
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12580-7
10.1016/j.heliyon.2024.e36549
e36549
Research Article
Stage IV ovarian cancer prognosis nomogram and analysis of racial differences: A study based on the SEER database
Wu Guilan a1
Chen Jiana a1
Niu Peiguang a1
Huang Xinhai a
Chen Yunda c
Zhang Jinhua pollyzhang2006@126.com
a⁎
a Department of Pharmacy, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350001, China
c The Affiliated High School of Fujian Normal University in PingTan, Fuzhou, 350400, China
⁎ Corresponding author. University, #18 Daoshan Road, Fuzhou, 350001, China. pollyzhang2006@126.com
1 Guilan Wu, Jiana Chen and Peiguang Niu contributed equally to this paper.

19 8 2024
30 8 2024
19 8 2024
10 16 e3654912 3 2024
16 8 2024
19 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Purpose

Stage IV ovarian cancer is a tumor with a poor prognosis and lacks prognostic models. This study constructed and validated a model to predict overall survival (OS) in patients with newly diagnosed stage IV ovarian cancer.

Methods

The data of this study were extracted from SEER database. Cox regression analysis was used to construct the nomogram model and implemented it in an online web application. Concordance index (C-index), calibration curve, area under receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to verify the performance of the model.

Results

A total of 6062 patients were collected in this study. The analysis showed that age, race, histological grade, histological differentiation, T stage, CA125, liver metastasis, primary site surgery, and chemotherapy were independent prognostic parameters, and were used to construct the nomogram model. The C-index of the training group and the verification group was 0.704 and 0.711, respectively. Based on the score of the nomogram responding risk classification system is constructed. The online interface of Alfalfa-IVOC-OS is free to use. In addition, the racial analysis found that Asian or Pacific Islander people had higher survival rates than white and black people.

Conclusion

This study established a new survival prediction model and risk classification system designed to predict OS time in patients with stage IV ovarian cancer to help clinicians evaluate the prognosis of patients with stage IV ovarian cancer.

Keywords

Ovarian cancer
SEER
Overall survival rate
Nomogram
Risk stratification
==== Body
pmc1 Introduction

Ovarian cancer is one of the most common gynecological malignancies and the fifth most common female cancer [1]. Because early symptoms are not obvious, most patients are not diagnosed until later stages (stage IV). In the UK and US population, about two-thirds of women have advanced disease at diagnosis [2]. Although patients with ovarian cancer can be treated with more advanced medical technologies and drugs, such as more effective chemotherapy agents [3], improved cell reduction, radiotherapy, and targeted therapy [4], the 5-year relative survival rate (RSR) remains below 50 %. It is evident that advanced ovarian cancer has a poor prognosis. Evaluating its prognosis and optimizing the therapeutic management of advanced ovarian cancer is essential for improving the prognosis. Makar et al. [5] published a review suggesting that patients with advanced ovarian cancer should be classified into prognostic categories to better distinguish which types of treatment benefit the most. Therefore, it is important to construct a prognostic assessment model for advanced ovarian cancer.

Several studies have developed models for the prognosis of patients with ovarian cancer. Unfortunately, data on patients with stage IV disease are scarce [[6], [7], [8]], as many reports typically analyze patients together. Wang et al. [9] constructed nomogram for prognostic assessment of patients with stage I-IV ovarian cancer, not only patients with stage IV ovarian cancer; Song et al. [10] constructed prognostic nomogram for patients with advanced ovarian cancer, including patients with stage III and stage IV. The above models used to predict the prognosis of patients with stage IV ovarian cancer may lead to inaccurate results. In order to allow physicians to make better clinical decisions for the benefit of ovarian cancer patients and to achieve individualized treatment and detection, there is an urgent need to establish accurate survival prediction models for stage IV ovarian cancer. In addition, there is little evidence on the prognosis of stage IV ovarian cancer [11]. Studies have shown that age, race and positive lymph nodes are important prognostic factors for ovarian cancer patients [[12], [13], [14]]. Racial difference is an important factor affecting the curative effect of ovarian cancer [15], and the mechanism of the effect of different races on the treatment of stage IV ovarian cancer is still unclear.

In recent years, nomogram has been widely developed and applied in oncology practice for prognostic prediction of specific clinical endpoints [[16], [17], [18], [19]]. It meets the requirements of an integrated model and plays a role in advancing personalized medicine [16]. For user-friendly application, the nomogram model can be converted into a Shiny Web application [20], which can be operated by any user via a smartphone or personal computer. In addition, the SEER database includes data on cancer diagnoses, patient demographics, tumor characteristics, treatment strategies, and survival records for nearly 30 % of the U.S. population. Therefore, this database has a unique advantage in cancer research [21,22]. Based on this background, we used the Stage IV ovarian cancer dataset from the SEER database to build a nomogram for predicting the prognosis of patients with Stage IV ovarian cancer, and at the same time, we developed the Shiny Web application for the model to facilitate its online application.

