Nomogram for predicting overall and cancer-specific survival in elderly patients (≥65 years) with epithelial ovarian cancer

Abstract

Background: Current evidence suggests that the morbidity and mortality of ovarian cancer in elderly patients have increased over the past few years. To date, there are no standard treatment guidelines for elderly (65 years and older) patients with ovarian cancer. This study aimed to use the SEER database to extract relevant clinicopathological data and construct a nomogram to predict the prognosis of elderly patients with ovarian cancer that could assist clinicians during clinical decision-making and improve the prognosis of this patient population.

Methods: We screened a total of 22,181 eligible patients. The collected patient information was randomly assigned to a training cohort (n = 15529) and validation cohort (n = 6652) at a ratio of 7:3. COX and LASSO analyses were used to screen the overall survival rate and tumor-specific survival rate of elderly ovarian cancer patients. The independent risk factors were used to establish a nomogram using the rms package. The predictive and clinical utility of nomograms was assessed using concordance index, area under the curve (AUC), calibration curve and Decision curve analysis. Kaplan Meier analysis further stratified and analyzed the overall survival rates of patients in the high and low-risk groups to evaluate the stratification ability of the nomogram.

Results: Our nomogram yielded significantly better performance than the AJCC staging system in predicting overall survival and tumor-specific survival prognosis in elderly (65 years and older) ovarian cancer patients. Survival curve analysis showed that the nomogram has excellent risk stratification ability.

Conclusions: Our nomogram could effectively predict the overall survival rate and tumor-specific survival rate of elderly patients with ovarian cancer (65 years old and above) to help clinicians make individual survival predictions and provide improved treatment recommendations.

Introduction

Globally, ovarian cancer (OC) is the third most common gynecological malignancy but still ranks first among gynecological malignancies in mortality rate[1]. Importantly, up to 80% of ovarian cancer patients are already at an advanced stage at diagnosis [2]. It is widely acknowledged that most ovarian cancer cases occur at the age of 40-70 years, and the incidence increases with age. The proportion of patients over 65 years has increased year by year. [3] In this regard, the reported median age of ovarian cancer at diagnosis in Europe is 63 years old[4]. Satisfactory cytoreductive surgery (R0 surgery) and a course of platinum plus paclitaxel chemotherapy remain the mainstay of treatment for ovarian cancer [5]. Elderly patients with ovarian cancer have been associated with poor nutritional status, poor tolerance to chemotherapy and a greater number of comorbidities and postoperative complications, resulting in a small proportion of elderly patients with ovarian cancer indicated for surgery. An increasing body of evidence suggests that elderly OC patients are often unable to derive benefit from standard chemotherapy, accounting for the relatively high recurrence and mortality rates[4, 6]. A study evaluated the effect of age on the treatment of ovarian cancer patients and found that only 45% of patients over 70 years of age underwent standard treatment [7]. Importantly, a population projection for the next decade has estimated the number of adults 65–74 years of age will almost double. These findings highlight the need to formulate an optimal clinical treatment plan for elderly patients with OC.[8]

It is well-established that nomograms provide an objective visual representation of data and can be used as a simple graphical prediction tool to calculate the probability of clinical event risk for each patient from a mathematical point of view[9]. There is overwhelming evidence suggesting that the predictive ability of nomograms for cancer is better than the TNM staging developed by AJCC [10, 11]. It has also been shown that nomograms can assist in formulating diagnosis and treatment plans and making appropriate clinical decisions by predicting prognosis [12, 13]. To the best of our knowledge, there are currently no standard treatment guidelines for elderly patients with ovarian cancer (65 years and older).

Therefore, this study sought to construct a nomogram to predict the prognosis of elderly patients with ovarian cancer based on clinicopathological data parameters extracted from the SEER database to help clinicians make better clinical treatment plans and improve the poor prognosis of this patient population.

Results

Clinical and pathological characteristics & Survival analysis

A total of 22,181 patients who met the inclusion criteria were screened in our study. Analysis of patients included in our study showed that EOC incidence in the elderly (≥65 years old) exhibited an increasing trend, with the highest incidence in the 65-79 years age group, accounting for 81.3% of all elderly ovarian cancer patients. Only 45% of patients had health insurance coverage, and only 42% of elderly patients were positive for CA125. Most elderly ovarian cancer patients presented with advanced-stage disease (stage III-IV accounting for 71.7%) and high tumor grade, with stage III-IV tumor accounting for 72.6%. Serous tumors accounted for a large proportion (71.9%). However, only 40% of patients underwent complete cytoreduction at the initial surgery, and complete lymph node dissection was only conducted in 20% of patients who underwent lymph node dissection. Residual disease was found in 4% of patients with a residual tumor less than 1 cm after surgery. 69.3% of patients received chemotherapy (Table 1).

