Prognostic factor analysis and nomogram construction for elderly patients with stages III and IV epithelial ovarian cancer: a study based on the SEER database

Introduction

Ovarian cancer (OC) is one of the most lethal gynecological malignancies affecting women worldwide, with approximately 300,000 new cases and nearly 200,000 deaths reported each year (1,2). Among these, epithelial ovarian cancer (EOC) is the most prevalent pathological subtype, accounting for over 90% of all ovarian cancer cases, particularly among postmenopausal women (3). Despite the advancements in new medical technologies and therapeutic agents in recent years, the 5-year survival rate for EOC remains below 50% (4,5). Currently, EOC treatment adheres to staging criteria set by the International Federation of Gynecology and Obstetrics (FIGO) and the American Joint Committee on Cancer (AJCC). However, the increasing elderly population, especially those diagnosed with advanced (stages III and IV) EOC, poses a growing public health challenge (6-8). Elderly EOC patients often present with comorbidities, immune function decline, and organ dysfunction, which not only diminish their tolerance to treatment but also result in poorer clinical outcomes for younger patients (9). Treatment for elderly patients is generally more conservative and relies on individual physician experience, lacking standardized treatment guidelines (6). This limitation underscores the necessity for individualized survival prediction models for elderly EOC patients, particularly those in stages III and IV, to support clinical decision-making and optimize treatment strategies.

Nomograms, as a visual prognostic tool, have been widely used to predict survival rates in cancer patients, demonstrating good readability and accuracy (10-12). Unlike FIGO and AJCC staging systems, nomogram models provide personalized survival predictions for individual patients. However, existing nomograms primarily focus on younger patients, postoperative patients, or those with metastatic EOC (13-15), without a tailored nomogram model targeting elderly EOC patients in stages III and IV. At these advanced stages, EOC often involves spread to the peritoneum or distant organs, resulting in a relatively poor prognosis and limited treatment options. Stage III EOC is characterized by tumor spread beyond the pelvis or to the peritoneum, while stage IV denotes distant metastasis (16). These patients frequently need more intricate treatment plans, involving a mix of surgery, chemotherapy, and targeted therapy, but they are at a heightened risk of treatment-related complications due to their physiological conditions (17). Therefore, this study aimed to construct a nomogram model for predicting overall survival (OS) and cancer-specific survival (CSS) in elderly EOC patients at stages III and IV with data from the Surveillance, Epidemiology, and End Results (SEER) database. We will evaluate the model’s discriminative ability and clinical utility using the C-index, calibration curves, and decision curve analysis (DCA). This research is underway to provide precise survival predictions for elderly EOC patients in stages III and IV, optimizing treatment decisions and improving clinical outcomes. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2129/rc).

Methods

Patient selection

Relevant data were sourced from the SEER database (https://seer.cancer.gov/data-software/) where data of 17 cohorts classified by cancer registries from 2000 to 2019 were available. This study used SEERStat software (SEERStat version 8.4.2) and did not require medical ethics review or informed consent. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Inclusion criteria: (I) patients must have a diagnosis of EOC with the primary site recorded as C56.9; the relevant ICD-O-3 codes encompassed epithelial neoplasms (8010–8049), adenomas and adenocarcinomas (8140–8389), and cystic, mucinous, and serous neoplasms (8440–8499); (II) participants were aged 60 years or older at diagnosis and classified as having stage III or IV EOC; (III) a pathologically confirmed diagnosis was mandatory, and only patients with a single primary malignant tumor and active follow-up were included. Cases were excluded based on the following: missing data on age, race, marital status, or confirmation of diagnosis; survival months equal to zero or unknown; diagnoses established solely through death certificates or autopsy; unknown metastatic status; unclear cancer antigen 125 (CA-125) status; unknown tumor grade or size; unknown AJCC stage; missing T/N/M classification; and unclear chemotherapy records. The selection process for this study is illustrated in Figure 1.

Figure 1 Flow chart of stage III and IV elderly EOC patients based on the inclusion and exclusion criteria. AJCC, American Joint Committee on Cancer; CA-125, cancer antigen 125; EOC, epithelial ovarian cancer; N, node; NA, not available; SEER, Surveillance, Epidemiology, and End Results; T, tumor.

