Abstract
Background
Recent studies have reported that overall survival of elderly patients with primary central nervous system lymphoma (PCNSL), who have the highest incidence of this disease, had failed to benefit from the advancements in treatment strategies over the past decades. This highlights the necessity for intensified research to guide treatment decisions for this specific patient population.
Methods
The Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute (NCI) was used to extract data of elderly PCNSL patients (age ≥ 60) who were divided into training and validation groups at the ratio of 7:3, for our analysis. Conditional survival [CS(y|x)] was defined as the probability at survival additional y years given that the patient had not died of PCNSL at a specified period of time (x years) after initial diagnosis. The CS pattern of elderly PCNSL patients was analyzed. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were applied to develop a novel CS-based nomogram.
Results
A total of 3315 elderly patients diagnosed with CNS lymphoma between 2000 and 2019 were extracted from the SEER database, of whom 2320 patients were divided into the training group and 995 into the internal validation group. CS analysis revealed a noteworthy escalation in the 5-year survival rate among elderly PCNSL patients for every additional year of survival. The rates progressed from an initial 21–49%, 63%, and 75%, culminating in an impressive 88% and the survival improvement over time was nonlinear. The LASSO regression identified nine predictors and multivariate Cox regression was used to successfully construct the CS-based nomogram model with favorable prediction performance.
Conclusion
CS of elderly PCNSL patients was dynamic and increased over time. Our newly-established CS-based nomogram can provide a real-time dynamic survival estimation, allowing clinicians to better guide treatment decision for these patients.
Keywords: Conditional survival, Overall survival, Primary CNS lymphoma, SEER, Nomogram
Introduction
Primary central nervous system lymphoma (PCNSL) is an aggressive non-Hodgkin lymphoma that is confined to the central nervous system (CNS), including the brain, leptomeninges, and spinal cord without evidence of systemic involvement, accounting for about 4% of newly diagnosed CNS tumors, with an annual incidence rate of 0.5 per 100,000 in the United States (Velicu et al. 2023; Lv et al. 2022; Grommes and DeAngelis 2017; Wismann et al. 2022). In comparison to systemic lymphomas located outside the central nervous system (CNS), which have a 5-year survival rate of approximately 69% with R-CHOP treatment, the prognosis for PCNSL is inferior, with a 5-year survival rate ranging from 15 to 30% (Velicu et al. 2023; Mendez et al. 2018). Over the past several decades, there has been a notable improvement in the prognosis of PCNSL patients, indicating significant advancements in its treatment. Notably, the introduction of the high-dose methotrexate induction regimen has played a crucial role in this positive trend, making overall survival (OS) of these patients, rising from 1.5 to 3.3 months without treatment to a range of 16.3–66 months (Lv et al. 2022; Shiels et al. 2016; Norden et al. 2011; Villanueva et al. 2023; Liu et al. 2021). However, elderly PCNSL patients who have a highest incidence of this disease are found to fail to benefit from the progress in treatment strategy in the past decades; These patients typically have a median OS of approximately 7 months (Mendez et al. 2018; Burns et al. 2023; David et al. 2023; Meulen et al. 2017), suggesting that intensified studies to guide treatment decision on this patient population are needed.
In modern medicine, precise dynamic survival estimation can effectively guide the clinical management and optimize treatment strategy in clinical practice (Ötleş et al. 2022; Yu et al. 2021). As a type of widely applied statistical means, conditional survival (CS) analysis can provide a real-time updated estimate of survival conveying the dynamics of survival rates over time (Dikken et al. 2013; Latenstein et al. 2020; Meng et al. 2023; Hieke et al. 2015). Additionally, general nomogram model, which has been widely applied in various cancers, can be used to integrated individual predictors to estimate patient survival (Maloney et al. 2023; Wang et al. 2022a; Wang et al. 2022b), but failing to consider survived time. Therefore, in this study, we attempted to integrate the CS analysis to a nomogram model for dynamic, personalized, and more accurate prediction of survival probability for elderly PCNSL patients, hoping to identify high-risk patients as candidates for more frequent follow-up or clinical trials.