2 Patients and methods

2.1 Patient selection

Women diagnosed with stage IV ovarian cancer between January 2010 and December 2015 were identified from the SEER database using SEER*Stat Plus version 8.4.2. Inclusion criteria were as follows: (1) Pathological diagnosis of ovarian cancer with morphological code C56.9; (2) Stage IV disease diagnosed by AJCC 7th edition criteria; (3) Diagnosed only with primary ovarian cancer. The main exclusion criteria were defined as follows: (1) Patients were followed up for less than 1 month. The flow chart of patient screening and study design is shown in Fig. S1.

2.2 Outcome indicators

The primary outcome was set as overall survival (OS). OS is defined as the time from diagnosis of stage IV ovarian cancer to death due to any cause. OS may be right-censored by the last follow-up time.

2.3 Statistical analysis

The patients were randomly divided into training and validation cohorts according to the ratio of 7:3 to ensure that outcome events were distributed randomly between the two cohorts. Use the data from the training group to analyze and build predictive models and risk classification systems. The data from the validation group were used to further validate the prediction model.

The predictive model was built as follows: First, univariate feature selection was performed. Features identified with significant difference (P ≤ 0.05) in the univariate analysis were then integrated into the multivariate Cox regression analyses with backward elimination procedure. Factors with p ≤ 0.05 were considered as independent prognostic factors in multivariate Cox regression models and were used to construct the nomogram models. For the convenience of doctors, an online model is built using Shiny. This model URLhttps://alfalfa-model.shinyapps.io/DynNomapp/. The user interface displays Alfalfa-IVOC-OS factors and coefficients, and users can input their own data to generate predictions through Alfalfa-IVOC-OS.

Concordance index (C-index) and area under curve (AUC) of the receiver operating characteristic (ROC) curve were used to evaluate discriminative ability [23]. In general, C-index and AUC values greater than 0.7 indicate a reasonable estimate [24]. The number of resampling for the bootstrap method is 1000. The calibration curve verifies the consistency between the lifetime predicted by the nomogram and the actually observed lifetime, mainly by comparing the fit degree between the calibration curve and the standard curve. The closer the calibration curve is to the standard curve, the better the prediction ability of the prediction model is. DCA is a method to evaluate the value of predictive models in practical clinical decision making. DCA was used to evaluate the clinical benefit of the model by quantifying the net benefit at different threshold probabilities [25]. The cut‐off point for risk stratifications was selected using X‐tile [26]. Kaplan-Meier method was used to calculate the survival estimates between different prognostic groups, and the long-rank test was used to compare.

All statistical analyses were performed using R software version 4.3.1. P < 0.05 was considered statistically significant.

3 Results

3.1 Characteristics of patients and diseases

A total of 6062 patients with advanced ovarian cancer were collected from the SEER database and randomly divided into a 7:3 ratio of 4251 patients in the training group and 1811 patients in the validation group. Median follow-up for the entire population was 21 months [interquartile range (IQR): 6–48]. The 3-year survival rate was 33.6 %. The 5-year survival rate was 19.5 %. Table 1 summarizes the demographic and clinical characteristics of these ovarian cancer patients. In the overall population, training and validation groups, patients <65 years and ≥65 years were roughly evenly divided. Caucasians (81.1 %) make up the majority of the overall ovarian cancer population. More than half of the patients have serous ovarian cancer. A total of 77.3 percent of patients were treated with chemotherapy. It is worth mentioning that about 40 % of patients do not undergo surgery. There were no differences in demography and clinical characteristics between the training group and the verification group (P > 0.05).Table 1 Demographic and clinical characteristics of patients with ovarian cancer.