Prognostic factors of elderly (≥65years old) patients with OC 

X-tile software (v3.6.1) was used to identify the cut-off point and stratify each variable: age 65-71, 72-79, 80-101years; the number of lymph nodes examined 1-2, 3-7, ≥8; the number of positive lymph nodes 1, ≥2 and tumor size ≤6.4cm, 6.6-9.4cm, ≥9.5cm. (Figure 2A)

Factors influencing prognosis in elderly ovarian cancer patients were identified in the training cohort using a Cox regression model. During univariate analysis, 21 parameters, including stage, lymph node-positive rate, site of primary surgery, age, residual lesion size, CA125 level, grade, tumor size, pathological type, and chemotherapy, exhibited statistically significant differences in OS (p_s< 0.05). Further screening was performed by LASSO analysis (P>0.01 were screened), yielding 18 factors related to OS (Figure 2B). Further multivariate Cox analysis (P<0.01) showed that 12 parameters, including stage, pathological type, age, size of residual lesions, and chemotherapy, were independent prognostic risk factors for elderly patients with ovarian cancer (Table 2). Similarly, 21 items such as age, tumor size, lymph node-positive rate, grade, stage, pathological type, CA125, residual lesion size, and chemotherapy were screened out by univariate analysis to identify risk factors associated with CSS. After LASSO analysis, we found that age, residual lesion size, tumor size, grade, stage, pathological type, chemotherapy and 17 other items were closely related to CSS. Multivariate COX analysis further removed confounding factors and found that 12 parameters, such as stage, pathological type, age, size of residual disease, and whether or not chemotherapy, were closely associated with CSS (Figure 2C).

Nomogram Construction and Validation 

Construction nomogram

Independent prognostic factors identified by multivariate Cox analysis were used to build a nomogram model to predict 3-, 5-, and 10-year OS and CSS in elderly patients with ovarian cancer. The scores of each independent prognostic factor in the nomogram were summed up to obtain a total score projected to OS and CSS at 3, 5, and 10 years. A high total score indicated a poorer prognosis in elderly patients with ovarian cancer. The OS nomogram model showed that among the 12 prognostic-related factors screened, the tumor stage had the greatest impact on prognosis. Independent risk factors that affected the prognosis were pathological type, age, whether receiving chemotherapy, size of residual lesions, and the number of positive lymph nodes. Moreover, we found that the number of lymph node biopsies, tumor grade, tumor laterality, CA125 level, tumor size, and the primary surgical site had the least effect on OS prognosis. The CSS nomogram model showed that among the 11 prognostic factors screened out, tumor stage had the greatest impact on prognosis, followed by pathological type, age, residual disease, chemotherapy, tumor grade, positive rate of lymph nodes, number of lymph node biopsies. The location of primary surgery, tumor size, tumor laterality, and the positive rate of tumor marker CA125 had the least impact on prognosis (Figure 3).

Validation of nomogram

Internal validation of the nomogram. The nomogram showed good predictive value for OS (C-index 0.705 and 0.699) and CSS (C-index 0.703 and 0.707) in the training and validation groups(Table 3), significantly higher than the AJCC staging, suggesting that our nomogram yielded significantly better predictive performance than the AJCC system.