The Surveillance, Epidemiology, and End Results (SEER) database contains patient, tumor, and treatment characteristics of all patients with various tumor types, as well as corresponding survival data, providing a valuable resource for clinical cancer research (Harlan and Hankey 2003). Hence, we conducted a retrospective research based on SEER database to assess CS of elderly PCNSL patients and to develop a novel CS-based nomogram for dynamic survival prediction.
Methods
Data source cohort definition
The SEER program of the National Cancer Institute (NCI) was used to extract data of elderly PCNSL patients (age ≥ 60) for our analysis. We identified patients diagnosed with non-Hodgkin's lymphoma which was defined using the SEER ICD O-3 coding including 9590–9599, 9670–9699, 9700–9719, 9720–9729, and 9827. And all patients were diagnosed with PCNSL within the CNS (ICD O-3 codes C70.1-C72.9) between 2000 and 2019. Patients lacked histological confirmation and diagnosed at autopsy were excluded from our study. And those cases without survival information and surgery treatment were also excluded.
Definition of variables
The patient demographics including age at diagnosis, sex, race, marital status, rural/urban residence, and household income were obtained from the SEER database. The data on tumor characteristics, including tumor site, tumor histological type, and the presence of other malignancies, were extracted. Furthermore, treatment information and survival data of these patients were also acquired. Overall survival (OS), which is defined as the time from the patient’s disease diagnosis to death, was applied as endpoint and estimated via the Kaplan–Meier method for our analysis.
Statistical analysis
The identified elderly PCNSL patients were divided into training and validation groups at the ratio of 7:3. Descriptive statistics were used to present patient, tumor, and treatment characteristics in total, training, and validation cohorts, respectively.
CS(y|x) was defined as the probability at survival additional y years given that the patient had not died of PCNSL at a specified period of time (x years) after the initial diagnosis. Calculations of CS were conducted via standard definition of conditional probability (Skuladottir and Olsen 2003)
in which OS(x) and OS(y + x) were survival probability estimated via the Kaplan–Meier methods for x- and (x + y)-years, respectively. For example, if we attempt to estimate the CS for surviving 4 more years for patients who had already have survival 1 year after initial diagnosis, CS(4|1) is calculated as CS(4|1) = OS(4 + 1)/OS(1) = 5-year OS rate/1-year OS rate. We also used Kernel-smoothing hazard function analysis to assess mortality in PCNSL patients followed up over 1 year.
Then, we used the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation to identify independently prognostic factors in training cohort. The multivariate Cox regression was conducted to confirm the prognostic value of selected variables and integrate those predictors to develop a novel CS-based nomogram model. The probability of 3-, 5-year OS and 5-year CS could be estimated with our nomogram model. Finally, the training and validation cohorts were used to validate the discriminative ability and calibration of the model. Harrell’s C-statistic (C-index) was adopted to demonstrate discriminative power with the value ranging from 0.5 to 1 indicating the discrimination ranges from none to perfect. Time-dependent receiver-operating characteristic (ROC) curves with area under the curve (AUC) were also applied to further confirm the model’s performance. To assess the clinical utility of the nomogram, the decision curve analysis (DCA) was employed to calculate the net benefit of our model as a medical intervention. Statistical tests were two-sided, and P values less than 0.05 were considered statistically significant. All statistical analyses were performed using R (version 4.1.0).
Results
Clinicopathological characteristics
A total of 3315 elderly patients diagnosed with CNS lymphoma between 2000 and 2019 were extracted from the SEER database, of whom 2320 patients were divided into the training group and 995 into the internal validation group. In the whole cohort, the majority were at the age between 60 and 79 (83.1%), with white race (84.6%), and living in a metropolitan county at diagnosis (89.1%). And half of patients were male (49.0%). For tumor characteristics, most CNS lymphomas were located in supratentorial (54.3%) and diagnosed as diffuse large B-cell lymphoma (DLBCL) type (82.0%). Also, there were 24.6% patients simultaneously suffered from other malignant tumors. In terms of treatment status, it appears that the majority of patients underwent chemotherapy (63.7%), while only 24.6% and 28.1%, respectively, underwent surgical removal and radiation therapy. See Table 1 for details.