Table 1	overall	Training group	Validation group	P value	
(N = 6062)	(N = 4251)	(N = 1811)	
Age				0.418	
 <65	2865 (47.3)	2024 (47.6)	841 (46.4)		
 ≥65	3197 (52.7)	2227 (52.4)	970 (53.6)		
Race				0.813	
 Black	612 (10.1)	436 (10.3)	176 (9.7)		
 Asian or Pacific Islander	533 (8.8)	374 (8.8)	159 (8.8)		
 White	4917 (81.1)	3441 (80.9)	1476 (81.5)		
Marital				0.501	
 Married	2732 (45.1)	1917 (45.1)	815 (45.0)		
 Unmarried	3026 (49.9)	2112 (49.7)	914 (50.5)		
 Unknown	304 (5.0)	222 (5.2)	82 (4.5)		
Tumor				0.675	
 T1	237 (3.9)	173 (4.1)	64 (3.5)		
 T2	549 (9.1)	392 (9.2)	157 (8.7)		
 T3	4087 (67.4)	2855 (67.2)	1232 (68.0)		
 TXa	1189 (19.6)	831 (19.5)	358 (19.8)		
Nodal				0.665	
 N0	2901 (47.9)	2037 (47.9)	864 (47.7)		
 N1	1875 (30.9)	1302 (30.6)	573 (31.6)		
 NX	1286 (21.2)	912 (21.5)	374 (20.7)		
Grade				0.463	
 I	63 (1.0)	43 (1.0)	20 (1.1)		
 II	248 (4.1)	181 (4.3)	67 (3.7)		
 III	1620 (26.7)	1118 (26.3)	502 (27.7)		
 IV	1124 (18.5)	807 (19.0)	317 (17.5)		
 Unknown	3007 (49.6)	2102 (49.4)	905 (50.0)		
Histology				0.471	
 non-serous	2677 (44.16)	1864 (43.85)	813 (44.89)		
 serous	3385 (55.84)	2387 (56.15)	998 (55.11)		
CA125				0.487	
 Negative/normal	140 (2.3)	104 (2.5)	36 (2.0)		
 Positiveb	4717 (77.8)	3296 (77.5)	1421 (78.5)		
 unknown	1205 (19.9)	851 (20.0)	354 (19.5)		
liver				0.889	
 No	4254 (70.2)	2989 (70.3)	1265 (69.9)		
 Unknown	356 (5.9)	251 (5.9)	105 (5.8)		
 Yes	1452 (24.0)	1011 (23.8)	441 (24.4)		
Surgery				0.239	
 No	2470 (40.7)	1711 (40.2)	759 (41.9)		
 Yes	3592 (59.3)	2540 (59.8)	1052 (58.1)		
Chemotherapy				0.206	
 No/Unknown	1377 (22.7)	985 (23.2)	392 (21.6)		
 Yes	4685 (77.3)	3266 (76.8)	1419 (78.4)		
a Tx:The primary tumor status could not be assessed.

b CA125 levels below 35U/mL are defined as positive.

3.2 Construction and verification of nomogram

In univariate regression analysis, 11 variables age, race, marital status, T stage, N stage, histological grade, histological differentiation type, CA125, liver metastasis, surgery and chemotherapy were significantly correlated with OS. In multivariate Cox regression analysis, age, race, T stage, histological grade, histological differentiation type, CA125, liver metastasis, surgery, and chemotherapy were identified as independent prognostic factors for patients with stage IV ovarian cancer (Table 2).Table 2 Univariate and multivariate Cox analyses predicting overall survival in patients with stage IV ovarian cancer.

Table 2	Univariate	Multivariate	
Character	HR（95%CI)	P value	HR（95%CI)	P value	
Age	
 <65	reference				
 ≥65	1.61 (1.52–1.7)	<0.001	1.27 (1.2–1.35)	<0.001	
Race	
 Asian or Pacific Islander	reference				
 Black	1.57(1.38–1.78)	<0.001	1.3 (1.14–1.49)	<0.001	
 White	1.26 (1.14–1.4)	<0.001	1.15 (1.03–1.27)	0.010	
Marital	
 Married	reference				
 Unmarried	1.3 (1.23–1.38)	<0.001	1.04 (0.98–1.1)	0.226	
 Unknown	1.24 (1.09–1.41)	0.001	0.99 (0.87–1.12)	0.830	
Tumor	
 T1	reference				
 T2	1.05 (0.88–1.25)	0.595	1.12 (0.94–1.34)	0.188	
 T3	1.08 (0.93–1.25)	0.31	1.36 (1.16–1.57	<0.001	
 TXa	1.93 (1.65–2.26)	<0.001	1.11 (0.95–1.31)	0.199	
Nodal	
 N0	reference				
 N1	0.93 (0.87–0.99)	0.033	1.01 (0.95–1.08)	0.671	
 NX	1.48 (1.38–1.59)	<0.001	1.07 (0.99–1.16)	0.079	
Grade	
 I	reference				
 II	1.44 (1.01–2.06)	0.043	1.66 (1.16–2.38)	0.005	
 III	1.65 (1.18–2.3)	0.003	1.95 (1.39–2.71)	<0.001	
 IV	1.48 (1.06–2.07)	0.02	1.91 (1.36–2.67)	<0.001	
 Unknown	2.64 (1.9–3.66)	<0.001	1.87 (1.34–2.6)	<0.001	
Histology	
 Non-serous	reference				
 serous	0.59 (0.56–0.62)	<0.001	0.84 (0.79–0.9)	<0.001	
CA125	
 Negative/normal	reference				
 Positiveb	1.36 (1.11–1.66)	0.003	1.42 (1.16–1.74)	<0.001	
 unknown	1.63 (1.32–2)	<0.001	1.33 (1.08–1.63)	0.008	
liver	
 No	reference				
 Unknown	1.3 (1.16–1.46)	<0.001	0.92 (0.81–1.03)	0.153	
 Yes	1.13 (1.06–1.2)	<0.001	1.11 (1.04–1.19)	0.002	
Surgery	
 No	reference				
 Yes	0.34 (0.32–0.36)	<0.001	0.43 (0.39–0.46)	<0.001	
Chemotherapy	
 No/Unknown	reference				
 Yes	0.36 (0.34–0.39)	<0.001	0.48 (0.44–0.51)	<0.001	
Abbreviations:HR hazard ratio; CI confidence interval.

a Tx:The primary tumor status could not be assessed.

b CA125 levels below 35U/mL are defined as positive.

