
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39294501
1275
10.1007/s12672-024-01275-8
Analysis
Conditional survival estimates for ependymomas reveal the dynamic nature of prognostication
Sun Chenjun
Yang Zhihao
Gu Zhiwei
Huang Hua drbrainhh@163.com

https://ror.org/0435tej63 grid.412551.6 0000 0000 9055 7865 Department of Neurosurgery, Shaoxing Central Hospital, The Central Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang China
18 9 2024
18 9 2024
12 2024
15 46030 3 2024
26 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Traditional survival analysis is frequently used to assess the prognosis of ependymomas (EPNs); however, it may not provide additional survival insights for patients who have survived for several years. Thus, the conditional survival (CS) pattern of this disease is yet to be further investigated. This study aimed to evaluate the improvement of survival over time using CS analysis and develop a CS-based nomogram model for real-time dynamic survival estimation for EPN patients.

Methods

Data on patients with EPN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. In order to construct and validate the model effectively, the selected patients were randomly divided at 7:3 ratio. CS is defined as the probability of surviving for a specified time period (y years) after initial diagnosis, given that the patient has survived x years. The CS pattern of EPN patients were explored. Then, the least absolute shrinkage and selection operator (LASSO) regression method with tenfold cross-validation was employed to identify prognostic predictors. Multivariate Cox regression was employed to develop a CS-based nomogram model, and we used this model to quantify EPN patient risk. Finally, the performance of the prediction model was also evaluated and verified.

Results

In total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. The CS analysis demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate of EPN patients saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1–9 years, respectively). The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a CS-based nomogram model. And further risk stratification was conducted based on nomogram model for these patients. Furthermore, this survival prediction model was successfully validated.

Conclusion

This study described the CS pattern of EPN patients and highlighted the gradual improvement of survival observed over time for long-term survivors. We also developed the first novel CS-nomogram model that enabled individualized and real-time prognosis prediction. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram.

Keywords

Ependymoma
SEER
Nomogram
Conditional survival
Prognosis
issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Ependymomas (EPNs) are tumors that primarily affect the central nervous system (CNS) and typically develop from ependymal cells or from the central canal of the spinal cord, accounting for 7–10% and 4% of the primary CNS tumors in children and adults [1–3]. EPN can manifest at any location along the neuroaxis, with a propensity for specific age-location preferences [4]. In children, it's often found in the posterior fossa, while it is more common in adults to occur supratentorially or in the spine compartments [4–6]. Based on the 2004 World Health Organization (WHO)'s classification, EPNs are categorized into three grades of malignancy: WHO grades 1, grades 2, and grades 3 [3, 7]. To provide a more comprehensive understanding of the biological, clinical, and histopathological features of these tumors, researchers have recently employed a molecular classification system to divide EPNs into nine different groups in 2015 [8, 9]. At present, the medical guidelines advise prioritizing gross-total resection (GTR) as the primary treatment for tumors, with possible radiotherapy for grade 3 tumors or when GTR is not attainable [4, 8, 10, 11]. Furthermore, surgical intervention and radiation therapy represent the principal form of treatment in children, and the advantage of postsurgical radiation therapy has been demonstrated in terms of local control and survival rates in pediatric patients with intracranial EPNs [5, 12]. Despite improvements in clinical management strategies and advancements in medical technology yielding notable benefits in the prognosis of individuals afflicted with EPN [8], further investigation is needed to determine their survival outcomes in the modern era of neurosurgery.

Numerous studies have described and analyzed the survival outcome of EPN, however, traditional survival analysis models are commonly employed [11, 13–17]. Traditional survival analysis solely considers the patient's status at the time of primary diagnosis as a milestone, disregarding the patient's survival duration [18]. And it fails to provide updated prognostic data for those tumor patients who have survived several years. Several studies have also reported that long-term prognosis is dynamically improving in various types of tumors [18–22]. However, the dynamic survival pattern of EPN patients remains uncertain.

Conditional survival (CS) is a reliable survival measure that evaluates the likelihood of cancer patients who have survived for a certain period after diagnosis to survive for a further specified length of time [23, 24]. It can deliver an up-to-date estimate of survival and provide significant support in clinical practice. Additionally, several traditional nomogram models had developed individualizing survival estimation of EPN patients [25–27], but failing to provide dynamic prognostic information over survival time. Therefore, incorporating CS into a nomogram shows potential for personalized, dynamic and real-time prognostic forecasting, making it a promising tool for clinicians.