Variable collection

All patients were randomly assigned into training and validation groups in a 7:3 ratio by R software (R version 4.3.1). A training dataset was used to primarily develop the nomogram, and a validation dataset was employed for evaluation and validation. 14 clinical and pathological variables relevant to prognosis with OS and CSS as outcomes were considered. Variables included tumor grade, age, marital status, race, AJCC stage, tumor-node-metastasis (TNM) staging, laterality, surgical method, chemotherapy status, radiotherapy status, tumor size, and CA-125 status. All of these variables were selected based on their potential relationship to the prognosis of EOC and were thus included in the model construction. The stratification of age was based on the cutoff value identified through ROC curve analysis, with details presented in Figure S1.

Statistical analysis

All statistical analyses were performed using R software (R version 4.3.1). Baseline characteristics of the training and validation groups were compared using the Chi-square test and Fisher’s exact test. Univariate Cox regression analysis (R package: survival) was employed to determine the relationship between clinical and pathological variables and prognosis. Subsequently, multivariate Cox regression analysis (R package: survival) was conducted to identify independent prognostic factors for elderly EOC patients (P<0.05). Nomograms were constructed based on these independent prognostic variables (R package: rms). The concordance index (C-index), receiver operating characteristic (ROC) curves (R packages: riskRegression, survival), calibration curves (R package: rms), and DCA (R packages: rmda, rms, dcurves) were calculated. The C-index, ROC curves, and calibration curves were used to evaluate the discriminative ability and predictive efficiency of the nomogram. DCA was utilized to assess the predictive capability and effectiveness of the nomogram. Additionally, survival differences among different risk stratification groups were compared using the log-rank test and Kaplan-Meier plots (R packages: survival, survminer).

Results

Clinical and pathological characteristics of patients

Among 3,749 included patients, 2,626 were assigned to the training set and 1,123 to the validation set. OS data indicated that 928 patients (24.75%) were alive, while 2,821 patients (75.25%) had died, without a significant difference between the training and validation groups (P=0.84). The median survival duration was 41 months [interquartile range (IQR), 20–70 months], with no significant difference observed between the groups (P=0.90). The median age stood at 69 years (IQR, 64–75 years), with no notable age disparity between sets (P=0.64). The racial distribution of the included patients showed that 87.12% were White and 5.63% were Black, with no significant racial disparity (P=0.22). Elevated CA-125 levels were present in 95.17%, without significant difference (P=0.90). Tumor grading showed that 47.27% were grade III and 42.25% were grade IV (P=0.08). Most patients were classified as AJCC stage III (71.38%), while 28.62% were stage IV (P=0.27) (Table 1).

Survival analysis

Univariate Cox analysis

In the analysis of OS and CSS, mortality among patients older than 72 years is substantially higher [hazard ratio (HR) =1.42, 95% confidence interval (CI): 1.29–1.56, P<0.001; HR =1.48, 95% CI: 1.34–1.63, P<0.001]. In addition, Black patients showed a higher mortality in reference to White patients (HR =1.32, P=0.04; HR =1.25, P=0.03). A lower mortality was found in married patients (HR =0.85, P=0.001; HR =0.76, P=0.001). Elevated CA-125 levels were associated with increased mortality (HR =1.54, P=0.002; HR =1.46, P=0.001) (Table 2). Mortality progressively increased with tumor grades II, III, and IV (P<0.05). Patients with bilateral ovarian tumors faced a higher risk than those with unilateral tumors (HR =1.16, P=0.009; HR =1.20, P=0.001) (Table 2). The mortality risk for stage IV patients, as assessed by the AJCC staging system, was significantly greater than that for stage III patients (HR =1.48, P<0.001; HR =1.52, P<0.001). Oophorectomy and other surgical methods substantially lowered mortality (P<0.001). Patients who underwent chemotherapy exhibited a significantly reduced risk of death (HR =0.54, P<0.001; HR =0.51, P<0.001) (Table 2).