The AUCs of the training and validation cohorts are shown in Figures 4A, 5A. The AUCs of the training cohort for OS prediction at 0.5, 1, 3, 5, and 10 years were significantly higher than the AJCC staging system (0.789, 0.765, 0.749, 0.766, and 0.781 vs. 0.633, 0.640, 0.676, 0.714, and 0.739, respectively). In the validation cohort, the AUCs for OS prediction at 0.5, 1, 3, 5, and 10 years were significantly higher than the AJCC staging system (0.776, 0.765, 0.740, 0.757, and 0.776, vs. 0.646, 0.640, 0.671, 0.703, and 0.737, respectively). Besides, the AUCs for predicting CSS in the training cohort at 0.5,1, 3, 5, and 10 years were significantly higher than the AJCC staging system (0.784, 0.760, 0.741, 0.764, and 0.803 vs. 0.625, 0.632, 0.667, 0.708, and 0.766). Finally, the AUCs to predict CSS in the validation cohort at 0.5, 1, 3, 5, 10 years were significantly higher than the AJCC staging system (0.783, 0.751, 0.743, 0.768, and 0.801 vs. 0.646, 0.633, 0.662, 0.701, and 0.761). We further analyzed and compared the time-dependent AUC of OS and CSS at 1 to 10 years between the nomogram and AJCC staging system in the training and validation groups. The results showed that our nomogram yielded significantly better prediction power than the AJCC staging system (Figure 4 C and D, Figure 5 C and D). Indeed, in contrast to the AJCC staging system, which only involves the primary site of the tumor, lymph node metastasis and distant metastasis, our OS nomogram prediction model consisted of 12 independent prognostic risk factors, while the CSS nomogram prediction model incorporated 11 independent prognostic factors. Even though our nomogram prediction contained multiple independent and potential confounding factors than the AJCC analysis system, the predictive power was still better than the AJCC staging system. Calibration plots at 1, 3, 5, 10 showed good agreement between OS/CSS nomogram predictions and actual observations in training and external validation cohorts (Figure 6). DCA was used to compare the benefits of our established OS and CSS nomograms and AJCC staging systems. Compared with the AJCC staging system, the DCA curves of the nomogram showed a greater net gain in the training and external validation cohorts (Figure 7).

Risk stratification in elderly (≥65 years old )ovarian cancer patients with ovarian cancer

A risk score for each variable was generated from the nomogram, and an overall score was calculated for all patients. X-tile was used to determine the cut-off values. The entire cohort was divided into low-risk and high-risk subgroups based on the median risk score. KM was used to conduct survival analysis between the groups (Figure 8), and significant differences in training cohort and validation cohort of OS (P < 0.001) and CSS (P < 0.001) were observed between the low- and high-risk groups, indicating an excellent nomogram risk stratification performance.

Discussion

As the world's older population continues to increase at an unprecedented pace, the proportion of elderly ovarian cancer patients has gradually increased. Accordingly, much emphasis has been placed on diagnosing and treating elderly ovarian cancer patients in recent years. It is widely acknowledged that elderly patients have more comorbidities, relatively poorer nutritional status than young people and are at higher risk of complications during treatment. The fact that most clinical trials have excluded elderly patients over 70 years old combined with other reasons account for current treatment difficulties faced by this particular patient population [8]. In the present study, we extracted the relevant clinicopathological information from the SEER database to establish a nomogram to predict the prognosis (OS and CSS) of elderly ovarian cancer patients and provide a basis for individualized precision treatment of elderly ovarian cancer patients.

The clinicopathological factors associated with prognosis were screened to establish a nomogram that could predict 3-, 5-, and 10-year OS and CSS in elderly ovarian cancer patients. The C-index and calibration curve of the training cohort and the validation cohort provided compelling evidence that both nomograms had a good discriminative ability, with high consistency between the survival results predicted by the nomogram and the actual patient outcomes. The ROC curve analysis showed that the nomogram established yielded a better predictive value than the AJCC staging system, pathological tumor type and age. Moreover, DCA curve analysis showed that the predictive performance of the nomogram was better than AJCC staging. Importantly, risk scores based on our nomograms exhibited a good ability to stratify patients into high and low-risk groups.

We screened out 12 and 11 independent risk factors closely related to OS and CSS, respectively, in elderly patients with ovarian cancer. Factors significantly associated with OS and CSS included AJCC stage, pathological type, residual disease, age, chemotherapy, lymph node dissection and positive rate.

AJCC staging is well recognized as the traditional pathological staging method for ovarian cancer that can effectively reflect the extent of tumor involvement in patients and provide an important basis for judging prognosis. Our nomogram consistently showed that AJCC staging was the most influential factor in the prognosis of elderly ovarian cancer patients. An increase in the AJCC stage was associated with a worse prognosis in elderly ovarian cancer patients. In the present study, about 70% of elderly patients had AJCC stages III-IV, significantly higher than rates observed in young patients (about 50%) reported in the literature [14]. The low detection rate in elderly patients with ovarian cancer may be attributed to the insidious onset and the lack of effective early detection methods coupled with social factors, including the relatively low frequency of gynecological health checkups in elderly patients compared to younger patients [15].