In this study, we aimed to investigate the CS pattern of EPN patients, and integrate CS into traditional nomogram model to establish a novel CS-based nomogram for EPN survivors.

Material and methods

Patient selection

We acquired the complete 2004–2017 dataset from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. This dataset consists of fundamental patient information acquired from 17 distinct registries located in the United States. Patients diagnosed with EPN, identified by ICD-O-3 codes 9391–9394 and confirmed through histological diagnosis, were included in our study. The topography codes for the primary site ranged from C70.0 to C72.9. And those cases without complete follow-up information were excluded. Prior to utilizing this database, we adhered to the SEER Research Data Use Agreement and Best Practices Assurance guidelines, and the ethics committee deemed the study exempt from ethical review following institutional protocol.

Variables selection

The demographic information of patients, such as age at diagnosis, sex, race, and household income, was collected from the SEER database. Data regarding tumor characteristics, including tumor site, tumor extension, tumor size, and tumor grade, was extracted. In addition, treatment details and survival status of these patients were also obtained. Due to the lack of molecular biology-related variables in the SEER database, we were unable to include such data in our analysis, which may somewhat diminish the reliability of our results. In our analysis, we utilized overall survival (OS) as the endpoint, which is defined as the duration from the initial diagnosis of the patient's disease to the time of death.

Statistical analysis

EPN patients meeting the criteria were categorized into a training group and a validation group with a 7:3 ratio. Descriptive statistics were employed to present characteristics of patients, tumors, and treatments in the overall, training, and validation cohorts, respectively. The clinical endpoint of the study, OS, was estimated using the Kaplan–Meier method.

The CS was determined using the formula CS(y|x) = OS(y + x)/OS(x). CS(y|x) referred to the likelihood of a patient surviving an additional y years after having already survived x years from the initial diagnosis. The OS(x) and OS(y + x) values were estimated using Kaplan–Meier method on patient survival rates for x- and (x + y)-years, respectively. The CS pattern of EPN patients were explored.

For variable selection, the LASSO regression method with tenfold cross-validation was employed to prevent overfitting. Subsequently, the multivariate Cox regression analysis was utilized to verify the prognostic value of chosen predictors and to establish a nomogram model. This nomogram model was integrated with CS analysis to enable dynamic prediction of survival. The probability of 3-, 5-year and 10-year OS, and 10-year CS could be estimated via our novel nomogram model. In addition, we used CS-based nomogram to calculate risk scores for patients and quantify patient risk. Then, a risk classification system was developed to assign patients into a high-, or low-risk group by the best cut-off value of risk scores. And the OS of patients in different risk groups was compared using the Kaplan–Meier curve and log-rank test.

To assess the predictive performance of the CS-based nomogram, a bootstrap method involving 1,000 resamplings was used for internal verification. The calibration curves were drawn to examine the degree of calibration and ensure the model's accuracy and reliability. The model's ability to discriminate between patients was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, and area under the curve (AUC). Finally, the clinical utility of our CS-nomogram was validated through decision curve analysis (DCA) which calculated the net benefit of initiating a medical intervention. The statistical tests used were two-sided and a significance level of 0.05 was applied. R (version 4.1.0) was used for all statistical analyses.

Results

Clinicopathological characteristics

In total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. Table 1 depicts the baseline characteristics of these patient. Within the total cohort, the majority of patients fell within the 20–59 age range, while 31.4% were under 20 years old. Differences between genders were not observed. Moreover, 36.4% of patients presented with EPN occurring within the spinal cord, while 26.8% and 25.5% were located in supratentorial and infratentorial regions, respectively. The majority of EPN patients are typically diagnosed with grade 2 (73.4%) and localized (83.3%) disease. With regards to treatment, a majority of the patients (82.9%) underwent surgery while nearly half (48.7%) received radiotherapy.Table 1 Clinicopathologic characteristics of ependymoma patients