Multivariate Cox analysis

In the multivariate Cox regression analysis, several variables were found influential on OS and CSS including CA-125 level, age, AJCC stage, marital status, T stage, lung metastasis status, surgical method, chemotherapy, tumor grade, and location. Less desired OS (HR =1.42, P<0.001) and CSS (HR =1.45, P<0.001) were observed in populations over 72 years in relation to those of who aged 60–72 years. Married patients exhibited significantly better survival compared to unmarried patients, with OS (HR =0.81, P=0.001) and CSS (HR =0.75, P=0.001) indicating a protective effect. Elevated CA-125 levels were associated with lower OS (HR =1.39, P=0.005) and CSS (HR =1.34, P=0.01) compared to normal levels (Table 3). Advanced tumor grades (II, III, IV) were significantly associated with poorer survival outcomes, with grade III patients showing significantly lower OS (HR =2.18, P<0.001) and CSS (HR =2.02, P=0.008) in contrast to those of grade I patients. Bilateral tumor patients also had lower survival rates, with OS (HR =1.17, P=0.006) and CSS (HR =1.22, P=0.001) indicating increased risk (Table 3). Additionally, patients at AJCC stage IV, T3 stage, and those with lung metastasis exhibited significantly reduced OS. Conversely, OS (HR =0.33, P<0.001) and CSS (HR =0.28, P<0.001) of patients undergoing oophorectomy and chemotherapy suggested significantly improved survival rates, while those who did not receive surgery or chemotherapy had poorer prognoses (Table 3).

Kaplan-Meier analysis

To assess the impact of different chemotherapy options on survival status, Kaplan-Meier survival curve analysis (Figure 2) was performed. The results indicated that patients who received no chemotherapy exhibited markedly lower OS (log-rank P<0.001; Figure 2A) and CSS (log-rank P<0.001; Figure 2B) rates compared to those who opted for it, with these differences also being statistically significant in the log-rank test.

Figure 2 Kaplan-Meier survival analysis curve for comparing OS (A) and CSS (B) between different chemotherapy groups. CSS, cancer-specific survival; OS, overall survival.

In Figure 3, a comparable Kaplan-Meier analysis was performed to assess the impact of different surgical methods on survival situations. The results indicated that patients who received no surgical treatment showed substantially lower OS rates than those who underwent surgery, particularly those who received oophorectomy, with the log-rank test confirming these differences as statistically significant (log-rank P<0.001; Figure 3A). Moreover, CSS results mirrored those of OS, demonstrating that patients who chose surgical treatment showed higher survival rates (log-rank P<0.001; Figure 3B). Additionally, supplementary Kaplan-Meier analyses based on other clinical variables are presented in Figures S2-S6.

Figure 3 Kaplan-Meier survival analysis curve for comparing OS (A) and CSS (B) between different surgery methods. CSS, cancer-specific survival; OS, overall survival.

Development and validation of the nomogram

Independent prognostic factors yielded by the multivariate Cox analysis were employed to predict 1-, 3-, and 5-year OS and CSS. Each variable is assigned a corresponding score based on its impact on survival with a higher score indicating a poorer prognosis. For instance, factors such as age over 72, elevated CA-125 levels, bilateral tumors, and unmarried status contribute to a higher total score, reflecting a worse survival prognosis. Conversely, surgical methods and chemotherapy significantly improve prognosis, as patients undergoing oophorectomy or chemotherapy receive lower scores, indicating higher survival rates. A total score can be calculated, enabling estimation of the patient’s survival probabilities at different lengths (Figure 4A,4B).

Figure 4 Nomograms to predict 1-, 3-, and 5-year OS (A) and CSS (B) for stage III and IV elderly patients with EOC. AJCC, American Joint Committee on Cancer; CA-125, cancer antigen 125; CSS, cancer-specific survival; EOC, epithelial ovarian cancer; OS, overall survival; T, tumor.