A nomogram of a patient with stage IV ovarian cancer is constructed based on the screened variables (Fig. 1), which shows an example of using a nomogram to predict the probability of survival for a given patient. We made the nomogram into a web-based application for dynamic risk prediction. The online prediction model is applied by the coefficients of multivariate Cox regression as shown in Fig. S2. We named this survival prediction model Alfalfa-IVOC-OS (" Alfalfa " is the name of our team, which stands for happiness and health). Surgery (Range of nomogram scores: 0–100) was the most important prognostic parameter for OS, followed by histological grade (0-88), chemotherapy (0-81), T stage (0-33), CA125 (0–32), ethnicity (0-27), age (0-27), liver metastasis (0-23), and histological differentiation type (0-21). The C-index of the training and validation groups was 0.704 and 0.711, respectively. Fig. 2 shows the nomogram AUC values of two cohorts predicting 3-year and 5-year OS [training group (Fig. 2a): 3-year OS 0.745 (95%CI, 0.73–0.76); 5-year OS 0.725 (95%CI, 0.706–0.744); validation group (Fig. 2b): 3-year OS 0.754 (95%CI, 0.731–0.777); 5-year OS 0.743 (95%CI, 0.715–0.771)]. The calibration curve (Fig. 2c–f) shows that the model's prediction curve is close to the 45° standard line, with high agreement between prediction and actual survival for this population. The DCA curve representation (Fig. 2g and h) yielded significant gains in predicting 3- and 5-year OS times within most threshold probabilities, suggesting that the predictive model is clinically beneficial.Fig. 1 A constructed nomogram for prognostic prediction of a patient with stage IV ovarian cancer. A 70-year-old white-race female patient with stage IV ovarian cancer had a T3 stage, histologic grade of II, a non-serous histologic classification, a positive CA125, and no liver metastases. The sum of the predicted score points for this patient (290) is located on the total point axis and a line is drawn down along the survival axis to determine the probability of 3 year (17.7 %) overall survival.

Fig. 1

Fig. 2 The time-dependent ROC curves of the nomogram predicting OS at (a)3-year and 5-year in the training cohort, and at (b)3-year and 5-year in the validation cohort.The calibration curves for predicting patients' OS at 3- and 5-year in the (c, d) training group and (e, f) validation group, respectively. Decision curves of the nomogram predicting 3 and 5-year OS in the (g) training group and (h) validation group, respectively.

Fig. 2

3.3 Risk classification system based on nomogram

We stratified survival prognosis based on the prognostic index calculated using nomogram. According to the cutoff analysis of the training group by X-tile software (Fig. S3), the patients with stage IV ovarian cancer were divided into three risk groups: low risk (prognostic index <251), medium risk (251≤ prognostic index <348) and high risk (prognostic index ≥348). The Kaplan-Meier OS curve for the training group (Fig. 3a), validation group (Fig. 3b), and whole patients (Fig. 3c) shows large differences among the three risk groups. In the training group Fig. 3a, the median OS time for patients with stage IV ovarian cancer in the low-risk, medium-risk, and high-risk groups was 36.0 months (IQR:18–57), and 9.0 months (IQR: 3–25.5) and 2.0 months (IQR:1–7), The 3-year OS rates for the three risk groups were 49.9 % (95 % CI, 0.479–0.520), 17 % (95 % CI, 0.15–0.19), and 5.3 % (95 % CI,0.038–0.074), respectively. There were significant differences in OS among the three risk groups (P < 0.001). In addition to verifying our model, we also externally verified FIGO tumor staging, as shown in Fig. 4, which showed that the Alfalfa-IVOC-OS model [C-index:0.704(95%CI,0.694–0.713)] had a stronger predictive power than FIGO staging [C-index:0.575(95%CI,0.565–0.585)], P < 0.001.Fig. 3 Comparison of OS in the low-risk, moderate-risk, and high-risk group in the (a) training group, (b) validation group, and (c) all patients.

Fig. 3

Fig. 4 Comparison of the C-index of the Alfalfa-IVOC-OS model and the FIGO.

Fig. 4

3.4 Racial differences in stage IV ovarian cancer survival

We described the demographic and clinical characteristics of stage IV ovarian cancer by ethnicity (Table S1). White ethnicity accounts for 81.1 % of stage IV ovarian cancers, while Asian or Pacific Islander ethnicity and black ethnicity account for 8.8 % and 10.1 % of stage IV ovarian cancers, respectively. Asian or Pacific Islander patients were mostly younger than 65 years, while white patients tended to be older than 65 years. The unmarried rate was higher in black patients (69.3 %). The percentage of black patients who did not receive surgery or chemotherapy was higher than that of other races.