Herein, we found that older patients that underwent chemotherapy had a significantly better prognosis than those who did not. However, ample evidence suggests that older patients are less tolerant of chemotherapy. A study reported that most elderly ovarian cancer patients did not receive standard-dose combination chemotherapy, while some elderly ovarian cancer patients received only carboplatin single-agent chemotherapy; bevacizumab was included in the first-line regimen in 18.9% of younger patients, compared with 7.8% in older ovarian cancer patients [16]. Even with single-agent chemotherapy, only 54% of elderly ovarian cancer patients completed 4 full-dose single-agent carboplatin chemotherapy cycles. The median survival time of elderly ovarian cancer patients who received less than 4 cycles of chemotherapy was significantly shorter than that of elderly ovarian cancer patients who completed 5-6 cycles of chemotherapy [17]. Current evidence suggests that chemotherapy toxicity in elderly patients is manageable [18]. It has been reported that a considerable number of elderly patients can tolerate adverse reactions caused by standard chemotherapy regimens [19].  Accordingly, age should not be a contraindication to chemotherapy, and effective treatment should not be delayed [20]. A study evaluating the safety and efficacy of bevacizumab combined with first-line carboplatin and paclitaxel chemotherapy in patients with FIGO stage IV ovarian cancer aged 70 years and older showed that overall health scores and small nutritional assessment scores for nutritional status improved in older adults on eight health screenings. There was a slight improvement in the overall health status score for the condition screening and the small nutritional assessment score for nutritional status. The median change from baseline scores was close to zero for the Mobility Fatigue test (which measures independence, activities of daily living, and self-perceived fatigue). This finding suggests that bevacizumab plus first-line carboplatin and paclitaxel is safe and effective for FIGO stage IV ovarian cancer in patients 70 years of age and older [21]. Therefore, combination chemotherapy with an active full course of treatment is beneficial to improve the prognosis of elderly patients [22]. As a form of chemotherapy, intraperitoneal hyperthermic perfusion chemotherapy has also played a positive role in improving the prognosis of elderly patients with ovarian cancer. Current evidence suggests that primary tumor cytoreduction surgery + intraperitoneal hyperthermic perfusion therapy can be effective in elderly ovarian cancer patients over 65 years old, with overall survival rates similar to patients younger than 65 years old. Interestingly, in the neoadjuvant chemotherapy plus cytoreductive surgery and intraperitoneal hyperthermic chemotherapy group, the overall survival time of patients over 65 was significantly higher than patients under 65 years old [23]. These findings highlight the importance of selecting an optimal treatment approach to improve the prognosis of elderly ovarian patients. In addition, it has been reported that as a classic ovarian cancer chemotherapy drug, carboplatin dosage is closely related to glomerular filtration rate and age. The Calvert formula can be used to calculate the dosage [24]. However, given that with increased life expectancy, the actual age of elderly patients is not equal to the biological age[20]，the glomerular filtration rate in elderly patients is difficult to estimate. The actual status of glomerular filtration rate in elderly patients is unclear. Further studies are required to assess whether the dose of carboplatin in elderly patients with ovarian cancer should be increased.

It is widely acknowledged that elderly patients with ovarian cancer have a poorer physical health status than younger patients. It has been shown that compared with most young patients who receive primary cytoreductive surgery at the beginning of treatment, elderly ovarian cancer patients tend to undergo intermediate cytoreductive surgery and surgery is not performed in many cases after neoadjuvant chemotherapy [17]. Our study found that the overall survival rate of elderly patients with ovarian cancer was significantly prolonged, even with residual lesions less than 1 cm. It has been suggested that elderly patients can derive the same overall survival benefit as young women with comprehensive tumor debulking surgery. Residual lesions have a greater impact on the overall survival rate of elderly ovarian cancer patients [25]. Since older patients who receive the same treatment regimen have similar perioperative adverse event rates to younger patients, age should not be an absolute contraindication for ultra-radical resection in older patients with advanced ovarian cancer after NACT. Moreover, age is reportedly not a factor that increases hospitalization costs for ovarian cancer patients [26]. Elderly patients should have the ability to select a treatment plan according to their own preferences. Accordingly, after a comprehensive assessment of the patient's condition, it is essential to properly communicate with the patient and their family members and explain the disease to the patient's treatment plan choice, which directly determines their prognosis.

