancies, with BM occurring in <1% of patients, and the median survival of EC patients with BM is only 10 to 17 months.[8,15,16] The most common site of BM in EC is the spine, and 70% of patients have multiple BM.[17] In the present study, 2 nomograms were constructed by analyzing relevant data from the SEER database to predict the risk of BM in patients with EC and the OS of EC patients with BM, respectively. In these nomograms, values for the individual patient are located along the variable axes, and a line is drawn upward to the points axis to determine the number of points assigned for each variable. There was a total points line at the bottom of the nomogram, and each variable score was summed to give the total points. And the accumulated total points can be used to predict the risk and OS of the patient. With the advantage of integrating all relevant factors, the model allows for an individualized risk assessment for each patient, which is often better than the subjective judgment of the clinician.[10]

It has been reported that patients with EC, including autopsy, have a 25% probability of BM, and the majority of patients have metastases in many sites, including the liver, lungs, and brain.[18] It was demonstrated in this study that T stage, N stage, race, grade, lung metastasis, liver metastasis, and brain metastasis were risk factors for EC with BM. The presence of metastases at distant sites indicates the presence of hematogenous transmission and increases the probability of BM in the patient.[17] In previous studies, it has been shown that as tumor size and depth of invasion increase, the rate of lymph node involvement also increases, as does the incidence of BM, which is consistent with our results.[19] Also, we observed that currently for patients with a histologic grade of 1 or 2 (so-called low-risk patients), the risk of extrauterine tumor spread is relatively low, whereas, with a histologic grade of 3 or 4, patients have a relatively increased risk of BM. Based on the screened risk factors, the construction of a nomogram model to predict the risk of BM can enable early detection of BM, which is crucial for EC patients to receive appropriate treatment.

In addition, our study showed that histologic type, surgery, chemotherapy, brain metastasis, and liver metastasis were independent prognostic factors for EC patients with BM. Based on 5 independent prognostic factors, a nomogram was constructed. The results showed that the nomogram can be used as an effective tool to identify high-risk patients while achieving an accurate prediction of OS. EC is a low-grade early-stage tumor and is more common, while non-endometrioid carcinoma and sarcoma are less common and have a stronger tendency to spread, resulting in a significantly poorer prognosis.[20] Stefano Uccella et al[16] found that the prognosis of patients with only a single BM was much better than that of patients with multiple organ metastases, which is consistent with our findings. There is still no standard treatment plan for EC patients with BM, but the available treatment options include surgery, chemotherapy, and radiotherapy.[6] The present results demonstrated the survival benefit of chemotherapy and surgery for EC patients with BM. EC is usually treated primarily by surgery, which has become part of the initial treatment of EC, with total hysterectomy being the standard.[21] In addition, there is an association between tumor clearance and patient survival, with a 9.3-month improvement in OS, reported for patients who achieved complete local tumor resection compared to those with incomplete tumor resection.[6] Chemotherapy is a systemic treatment, and the combination of anthracyclines, purple shirts, and platinum is currently used for EC patients with BM.[22] Chemotherapy can act to kill cancer cells in both primary tumor lesions and BM, so in patients with first diagnosed advanced EC (including those with BM), chemotherapy significantly improves the prognosis of patients and increases the survival rate as the number of chemotherapy cycles increase.[23] Radiotherapy failed to improve the prognosis of EC patients with BM, which may be explained by the reduced responsiveness of the aggressiveness of these cancers to treatment and the rapid progression of the disease.[10] However, in our clinical work, we can control bone destruction and prevent fracture by local radiotherapy and application of phosphate or denosumab to the BM lesions, thus relieving pain and improving the quality of life of patients.[19]

Recently, there have been few studies on nomograms to predict OS in patients with EC. Although some genetically based nomograms to predict prognosis in patients with EC have been reported previously, the difficulty and expense of obtaining relevant genetic data on patients have reduced the clinical utility of the models.[24–26] In the present study, we constructed diagnostic and prognostic nomograms to predict the risk of BM in EC patients and the OS of EC patients with BM by analyzing a large number of data, respectively. We believe that 2 nomograms representing OS and distant metastasis, respectively, are complementary and can increase their clinical value in patients with EC. The total score can be calculated by obtaining data for the corresponding variable on the nomogram for each EC patient. The risk of BM can then be easily identified on the diagnostic nomogram, identifying patients in the high-risk group, and guiding clinical practice in early intervention. Similarly, the prognosis of EC patients with BM can be determined from the prognostic nomogram. In the validation of the 2 nomograms, the 2 nomograms showed excellent performance in BM risk assessment and OS prediction in EC patients, respectively, which will enable more accurate personalized clinical decision-making and monitoring. Inevitably, of course, this study has some limitations. First, this was a retrospective study in which selection bias was inevitable. Secondly, the database does not reflect the complete process of treatment and does not clarify the sequence of treatment means and specific information related to treatment, such as the cycle of chemotherapy and the dose of radiotherapy. Third, information collected in the SEER database is about the disease at the time of the first diagnosis and does not record BM that occurred later. Fourthly, since 80% of the individual are “White”, this analysis could be a biased representation for the White population.

5 Conclusion

The risk factors for the development of BM in patients with EC and independent prognostic factors for EC patients with BM were identified in this study. On this basis, we created 2 nomograms that can be used as predictive tools for EC patients to help clinicians differentiate, assess, and evaluate the risk and prognosis of EC patients with BM.

Author contributions

Conceptualization: Fengkai Yang, Xiaohui Huang, Yucheng Wang.

Data curation: Fengkai Yang, Ruhan Zhao.

Formal analysis: Fengkai Yang, Xiaohui Huang.

Methodology: Fengkai Yang, Ruhan Zhao.

Supervision: Fengkai Yang, Yucheng Wang.

Writing – original draft: Fengkai Yang.

Writing – review & editing: Fengkai Yang, Ruhan Zhao, Yucheng Wang.

References

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Keywords:

bone metastasis; endometrial cancer; nomogram; overall survival; SEER

Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.