Characteristics	Whole cohort (N = 1829)	Training cohort
(N = 1280)	Validation cohort
(N = 549)	
Age at diagnosis				
 < 20	575 (31.4%)	414 (32.3%)	161 (29.3%)	
 20–59	953 (52.1%)	655 (51.2%)	298 (54.3%)	
 ≥ 60	301 (16.5%)	211 (16.5%)	90 (16.4%)	
Sex				
 Male	923 (50.5%)	659 (51.5%)	264 (48.1%)	
 Female	906 (49.5%)	621 (48.5%)	285 (51.9%)	
Race				
 White	1503 (82.2%)	1045 (81.6%)	458 (83.4%)	
 Non-white	301 (16.5%)	216 (16.9%)	85 (15.5%)	
 Unknown	25 (1.4%)	19 (1.5%)	6 (1.1%)	
Tumor site				
 Supratentorial	491 (26.8%)	357 (27.9%)	134 (24.4%)	
 Infratentorial	467 (25.5%)	320 (25.0%)	147 (26.8%)	
 Spinal cord	665 (36.4%)	457 (35.7%)	208 (37.9%)	
 Brain, NOS	206 (11.3%)	146 (11.4%)	60 (10.9%)	
Tumor grade				
 Grade II	1343 (73.4%)	935 (73.0%)	408 (74.3%)	
 Grade III	486 (26.6%)	345 (27.0%)	141 (25.7%)	
Tumor extension				
 Localized	1524 (83.3%)	1058 (82.7%)	466 (84.9%)	
 Regional/distant	257 (14.1%)	190 (14.8%)	67 (12.2%)	
 Unknown	48 (2.6%)	32 (2.5%)	16 (2.9%)	
Tumor size				
 < 40 mm	685 (37.5%)	485 (37.9%)	200 (36.4%)	
 ≥ 40 mm	656 (35.9%)	453 (35.4%)	203 (37.0%)	
 Unknown	488 (26.7%)	342 (26.7%)	146 (26.6%)	
Surgery				
 No surgery	313 (17.1%)	212 (16.6%)	101 (18.4%)	
 STR	779 (42.6%)	553 (43.2%)	226 (41.2%)	
 GTR	737 (40.3%)	515 (40.2%)	222 (40.4%)	
Radiotherapy				
 No	939 (51.3%)	642 (50.2%)	297 (54.1%)	
 Yes	890 (48.7%)	638 (49.8%)	252 (45.9%)	
Household income				
 < 65,000$	832 (45.5%)	603 (47.1%)	229 (41.7%)	
 ≥ 65,000$	997 (54.5%)	677 (52.9%)	320 (58.3%)	
NOS, not other specific; STR, subtotal resection; GTR, gross total resection

Conditional survival analysis

The Kaplan–Meier method showed that EPN patients have an estimated OS of 88%, 84% and 74% at 3-, 5-, and 10-years, respectively. Further analysis using the CS approach demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1–9 years, respectively) (Fig. 1).Fig. 1 Conditional survival estimates were calculated for EPN patients. The Kaplan–Meier method was used to estimate overall survival at diagnosis (year 0) and conditional survival for patients who had survived 1 to 9 years post-diagnosis. EPN, ependymoma

CS-based nomogram development and patient risk stratification

The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a prognostic model (Fig. 2). Multivariate Cox regression analysis confirmed the prognostic value of selected variables (Fig. 3) and we also employed multivariate Cox model to construct a nomogram for estimating prognosis individually. This prediction model was also applied with the CS formula, allowing for real-time updates of a patient's 10-year survival based on their time survived (Fig. 4). The CS nomogram assigned a point value to each patient variable entered, with the sum of those points corresponding to the patient's 3-year, 5-year and 10-year OS, and 10-year CS rates.Fig. 2 The least absolute shrinkage and selection operator (LASSO) regression analysis (A) with tenfold cross-validation (B) was conducted for predictor screening

Fig. 3 Multivariate Cox regression forest plot showing the effect of predictors on overall survival of EPN patients. EPN, ependymoma