To evaluate the overall performance of the two nomograms, C-index, ROC curves, and calibration curve tests were conducted. The ROC curve evaluation demonstrated good discriminative ability for both OS and CSS across the training and validation sets. Figure 5A,5B display the ROC curves for OS in the training and validation sets, respectively, with the area under the ROC curve (AUC) of 0.77, 0.68, and 0.66 for 1-, 3-, and 5-year OS in the training set, and 0.74, 0.69, and 0.70 in the validation set. Similarly, Figure 5C,5D present the ROC curves for CSS in the training and validation sets, showing AUC values of 0.77, 0.68, and 0.67 for 1-, 3-, and 5-year CSS in the training set, and 0.76, 0.66, and 0.67 in the validation set. Overall, the model demonstrated strong performance in predicting short-term (1-year) and mid-term (3-year) survival, with a slight decrease in AUC for long-term (5-year) prediction. However, it remained within an acceptable range, indicating good accuracy in survival prognosis (Figure 5). The C-index for predicting OS in the training cohort was 0.641, while the C-index for CSS was 0.643. Additionally, calibration curves for both the training and validation cohorts indicated that the observed survival rates aligned well with those predicted by the nomogram (Figure 6).

Figure 5 ROC curve analysis for OS (A,B) and CSS (C,D) in the training and testing sets. The models display AUC values for 1-, 3-, and 5-year survival. AUC, area under the curve; CI, confident interval; CSS, cancer-specific survival; OS, overall survival; ROC, receiver operating characteristic.

Figure 6 Calibration curve analysis for OS (A,B) and CSS (C,D) in the training and testing sets. The models display AUC values for 1-, 3-, and 5-year survival. AUC, area under the curve; CSS, cancer-specific survival; OS, overall survival.

Furthermore, DCA was performed to assess the clinical utility and net benefit of the nomogram model. The DCA curves demonstrated that, compared to treating all patients (red line) or treating none (green line), the nomogram model (blue line) provided higher net benefits across a wide range of threshold probabilities in both the training and validation sets. This trend was consistent for OS (Figure 7A-7F) and CSS (Figure 7G-7L) predictions at different time points, suggesting that the nomogram model substantially enhances predictive accuracy and offers practical clinical benefits, serving as an important adjunct tool for clinical decision-making (Figure 7).

Figure 7 DCA of the OS-associated and CSS-associated nomograms. DCA curves of 1-, 3-, and 5-year OS in the training cohort (A-C) and validation cohort (D-F). DCA curves of 1-, 3-, and 5-year CSS in the training group (G-I) and validation group (J-L). CSS, cancer-specific survival; DCA, decision curve analysis; OS, overall survival.

Discussion

The prognosis of elderly patients with EOC is of crucial clinical importance. The aging population means an increasing number of elderly women are facing the threat of ovarian cancer, while relevant therapies and prognosis assessments for elderly patients remain major challenges in the field of gynecological oncology. Compared to younger patients, elderly patients often encounter more challenges during treatment, including poorer treatment tolerance, more comorbidities, and declining immune function. This study, based on the SEER database, analyzed the prognostic risk factors for elderly EOC patients in stages III and IV and constructed corresponding nomograms to provide strong decision-making support for individualized treatment for these patients.

Age was identified as an independent risk factor affecting the prognosis of EOC patients, in this study. As patients age, the OS and CSS of elderly patients significantly decline (1), a phenomenon influenced by multiple factors. Firstly, elderly patients often have various underlying health issues, such as cardiovascular diseases and diabetes, which can compromise their tolerance to treatments like surgery and chemotherapy (18). Secondly, immune senescence is a common phenomenon in the elderly population, weakening the body’s resistance to cancer (9,19). Additionally, the physical and nutritional status of elderly patients is often poorer, which affects their recovery from invasive treatments (20).

In our study, both univariate and multivariate Cox regression analyses showed that age is a significant factor affecting OS and CSS in EOC patients. The survival rate of elderly patients is significantly lower than that of younger patients, which aligns with results from previous studies. Research indicates that elderly patients are more likely to experience treatment interruptions or poor outcomes due to comorbidities and lower treatment tolerance, thereby impacting prognosis (21). Thus, for elderly EOC patients, early intervention to mitigate the effects of comorbidities and improve treatment adherence is particularly important. Racial differences are another risk factor identified in this study as non-white patients had significantly lower OS and CSS than those of white patients. This disparity may stem from various factors, including socioeconomic conditions, access to healthcare resources, and cultural beliefs affecting treatment adherence (22). It has been noted that non-white patients are less likely to receive standard treatments, particularly surgical and chemotherapy, which may contribute to their poorer prognosis (23).