Table1.
Clinicopathologic characteristics of elderly PCNSL
| Parameters | Total cohort (N = 3315) | Training cohort (N = 2320) | Validation cohort (N = 995) |
|---|---|---|---|
| Age at diagnosis | |||
| 60–69 | 1368 (41.3%) | 941 (40.6%) | 427 (42.9%) |
| 70–79 | 1389 (41.9%) | 990 (42.7%) | 399 (40.1%) |
| ≥ 80 | 558 (16.8%) | 389 (16.8%) | 169 (17.0%) |
| Sex | |||
| Male | 1624 (49.0%) | 1133 (48.8%) | 491 (49.3%) |
| Female | 1691 (51.0%) | 1187 (51.2%) | 504 (50.7%) |
| Race | |||
| Black | 502 (15.1%) | 345 (14.9%) | 157 (15.8%) |
| White | 2803 (84.6%) | 1968 (84.8%) | 835 (83.9%) |
| Others/unknown | 10 (0.3%) | 7 (0.3%) | 3 (0.3%) |
| Marital status | |||
| Single | 1127 (34.0%) | 805 (34.7%) | 322 (32.4%) |
| Married | 2058 (62.1%) | 1426 (61.5%) | 632 (63.5%) |
| Unknown | 130 (3.9%) | 89 (3.8%) | 41 (4.1%) |
| Tumor site | |||
| Supratentorial | 1800 (54.3%) | 1256 (54.1%) | 544 (54.7%) |
| Infratentorial | 265 (8.0%) | 188 (8.1%) | 77 (7.7%) |
| Brain, NOS | 1250 (37.7%) | 876 (37.8%) | 374 (37.6%) |
| Tumor histology | |||
| Non-DLBCL | 196 (5.9%) | 133 (5.7%) | 63 (6.3%) |
| DLBCL | 2717 (82.0%) | 1895 (81.7%) | 822 (82.6%) |
| Lymphoma, NOS | 402 (12.1%) | 292 (12.6%) | 110 (11.1%) |
| Coexistence with other malignancy | |||
| No | 2499 (75.4%) | 1735 (74.8%) | 764 (76.8%) |
| Yes | 816 (24.6%) | 585 (25.2%) | 231 (23.2%) |
| Surgery | |||
| No surgery/biopsy | 2501 (75.4%) | 1749 (75.4%) | 752 (75.6%) |
| STR | 224 (6.8%) | 151 (6.5%) | 73 (7.3%) |
| GTR | 590 (17.8%) | 420 (18.1%) | 170 (17.1%) |
| Radiotherapy | |||
| No | 2382 (71.9%) | 1675 (72.2%) | 707 (71.1%) |
| Yes | 933 (28.1%) | 645 (27.8%) | 288 (28.9%) |
| Chemotherapy | |||
| No | 1205 (36.4%) | 844 (36.4%) | 361 (36.3%) |
| Yes | 2110 (63.7%) | 1476 (63.6%) | 634 (63.7%) |
| Rural–urban | |||
| Non-metropolitan | 360 (10.9%) | 267 (11.5%) | 93 (9.3%) |
| Metropolitan | 2955 (89.1%) | 2053 (88.5%) | 902 (90.7%) |
| Household income | |||
| < $50,000 | 1373 (41.4%) | 972 (41.9%) | 401 (40.3%) |
| ≥ $50,000 | 1942 (58.6%) | 1348 (58.1%) | 594 (59.7%) |
NOS, not other specific; DLBCL, diffuse large B-cell lymphoma; STR, subtotal resection; GTR gross total resection
Conditional survival analysis
We utilized the Kaplan–Meier method to estimate the OS probability among elderly patients with CNS lymphoma, revealing a mere 28% and 21% OS at the 3-year and 5-year mark respectively (Fig. 1A). Using conditional survival analysis, we observed a noteworthy escalation in the 5-year survival rate among these patients for every additional year of survival. The rates progressed from an initial 21–49%, 63%, 75%, culminating in an impressive 88% and the survival improvement over time was nonlinear (Fig. 1A and B). We then conducted the CS(1|x) curve analysis to determine the 1-year survival rate of these patients after X years survival and found that when X was 0, indicating the lowest survival rate of 43% in the first year following the patient's diagnosis (Fig. 1B). Meanwhile, the Kernel-smoothing hazard function analysis showed that the disease mortality increased steeply toward the peak around the first year after diagnosis (Fig. 1C).