Fig. 4 Conditional survival-based nomogram was successfully established

Furthermore, a risk stratification system was developed to classify patient risk based on the cumulative score calculated by the CS-based nomogram. The standardized log-rank statistic determined that the optimal cut-off point for survival was at 231, dividing patients into high-risk and low-risk categories (Fig. 5A and B). The Kaplan–Meier curves demonstrated that the risk classification system effectively differentiated the prognosis of various risk groups in both training and validation cohorts. The outcome for patients in the low-risk category was significantly superior to that of individuals classified as high-risk (Fig. 5C and D).Fig. 5 A risk stratification system was developed to classify patient risk based on the cumulative score calculated by the CS-based nomogram. A Distribution of total risk points; (B) The standardized log-rank statistics; Kaplan–Meier analysis and log-rank tests for different risk groups in training (C) and validation (D) cohorts

Calibration and validation of the CS-based nomogram

The C-index yielded a predictive accuracy of 0.721 in the training cohort, and 0.752 in the validation cohort. The nomogram demonstrated favorable calibration in both cohorts, exhibiting strong associations between predicted and observed survival proportions at three distinct time points (Fig. 6A and B). The results of the ROC analysis demonstrated that the CS-based nomogram consistently displayed favorable discriminatory power in both the training and validation groups. The AUC values for the 3-, 5- and 10-year timepoints were 0.74, 0.75 and 0.65 in the training group (Fig. 6C), and 0.77, 0.78 and 0.67 in the validation group (Fig. 6D), respectively. The DCA curves demonstrated the significant advantage of utilizing the CS-nomogram as a tool for initiating medical intervention rather than adopting a rigid "all or nothing" approach in both training and validation groups (Fig. 7A and B).Fig. 6 The conditional survival-based nomogram evaluation and validation. Calibration plots (A and B) and time-dependent receiver operating characteristic (ROC) curves (C and D) for assessing the accuracy and discrimination of the prediction model in both training and validation cohorts

Fig. 7 Decision curve analysis (DCA) were used to assess the clinical utility of the nomogram in both the training (A) and validation groups (B)

Discussion

Traditional survival analysis is commonly employed to analyze the prognosis of EPN, but it fails to provide addition survival information for patients who have survived for several years. Thus, the CS pattern of this disease is yet to be further investigated. In this study, we utilized the SEER database, which offers a considerable sample size, to gather data on patients with EPN and describe their CS pattern. Additionally, it is widely acknowledged that a prognostic model capable of predicting patient survival is crucial for tailoring therapy for individuals with tumors, particularly a dynamic and real-time prognostic forecasting tool [19]. Therefore, we constructed the first CS-based nomogram model to obtain real-time prognostic information for long-term EPN survivors. We also confirmed the favorable prediction performance with precise discrimination and reliable calibration of our predictive model in both the training and validation cohorts. Moreover, our nomogram model had demonstrated its clinical superiority in terms of utility, as evident from the results obtained through DCA curves.

CS analysis in this study demonstrated that patients' OS improved gradually with the years the EPN patient had survived, consistent with findings in other malignancies [18, 19, 21, 22]. As the years post-diagnosis progressed, the 10-year survival rate showed a consistent increase, starting at 74% and rising to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and finally 98% (after surviving for 1–9 years, respectively) in EPN patients. The mining and provision of these dynamic survival information, in turn, will further enhance the confidence of some EPN patients, alleviate psychological anxiety and promote their physical and mental health. Furthermore, the dynamic CS pattern could also aid in development of cost-effective surveillance methods for optimizing the follow-up strategies.

Although the CS pattern could provide real-time survival information, it disregards clinicopathological characteristics affecting prognosis of individual patients. Thus, we combined the CS analysis with the traditional nomogram, which incorporates significant variables to estimate survival [28], to constructed a novel CS-based nomogram model. This model used age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy as survival predictors identified by LASSO regression analysis. We found patient age, tumor grade and tumor site were the strongest factors influencing the outcomes in our model. Deng et al. also observed notable disparities in the OS rates of EPN patients across different age groups, specifically between pediatric and adult populations [26]. We further noted that individuals diagnosed with EPN located in the spinal cord and classified as grade 2 showed more favorable prognoses, in line with the findings of previous articles [3, 8, 13, 26]. Moreover, According to the WHO classification of CNS tumors of 2021, EPNs are classified according to a combination of histological and molecular features across three tumor locations (supratentorial, infratentorial and spinal) [4, 29]. Within the supratentorial compartment, exist two additional entities demonstrating variations in molecular features, clinico-pathological traits, and prognostic outcomes—namely, the ZFTA fusion-positive supratentorial EPN, and the YAP1 fusion-positive supratentorial EPN [29]. Posterior fossa EPNs were delineated into two predominant groups: posterior fossa type A (PFA) and B (PFB) [29]. Furthermore, the quantity of histone H3K27-trimethylation proves abundant in PFB lesions, contrasting with its scarcity within PFA tumors [4, 29]. It is noteworthy that genomic gain on chromosome 1q and loss on chromosome 6q has been repeatedly identified as an independently significant predictor for unfavorable prognoses in patients afflicted with PFA EPNs [30]. These molecular genetic alterations and tumor classifications may better elucidate our findings. However, as the SEER data lacks information on these aspects, we are unable to probe deeper into their implications at this juncture. Future studies could further incorporate molecular genomics data for a more comprehensive analysis.