Surgery and chemotherapy remain the main treatment options for EOC, especially in advanced (stages III and IV) stages, where combined treatment (surgery plus chemotherapy) has become the clinical standard (24,25). In our study, both surgery and chemotherapy were significantly associated with OS and CSS in elderly EOC patients. Those receiving both treatments exhibited notably higher survival rates in reference to those who received neither or either of the treatments. However, these interventions are not universally effective for all patients. In high-risk cases, the combination can significantly improve OS and CSS, whereas the efficacy of chemotherapy may be limited in low-risk patients (26). This indicates that for low-risk patients, local excision should be more carefully considered as chemotherapy may impose additional burdens. For high-risk patients, chemotherapy can provide an extra survival advantage when combined with surgery. Therefore, individualized treatment plans should be tailored to different risk levels to optimize prognosis.

Currently, there is considerable debate about the appropriateness of surgery and chemotherapy for elderly EOC patients. While these treatments have proven effective for younger individuals, elderly patients may struggle with the physiological demands of invasive procedures (27). Research indicates that elderly patients aged 80 years and above with stage III EOC undergoing neoadjuvant chemotherapy experienced fewer perioperative complications, but this does not translate into significant improvements in OS or CSS (28). Therefore, for elderly patients, a more cautious evaluation of the treatment options is warranted to balance treatment efficacy and potential risks. In our study, radiotherapy did result in significant improvement in OS or CSS for elderly EOC patients. Unlike those standard approaches in EOC treatment, radiotherapy is mainly used for symptom relief or control of local recurrence (29). Considering the compromised physiological functions of the elderly, more side effects from radiotherapy with limited benefits might be another issue. Thus, the application of radiotherapy in elderly EOC patients, especially in advanced cases, should be approached with caution, as its efficacy often pales in comparison to that of surgery and chemotherapy (30).

With data from the SEER database, two nomograms were developed to predict the OS and CSS of elderly EOC patients. Nomograms are an intuitive prognostic tool that can generate total scores for individual patients based on multiple prognostic factors, thereby predicting their survival probabilities at 1, 3, and 5 years (31). During validation, our nomogram models demonstrated good predictive capabilities, with high AUC values for the ROC, indicating excellent discrimination. In contrast to the traditional AJCC staging system, our nomogram models provide more accurate risk stratification. The AJCC staging system primarily relies on anatomical tumor characteristics, overlooking individual patient characteristics such as age, marital status, and race. By integrating additional prognostic factors, our nomogram can provide clinicians with a more individualized prognostic assessment tool, aiding them in making more informed treatment decisions.

There are still some limitations in this study. Firstly, the SEER database failed to offer detailed treatment information, including specific chemotherapy regimens, postoperative complications, and residual tumor size. These factors may significantly impact prognosis but were not included in our study. Secondly, the retrospective analysis nature means a potential for selection bias, highlighting the need for further validation through prospective research. Additionally, more subjective factors including life quality and functional status were not adequately considered in this study. Treatment plans for elderly EOC patients should not only focus on extending survival but also improving quality of life. Future research should further explore the impact of these factors on prognosis to develop more comprehensive treatment strategies for elderly patients.

Conclusions

This study developed prognostic nomograms for elderly patients with stage III and IV EOC based on the SEER database, identifying significant factors affecting OS and CSS, including age, race, surgery, and chemotherapy. The developed nomograms present clinicians with an intuitive tool for individualized prognostic assessment of elderly EOC patients, facilitating more informed treatment decisions. However, further external validation and prospective studies are needed to optimize the prognostic models and deliver more precise individualized treatments for elderly EOC patients.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2129/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2129/prf

Funding: This work was supported by Natural Science Foundation of Zhejiang Province (No. LQ20H040009), Natural Science Foundation of Zhejiang Province (No. LQ20H160054), and Natural Science Foundation of Zhejiang Province (No. LQ20H160049).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2129/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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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