From Fig. 5, we find that Asian or Pacific Islander people have higher survival rates than white and black people, and white people have higher survival rates than black people. Univariate Cox regression results (Table 2) showed that compared with Asian or Pacific Islander patients, white race (HR = 1.26; 95%CI: 1.14–1.4) and black race patients (HR = 1.57; 95%CI: 1.38–1.78) had significantly worse OS (all log-rank P < 0.05). In multivariate Cox regression, after adjusting for age, marital status, T stage, N stage, histological grade, histological type, CA125 status, liver metastasis, surgery, and chemotherapy, there was a significant difference between Caucasian and Pacific Islander patients (HR = 1.15; 95%CI: 1.03–1.27) and black (HR = 1.3; 95%CI: 1.14–1.49) The prognosis was still significantly worse.Fig. 5 Kaplan-Meier curves in ovarian cancer patients of different races.

Fig. 5

4 Discussion

Stage IV ovarian cancer is a malignant tumor with poor prognosis and high mortality. How to improve the prognosis of this disease has always been a clinical problem to be solved. This study analyzed the data of 6062 patients with stage IV ovarian cancer. Through univariate and multivariate Cox regression analysis, 9 clinical characteristics, including age, race, T stage, histological grade, tissue type, CA125, liver metastasis, surgery, and chemotherapy, were identified as significant factors affecting the prognosis of stage IV ovarian cancer. The survival prediction model (Alfalfa-IVOC-OS) was constructed. The model predicts survival in patients with stage IV ovarian cancer and provides a personalized disease-related risk assessment. Through C-index, ROC curve, calibration curve, DCA curve and other verification, the model has good predictive ability and clinical practicability, and it is expected to provide certain reference for the future clinical development of treatment strategies for stage IV OC patients.

How to choose treatment is the most controversial issue in stage IV OC patients and needs urgent research. This study found that surgical treatment was the first important prognostic factor in patients with stage IV OC, and surgical treatment had a survival benefit in patients with stage IV OC. Some retrospective studies [[27], [28], [29]] also supported this result. This suggests that the presence of distant metastases in ovarian cancer should not deter surgeons from performing surgical treatments if the patient can tolerate them. Cytoreductive surgery is the standard treatment for patients with stage IV ovarian cancer. Due to the improvement of perioperative nursing and gynecological surgical skills, better results have been achieved in terms of optimal cell reduction [30]. Resection should be limited to cases where optimal residual tumor can be achieved, but selection is a challenge. The challenge in the decision-making process is the balance between the expected improvement in survival and the expected surgical morbidity and mortality. It remains to be noted that surgical treatment is a complex issue that requires multidisciplinary evaluation [30]. Chemotherapy was also an important independent prognostic factor in our study, and failure to receive chemotherapy was an independent risk factor. Chemotherapy is significantly associated with prognosis, which is of great value in improving survival outcomes. Neoadjuvant chemotherapy can reduce the size of the tumor, thereby creating conditions for surgery. While different patients have different sensitivities to chemotherapy, Makar et al. concluded that non-serous tumors with good prognosis have lower chemical sensitivities, and neoadjuvant chemotherapy-interval tumor reduction surgery (NACT-IDS) is preferred for patients with advanced serous ovarian cancer with severe comorbidities or low physical status. Stage IV ovarian cancer is not a contraindication for primary tumor reduction surgery (PDS), and the absence of surgery leads to a poor prognosis [5]. Histological grade was the second most important prognostic factor in the study. The results of Kezia Gaitskell et al. were consistent with our findings that, after adjusting for other factors, tumor tissue type and grade were still predictors of survival, which further demonstrated the biological differences between ovarian cancer tissue types [31] and also demonstrated the importance of histological grade in predicting the prognosis of stage IV ovarian cancer. Age is a controversial issue, and two retrospective studies [32,33] have shown that older patients are a prognostic risk factor for stage IV ovarian cancer. Another retrospective study [34] found that age was not associated with ovarian cancer survival. Stage IV ovarian cancer over 65 years of age was found to be an independent risk factor. These results suggest that older patients with ovarian cancer have a worse prognosis. This may be due to the fact that older age is associated with a higher incidence of toxicity in chemotherapy treatment [[35], [36], [37]]. Studies have shown [30] that the combined effect of increasing age and advanced clinical stage factors significantly increases the risk of perioperative death. The elderly often suffer from comorbidities. Patient comorbidities have a strong influence on mortality and morbidity. Older patients often ignore the non-specific symptoms of ovarian cancer, possibly attributing these to normal signs of aging or pre-existing conditions. An increased state of vulnerability associated with aging, in frail patients, affects complication rates and survival. This suggests that cancer care should be taken more seriously with older patients. In our results, lymph node metastasis was not included in the construction of prognostic models. However, studies have shown [38] that the prognostic effect of lymph node metastasis on survival has been clearly demonstrated. We considered that the advanced stage of the disease masked the effect of lymph node metastasis on the prognosis. In our study, we included the cancer antigen CA125 as a prognostic marker, and previous data suggest that more than 85 % of patients with stage IV ovarian cancer have elevated CA125 levels. Our study also confirms that positive CA125 is an independent risk factor for stage IV ovarian cancer disease. It is worth noting that some studies have found that the preoperative serum CA125 threshold of 500IU/ml can predict the success of surgery [39], which once again confirms the importance of CA125, and further studies are expected.