Lymphadenectomy is subject to much controversy in the surgical treatment of ovarian cancer. It is widely believed that advanced ovarian cancer patients without significant lymph node metastases do not need systematic lymph node dissection. Nonetheless, macroscopic retroperitoneal lymph nodes should be aggressively debulked since the residual disease is significantly associated with the prognosis of ovarian cancer patients[27]. In contrast, another study substantiated that systematic lymph node dissection is beneficial to the survival of patients with early-stage ovarian clear cell carcinoma[28]. In the present study, we observed a better prognosis in elderly ovarian cancer patients with adequate lymph node dissection and node-negative disease, which may be accounted for by the fact that lymph node metastasis is a component of the FIGO staging. Indeed, patients with FIGO stage III-IV have a poor prognosis, and adequate lymphocyte dissection, as part of tumor cytoreduction, can improve the prognosis of elderly ovarian cancer patients. 

In a nutshell, relevant clinicopathological data were extracted from the SEER database to establish a nomogram to predict the prognosis of elderly ovarian cancer patients. However, some limitations and shortcomings were present in our study. Due to its retrospective nature, the sample size for some clinicopathological parameters was relatively small after stratification. Moreover, most clinical trials conducted excluded patients older than 70 years [23]. Nowadays, immune [29] and targeted [30] therapy are used as treatment options for ovarian cancer, but there is no relevant data on elderly patients with ovarian cancer. These findings highlight the need for more prospective, randomized clinical trials to further define the optimal treatment regimen for elderly patients with ovarian cancer.

Conclusion

In the present study, we extracted the relevant clinicopathological information from the SEER database to establish a nomogram to predict the prognosis (OS and CSS) of elderly ovarian cancer patients and provide a basis for individualized precision treatment of elderly ovarian cancer patients.The clinicopathological factors associated with prognosis were screened to establish a nomogram that could predict 3-, 5-, and 10-year OS and CSS in elderly ovarian cancer patients. The nomogram established yielded a better predictive value than the AJCC staging system, pathological tumor type and age. Importantly, risk scores based on our nomograms exhibited a good ability to stratify patients into high and low-risk groups.

Materials And Methods

Data source and extraction

The Surveillance, Epidemiology, and End Results (SEER) database, aims to collect information about cancer characteristics, cancer incidence and results. Data from the SEER (https://seer.cancer.gov/seerstat/) database was used to identify women aged 65 years and above diagnosed with epithelial ovarian cancer (EOC) between 2010 and 2016. Histopathological types were classified according to The International Classification of Diseases for Oncology, Third Edition (ICD-O-3) codes (Same as our previous study [14, 31]), include Serous, Mucinous, Endometrioid, Clear cell, Transitional cell, Epithelial stromal. According to the World Health Organization (WHO), the elderly are defined with a chronological age of 65 years and above [32]. In our study, the relevant data of patients over 65 years of age were further screened and aggregated according to age for retrospective analysis, using SEER*Stat software version 8.3.6 (account ID: 19731-Nov2019). No experiments were performed on humans, animals, or human tissue samples in this study.

The inclusion and exclusion criteria for extracting and screening data from the SEER database were as follows: Inclusion criteria: (1) Diagnosis of EOC established by primary malignant tumor biopsy or postoperative pathology; (2) Availability of clinical data; (3) Aged 65 years or older；(4) Complete tumor staging or debulking surgery with detailed surgical records (e.g., including initial surgical plan, whether regional lymph node dissection was performed, tumor size, and residual tumor size). 

The exclusion criteria: (1) Coexistence of another primary tumor or other serious life-threatening diseases; (2) No specific treatment plan; (3) Lack of information on follow-up and survival status and other information; (4) Aged below 65.

The variables analyzed in this study included: age, marital status, education level, tumor size, insurance status, lymph nodes (LN) examined number, LN positive number, grade, American Joint Committee on Cancer (AJCC) stage, histopathological type, initial surgical plan, regional lymph node removal or not, tumor marker level (CA125), regional LN Surgery, residual tumor size, radiotherapy, chemotherapy or not, organ metastasis (including lung, liver, bone, brain), tumor-specific survival time(CSS) and overall survival time(OS). The beginning of follow-up was defined as the time beginning after the initial operation, the end-point was cancer-specific death, and the end date of the follow-up was December 31, 2016. We screened a total of 22,181 eligible patients randomly assigned to a training (n = 15529) and validation (n = 6652) cohorts at a 7:3 ratio.