Fig. 1.
Conditional survival analysis of elderly PCNSL patients. A Kaplan–Meier estimates of survival at diagnosis (0 years) and conditional survival based on years already survived after diagnosis (1–5 years). B CS(1|x) curve demonstrated the probability of survival additional 1 year after surviving for x years since diagnosis and 5-year CS curve showing the 5th year of survival after surviving for x years since diagnosis; C the Kernel-smoothing hazard function curve. CS, conditional survival
The CS-nomogram construction
From training cohort, the LASSO regression with tenfold cross-validation identified nine predictors including age at diagnosis, sex, marital status, tumor site, tumor histology, coexistence with other malignancy, surgery type, radiotherapy, and chemotherapy for our novel CS-based nomogram model construction (Fig. 2A, B). The multivariate Cox regression forest plot demonstrated a significant association between these predictors and the outcome of PCNSL (Fig. 3, P < 0.05), providing further confirmation of the prognostic value of these selected variables. Then, we successfully developed the CS-based nomogram using the multivariate Cox regression method in training set to estimate CS probability individually for elderly PCNSL patients (Fig. 4). The CS formula was applied into our nomogram model to predict real-time outcomes updated over time. Our novel nomogram can predict the 5-year survival rate of patients who have survived X years by calculating an overall score based on various clinical features and treatment information, facilitating accurate prognostic assessment for clinical patients. And C-index values of the nomogram was 0.691 in training group, indicating favorable prediction performance.
Fig. 2.
The least absolute shrinkage and selection operator (LASSO) regression analysis (A) and tenfold cross-validation (B)
Fig. 3.
The Multivariate Cox regression forest plot. NOS, not other specific; DLBCL, diffuse large B-cell lymphoma; STR, subtotal resection; GTR, gross total resection; RT, radiotherapy; CT, chemotherapy
Fig. 4.
Conditional survival-based nomogram predicting 3- and 5-year overall survival (OS) and 5-year conditional survival (CS) for elderly PCNSL patients. NOS, not other specific; DLBCL, diffuse large B-cell lymphoma; STR, subtotal resection; GTR, gross total resection
The CS-nomogram validation
Furthermore, we applied an internal validation cohort from the SEER database (n = 995) to validate the nomogram, showing that our novel model also had good prognostic value (C-index: 0.671). The calibration plots demonstrated good concordance between predicted and observed 3- and 5-year OS probability in both training and validation sets (Fig. 5A, B). The ROC analysis indicated that the nomogram exhibited a good discriminative ability in both groups (Fig. 5C, D). The 3- and 5-year AUC values were both 0.73 in training group, and the validation group’s 3- and 5-year AUV values were 0.71 and 0.70. Ultimately, we applied DCA to evaluate the clinical benefit of our model and the corresponding scope of application. The DCA curves revealed an enhanced net benefit of using the CS-nomogram as a tool for triggering medical intervention with wider threshold probabilities in both training and validation groups (Fig. 6).
Fig. 5.
The CS-nomogram validation. Calibration plots of the nomogram for training and (A) validation group (B); Time-dependent area under curve (AUC) curves for training (C) and validation group (D). AUC, area under the curve
Fig. 6.