Notably, our study found that Asian or Pacific Islander people have significantly better OS than other ethnic groups, a finding that has not previously been reported. The black race has a poorer prognosis. Our analysis found that blacks were less likely to receive both surgery and chemotherapy, which may be associated with a poorer prognosis. Another data suggests that socioeconomic factors and access to care may be responsible for lower ovarian cancer survival rates among African American women [40]. Therefore, highlighting the differences between ethnic groups in ovarian cancer and addressing the differences in future research is a key part of improving cancer care. The nomogram integrates multiple factors (including demographic and clinicopathological features) into a quantitative model that is superior to some traditional staging systems, such as the American Joint Committee on Cancer (AJCC) and FIGO staging system, in predicting prognosis and making clinical decisions [41,42]. We divided patients into low, medium and high-risk groups according to their total score on the nomogram. Kaplan-Meier method showed significant differences in OS among the three risk groups. The ability to recognize moderate and high-risk groups is better, and special attention should be paid to patients with scores greater than 348, which have a poorer prognosis. In this study, we use the R package Shiny to transfer the nomogram model to a user-friendly Web application. Recently, Takeshi Emura [20] developed a Shiny based Web application for dynamically predicting the risk of death from breast cancer. Clearly, this type of dynamic prediction tool could facilitate the development of personalized medicine, enabling clinicians to leverage powerful predictive algorithms without detailed knowledge of R. In summary, we analyzed population data from 18 different regions in the SEER database. The C-index, calibration curve and DCA curve were calculated using bootstrap and cross-validation methods, and were replicated well in the verification group. This model can more accurately identify patients with poor prognosis, for whom more attention is often needed. This tool may play a role in optimizing treatment management for patients with ovarian cancer. The patients who participated in the analysis of the nomogram represented the majority of patients with stage IV ovarian cancer, ensuring the value of this predictive model in clinical practice. To our knowledge, this study is the first to date to analyze stage IV ovarian cancer with large retrospective data. The findings provide new insights into the prognostic factors of stage IV ovarian cancer and the epidemiological and clinical characteristics of different ethnic groups. The predictive nomogram developed in this study accurately predicted tumor survival.

Our study also has some limitations, first of all, the SEER data did not provide specific data on chemotherapy drugs and dosages, and we could not further explore the impact of chemotherapy. Performance status is one of the important prognostic factors, but this variable cannot be analyzed because there is no information on Performance status in the database. Second, our study still requires multi-center clinical validation to evaluate the clinical benefit of our nomogram. Finally, because the SEER database is registered in the United States. Therefore, caution is needed when applying the nomogram to the demographics of other countries. The results of the epidemiological analyses in this paper may not suitable in other countries. In the future, we will design a prospective study to verify the model's performance and benefits in the real world.

Our nomogram can be used to predict the probability of survival in patients with stage IV ovarian cancer and help clinicians make better treatment decisions.

Funding

Funds for the Innovation of Science and Technology of Fujian Maternity and Child Health Hospital, China (YCXY 23-02 ). Fund for Natural Science Foundation of Fujian Province, China (Grant number 2023J011219 ).

Data availability statement

The data of this study are available in the SEER database (https://seer.cancer.gov/). The access number for SEER database is 24026-Nov2021. The data included in this study will be made available on request.

Ethical approval

The Institutional Review Board of Fujian Maternal and Child Health Hospital waived informed consent for this study because the data in the SEER database were de-identified and publicly available after obtaining permission to use them. We confirm that this study was conducted in accordance with the Declaration of Helsinki.

CRediT authorship contribution statement

Guilan Wu: Writing – original draft, Software, Data curation. Jiana Chen: Writing – review & editing, Supervision, Project administration. Peiguang Niu: Writing – review & editing, Supervision, Project administration. Xinhai Huang: Writing – review & editing, Supervision, Project administration. Yunda Chen: Data curation. Jinhua Zhang: Writing – review & editing, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Acknowledgement

None.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36549.
==== Refs
References