End-point 

The end-points of this study included OS and CSS. Clinicopathological features and the associated stratification for inclusion in the study sites were as follows: Year of disease diagnosis:1988-2000; 2001-2010; 2011-2016. Age：65 (including 65)-71 years old; 72 (including 72)-79 years old; 80 (including 80)-101 years old. Marital status: Married; Widowed/Separated; Divorced; Unknown. Insurance status: Blanks/Unknown; insured; uninsured. Preoperative CA125 level: Negative; Positive; Unknown. Tumor size：≤6.4cm；6.5-9.4cm；≥9.5cm; Unknown. Primary surgery site: No debulking surgery(UnDebulking) was performed, including Ovarian cystectomy, only adnexectomy or hysterectomy; tumor cell debulking (Debulking); Pelvic exenteration. Examined number of LN：1-2；3-7；≥8; Undo/Unknown. LN positive number; Negative；1；≥2; Undo/unknown. Tumor grade: I; II; III-IV. AJCC stage: I; II; III; IV. Histology: Serous; Mucinous; Endometrioid; Clear cell; Transitional; Sarcoma. Laterality: Unilateral; Paired; Bilateral. Regional lymph node surgery: None; 1-3 regional; 4 or more regional; Blanks/Unknown. Residual tumor size: none; ≤1cm; ＞1cm; Unknown. Liver, Lung, Bone and Brain Metastasis: Yes; None/unknown. Radiation: Yes; None/unknown. Chemo: None/unknown; Yes. 

Statistical analysis

Each variable was divided into frequency data and proportion data. Clinicopathological characteristics were compared between training and validation cohorts using the chi-square test. Parameters with p-value < 0.05 during univariate least absolute shrinkage and selection operator (LASSO) method were selected to screen clinicopathological features closely related to prognosis. Multivariate Cox regression analysis further removed confounders, identified important prognostic factors, and estimated hazard ratios (HRs) and 95% confidence intervals (CIs). Based on multivariate Cox regression analysis results, a nomogram model was constructed using the rms package in R software. The performance of the nomogram was internally validated in the training cohort and externally in the validation cohort. concordance index (C-index) and Time-dependent area under the curve (AUC) were used to evaluate the discriminative ability of the nomogram, and a calibration curve was generated to test the agreement between predicted OS, cause-specific survival (CSS) and actual OS, CSS. Decision curve analysis (DCA) was used to assess the clinical validity and benefit of our model. The nomogram was used to calculate the total score of all patients. Based on the best cut-off value determined by X-tile (version 3.6.1, Yale University, New Haven, Connecticut, USA), patients were divided into a high-risk and a low-risk group. Kaplan Meier method was used for survival analysis to evaluate the discriminative ability of our nomogram for prognosis. Marginal estimates were used to establish a calibration plot of the nomogram representing the calibration between the predicted and observed survival. All statistical analyses were performed using SPSS (version 25.0, SPSS, Chicago, IL, USA) and R software (version 3.6.0, http://www.r-project.org/). A P-value < 0.05 was statistically significant. The research process is shown in Figure 1.  

Abbreviations

SEER :The Surveillance, Epidemiology, and End Results; EOC : epithelial ovarian cancer; LN: lymph nodes;  AJCC: American Joint Committee on Cancer; CSS: tumor-specific survival time; OS: overall survival time; LASSO: least absolute shrinkage and selection operator; C-index: concordance index; AUC: Time-dependent area under the curve; DCA: Decision curve analysis 

Declarations

Ethics approval and consent to participate: 

All analyses were conducted in accordance with relevant regulations and guidelines.

Consent to publish: 

All authors agree to publish

Availability of data and materials: 

Data from the SEER (https://seer.cancer.gov/seerstat/) database was used to identify women aged 65 years and above diagnosed with epithelial ovarian cancer (EOC) between 2010 and 2016.

Competing interests: 

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Funding: 

Not applicable.

Authors' Contributions: 

Mingzi Tan and Liancheng Zhu designed research.Mingzi Tan wrote manuscript. Mingzi Tan, Jian Gao and Liancheng Zhu involved in data collection and data statistical analysis. Liancheng Zhu critically reviewed the manuscript. All authors read and approved the final manuscript.

Acknowledgements: 

Thanks to all the authors who participated in writing this article.

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Tables

Table 1

Clinical and pathological characteristics of elderly (≥65years old) OC patients

Table 2

 Univariate and multivariate Cox analysis

Table 3

 Comparison of C-indexes