Currently, medical guidelines recommend giving priority to GTR as the initial treatment for tumors [8]. Radiotherapy may be considered for grade 3 tumors or when GTR is not feasible in adults [8, 16, 26]. Also, radiotherapy are the mainstay of treatment for EPNs in children and the utility of adjuvant radiation therapy has been demonstrated in improving outcomes of local control and survival rates among children afflicted with intracranial EPNs [8]. Although significant concerns exist regarding the toxicological impact of radiotherapy in younger children, proton beam radiotherapy and intensity-modulated radiation therapy has been implemented to minimize adverse long-term effects [8]. Our study also confirmed that GTR conferred a survival advantage among EPNs. Currently, the use of radiotherapy for this type of tumor is restricted and is solely employed for patients with specific indications. The effectiveness of radiotherapy in our study was also found to be limited. While LASSO analysis identified it as a prognostic factor, its predictive value was relatively weak, and the relationship between radiotherapy and prognosis was not readily apparent in the multivariate Cox model. This might attribute to our comprehensive CS-based prediction model construction aiming for all ages and tumor locations. Also, we must also consider the variability in radiotherapy effectiveness based on individual characteristics. Adult tumors are predominantly PFB, where radiotherapy may not provide significant benefit. In contrast, pediatric tumors are mostly PFA, where radiotherapy plays a crucial role in enhancing survival. Future studies may require a more comprehensive stratification of patient characteristics to precisely elucidate the specific contribution of radiotherapy. Regardless, our model demonstrated its applicability through its extensive coverage of various EPN populations. The effectiveness of chemotherapy for pediatric cases is currently an unanswered scientific question. With apprehension regarding irradiation delivery to extremely young patients, postsurgical chemotherapy has been commonly suggested; however, in older children, chemotherapy is often implemented concurrently with radiotherapy [8] Due to the limitation of SEER database, we cannot further analyze the role of chemotherapy in EPN. Furthermore, we were able to easily access clinical data regarding patient traits that facilitated the forecasting of their evolving survival prospects via our dynamic survival prediction tool.

This study does have some limitations that need to be addressed. Firstly, as a study with retrospective nature, potential selection bias is inevitable. Secondly, the SEER database lacked certain information such as molecular biomarker data, comprehensive treatment details, and patient comorbidities. Also, patients included in our study lacks central pathological and radiological review. Thirdly, our data has only undergone internal verification, and external verification in the real world is necessary for further validation. Lastly, updating this CS-nomogram over several years is necessary as treatment strategies improve. Furthermore, considering the need for further validation of the generalizability of our study data and the variability among individual cases, our nomogram model may pose a risk of providing false reassurance to some patients.

Conclusions

This study described the CS pattern of EPN patients and elucidate the gradual improvement of survival over time for long-term survived EPN patients. We also developed the first novel CS-nomogram model for predicting individualized and real-time survival, demonstrating robust performance and providing more dynamic prognostic information for long-term survivors. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram.

Acknowledgements

The authors would like to thank the SEER database for the availability of the data.

Author contributions

CS and HH designed the study; CS, ZY, ZG and HH contributed to data analysis. CS and ZY wrote the initial draft of the manuscript; CS, ZY, ZG and HH reviewed and edited the manuscript. All authors read and approved the manuscript.

Funding

None.

Data availability

The datasets analyzed for this study can be found in the Surveillance, Epidemiology, and End Results (SEER) database: https://seer.cancer.gov/data-software/.

Declarations

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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