1 Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2019 CA Cancer J Clin 69 1 2019 7 34 10.3322/caac.21551 30620402
2 Peres L.C. Cushing-Haugen K.L. Köbel M. Invasive epithelial ovarian cancer survival by histotype and disease stage J Natl Cancer Inst 111 1 2019 60 68 10.1093/jnci/djy071 29718305
3 Vogel T.J. Jeon C. Karlan B. Walsh C. Digoxin therapy is not associated with improved survival in epithelial ovarian cancer: a SEER-Medicare database analysis Gynecol. Oncol. 140 2 2016 285 288 10.1016/j.ygyno.2015.12.003 26691221
4 Doll K.M. Pinheiro L.C. Reeve B.B. Pre-diagnosis health-related quality of life, surgery, and survival in women with advanced epithelial ovarian cancer: a SEER-MHOS study Gynecol. Oncol. 144 2 2017 348 353 10.1016/j.ygyno.2016.12.005 27988047
5 Makar A.P. Tropé C.G. Tummers P. Denys H. Vandecasteele K. Advanced ovarian cancer: primary or interval debulking? Five categories of patients in view of the results of randomized trials and tumor biology: primary debulking surgery and interval debulking surgery for advanced ovarian cancer Oncol. 21 6 2016 745 754 10.1634/theoncologist.2015-0239
6 Rauh-Hain J.A. Rodriguez N. Growdon W.B. Primary debulking surgery versus neoadjuvant chemotherapy in stage IV ovarian cancer Ann. Surg Oncol. 19 3 2012 959 965 10.1245/s10434-011-2100-x 21994038
7 Curtin J.P. Malik R. Venkatraman E.S. Barakat R.R. Hoskins W.J. Stage IV ovarian cancer: impact of surgical debulking Gynecol. Oncol. 64 1 1997 9 12 10.1006/gyno.1996.4550 8995540
8 Bristow R.E. Montz F.J. Lagasse L.D. Leuchter R.S. Karlan B.Y. Survival impact of surgical cytoreduction in stage IV epithelial ovarian cancer Gynecol. Oncol. 72 3 1999 278 287 10.1006/gyno.1998.5145 10053096
9 Wang R. Xie G. Shang L. Development and validation of nomograms for epithelial ovarian cancer: a SEER population-based, real-world study Future Oncol. 17 8 2021 893 906 10.2217/fon-2020-0531 33533669
10 Song Z. Zhou Y. Bai X. Zhang D. A practical nomogram to predict early death in advanced epithelial ovarian cancer Front. Oncol. 11 2021 655826 10.3389/fonc.2021.655826
11 Dabi Y. Huchon C. Ouldamer L. Patients with stage IV epithelial ovarian cancer: understanding the determinants of survival J. Transl. Med. 18 1 2020 134 10.1186/s12967-020-02295-y 32293460
12 Sakhuja S. Yun H. Pisu M. Akinyemiju T. Availability of healthcare resources and epithelial ovarian cancer stage of diagnosis and mortality among Blacks and Whites J. Ovarian Res. 10 1 2017 57 10.1186/s13048-017-0352-1 28830564
13 Taylor J.S. He W. Harrison R. Disparities in treatment and survival among elderly ovarian cancer patients Gynecol. Oncol. 151 2 2018 269 274 10.1016/j.ygyno.2018.08.041 30253875
14 Wu J. Sun H. Yang L. Improved survival in ovarian cancer, with widening survival gaps of races and socioeconomic status: a period analysis, 1983-2012 J. Cancer 9 19 2018 3548 3556 10.7150/jca.26300 30310512
15 Zeng C. Wen W. Morgans A.K. Pao W. Shu X.O. Zheng W. Disparities by race, age, and sex in the improvement of survival for major cancers: results from the national cancer institute surveillance, epidemiology, and end results (SEER) program in the United States, 1990 to 2010 JAMA Oncol. 1 1 2015 88 96 10.1001/jamaoncol.2014.161 26182310
16 Balachandran V.P. Gonen M. Smith J.J. DeMatteo R.P. Nomograms in oncology: more than meets the eye Lancet Oncol. 16 4 2015 e173 e180 10.1016/s1470-2045(14)71116-7 25846097
17 Yang J. Pan Z. Zhou Q. Nomogram for predicting the survival of patients with malignant melanoma: a population analysis Oncol. Lett. 18 4 2019 3591 3598 10.3892/ol.2019.10720 31516573
18 Pan Y.X. Chen J.C. Fang A.P. A nomogram predicting the recurrence of hepatocellular carcinoma in patients after laparoscopic hepatectomy Cancer Commun. 39 1 2019 55 10.1186/s40880-019-0404-6
19 Kong J. Zheng J. Cai J. A nomogram for individualized estimation of survival among adult patients with adrenocortical carcinoma after surgery: a retrospective analysis and multicenter validation study Cancer Commun. 39 1 2019 80 10.1186/s40880-019-0426-0
20 Emura T. Michimae H. Matsui S. Dynamic risk prediction via a Joint frailty-copula model and IPD meta-analysis: building web applications Entropy 24 5 2022 10.3390/e24050589
21 Kuo T.M. Mobley L.R. How generalizable are the SEER registries to the cancer populations of the USA? Cancer Causes Control 27 9 2016 1117 1126 10.1007/s10552-016-0790-x 27443170
22 Hayat M.J. Howlader N. Reichman M.E. Edwards B.K. Cancer statistics, trends, and multiple primary cancer analyses from the Surveillance, Epidemiology, and End Results (SEER) Program Oncol. 12 1 2007 20 37 10.1634/theoncologist.12-1-20
23 Wu J. Zhang H. Li L. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: a population-based analysis Cancer Commun. 40 7 2020 301 312 10.1002/cac2.12067
24 Grant S.W. Collins G.S. Nashef S.A.M. Statistical Primer: developing and validating a risk prediction model Eur. J. Cardio. Thorac. Surg. 54 2 2018 203 208 10.1093/ejcts/ezy180
25 Van Calster B. Wynants L. Verbeek J.F.M. Reporting and interpreting decision curve analysis: a guide for investigators Eur. Urol. 74 6 2018 796 804 10.1016/j.eururo.2018.08.038 30241973
26 Camp R.L. Dolled-Filhart M. Rimm D.L. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization Clin. Cancer Res. 10 21 2004 7252 7259 10.1158/1078-0432.Ccr-04-0713 15534099
27 Lim M.C. Chang S.J. Park B. Survival after hyperthermic intraperitoneal chemotherapy and primary or interval cytoreductive surgery in ovarian cancer: a randomized clinical trial JAMA Surg 157 5 2022 374 383 10.1001/jamasurg.2022.0143 35262624
28 Tate S. Nishikimi K. Matsuoka A. Otsuka S. Shozu M. Highly aggressive surgery benefits in patients with advanced ovarian cancer Anticancer Res. 42 7 2022 3707 3716 10.21873/anticanres.15860 35790254
29 Sørensen S.M. Høgdall C. Mosgaard B.J. Residual tumor and primary debulking surgery vs interval debulking surgery in stage IV epithelial ovarian cancer Acta Obstet. Gynecol. Scand. 101 3 2022 334 343 10.1111/aogs.14319 35187660
30 Di Donato V. Giannini A. D'Oria O. Hepatobiliary disease resection in patients with advanced epithelial ovarian cancer: prognostic role and optimal cytoreduction Ann. Surg Oncol. 28 1 2021 222 230 10.1245/s10434-020-08989-3 32779050
31 Gaitskell K. Hermon C. Barnes I. Ovarian cancer survival by stage, histotype, and pre-diagnostic lifestyle factors, in the prospective UK Million Women Study Cancer Epidemiol 76 2022 102074 10.1016/j.canep.2021.102074
32 Ekmann-Gade A.W. Høgdall C.K. Seibæk L. Noer M.C. Fagö-Olsen C.L. Schnack T.H. Incidence, treatment, and survival trends in older versus younger women with epithelial ovarian cancer from 2005 to 2018: a nationwide Danish study Gynecol. Oncol. 164 1 2022 120 128 10.1016/j.ygyno.2021.10.081 34716025
33 Zhao L. Yu P. Zhang L. A nomogram to predict the cancer-specific survival of stage II-IV Epithelial ovarian cancer after bulking surgery and chemotherapy Cancer Med. 10 13 2021 4344 4355 10.1002/cam4.3980 34057318
34 Wang B. Wang S. Ren W. Development and validation of a nomogram to predict survival outcome among epithelial ovarian cancer patients with site-distant metastases: a population-based study BMC Cancer 21 1 2021 609 10.1186/s12885-021-07977-4 34034697
35 Shayne M. Culakova E. Poniewierski M.S. Dose intensity and hematologic toxicity in older cancer patients receiving systemic chemotherapy Cancer 110 7 2007 1611 1620 10.1002/cncr.22939 17705197
36 Muss H.B. Berry D.A. Cirrincione C. Toxicity of older and younger patients treated with adjuvant chemotherapy for node-positive breast cancer: the Cancer and Leukemia Group B Experience J. Clin. Oncol. 25 24 2007 3699 3704 10.1200/jco.2007.10.9710 17704418
37 Hurria A. Fleming M.T. Baker S.D. Pharmacokinetics and toxicity of weekly docetaxel in older patients Clin. Cancer Res. 12 20 Pt 1 2006 6100 6105 10.1158/1078-0432.Ccr-06-0200 17062686
38 Benedetti Panici P. Giannini A. Fischetti M. Lecce F. Di Donato V. Lymphadenectomy in ovarian cancer: is it still justified? Curr. Oncol. Rep. 22 3 2020 22 10.1007/s11912-020-0883-2 32036457
39 Merlo S. Besic N. Drmota E. Kovacevic N. Preoperative serum CA-125 level as a predictor for the extent of cytoreduction in patients with advanced stage epithelial ovarian cancer Radiol. Oncol. 55 3 2021 341 346 10.2478/raon-2021-0013 33675192
40 Peres L.C. Schildkraut J.M. Racial/ethnic disparities in ovarian cancer research Adv. Cancer Res. 146 2020 1 21 10.1016/bs.acr.2020.01.002 32241384
41 Wang C. Yang C. Wang W. A prognostic nomogram for cervical cancer after surgery from SEER database J. Cancer 9 21 2018 3923 3928 10.7150/jca.26220 30410596
42 Pu N. Li J. Xu Y. Comparison of prognostic prediction between nomogram based on lymph node ratio and AJCC 8th staging system for patients with resected pancreatic head carcinoma: a SEER analysis Cancer Manag. Res. 10 2018 227 238 10.2147/cmar.S157940 29440932