Decision curve analysis (DCA) curves for evaluating clinical usefulness of the nomogram in training (A) and validation group (B)
Discussion
The incidence of PCNSL is steadily increasing as the population ages and this disease mainly occurs in the elderly (Chukwueke and Nayak 2019; Holdhoff et al. 2020). It’s reported that OS of the general population with PCNSL had increased over past decades; however, elderly patients are the exception (Mendez et al. 2018; Meulen et al. 2017; Furst et al. 2019), highlighting the need for prospective clinical trials in elderly PCNSL patients. Therefore, our study aimed to establish a dynamic CS-based nomogram model for accurate survival prediction, thereby recognizing high-risk patients as candidates for related clinical trials. And this nationwide study using SEER database is the first to construct CS-based nomogram model targeting the elderly population with PCNSL.
CS analysis is a novel survival estimation method that is especially of interest in cancers with poor outcomes, providing real-time estimation of changes in survival (Hieke et al. 2015; Zabor et al. 2013). We first analyzed CS patterns in elderly PCNSL patients and found a noteworthy escalation in the 5-year survival rate among these patients for every additional year of survival. The survival improvement over time was nonlinear and the disease mortality increased sharply, peaking around at first year after diagnosis, highlighting the critical importance of allocating heightened attention to the initial year after diagnosis. This result was also validated via the annual hazard curve and was consistent with the other published studies. The elevated fatality rate in the initial stages could be attributed to the highly malignant nature of the tumor and its rapid deterioration. Additionally, since the majority of patients are middle-aged or elderly, their life expectancy and overall health condition could also contribute to the augmented early mortality rate. Recognizing this vulnerable time window emphasizes the necessity for more proactive treatment approaches and comprehensive follow-up plans during the early stages of treatment, thereby enhancing treatment strategies and optimizing outcomes for elderly PCNSL patients.
Traditional survival analysis may not allow for real-time response (Yang et al. 2021; Deng et al. 2020). Considering the ability of the real-time survival estimation of CS analysis, we integrated CS into a nomogram model and developed a novel CS-based nomogram to provide elderly PCNSL survivors with real-time updated survival information. Via CS analysis, we have found a significant improvement in the survival rates of elderly PCNSL patients who successfully survived first year after their initial diagnosis. And our nomogram model providing these patients with real-time updated significant increase of 5-year survival probability may bring a potential positive psychological state. As a result, this can, in turn, contribute to greater adherence to follow-up and treatment for elderly PCNSL patients. And we can further screen out high-risk patients and optimize the treatment process by risk stratification using our nomogram model. Finally, to determining the performance and clinical application value of our model, the C-index value, calibration plots, ROC analysis, and DCA analysis were applied and confirmed the favorable prognosis prediction ability of our newly developed nomogram.
Several studies have reported that there was no improvement in survival in elderly PCNSL patients over past decade, despite the advances in treatment strategies (Mendez et al. 2018; Meulen et al. 2017). Mendez et al. reported that elderly PCNSL group had a worse median survival with no improvement in survival over the last 40 years, underscoring the need for prospective clinical trials in these patients (Dikken et al. 2013). Our data uncovered the time window of high mortality in elderly PCNSL patients after diagnosis, of when the supportive care should be further intensified. And Our CS-based nomogram model can help patients with real-time risk classification for better clinical trial enrollment. Furthermore, survivors of PCNSL often endure neurological dysfunction, psychological distress from the disease, and side effects reactions from chemotherapy. Therefore, it has been demonstrated that marriage positively affects the prognosis of patients with PCNSL (Deng et al. 2020; Tang et al. 2022). Consistently, our multivariate Cox regression analysis also confirmed protective effects of social support on PCNSL survival. In addition, differences in tumor survival between races have been reported in a variety of tumors (Omofuma et al. 2023; Bui et al. 2021). However, in PCNSL, race did not significantly affect prognosis in our study (P > 0.05). This may be due to the highly malignant and rapidly progressive characteristics of PCNSL, masking any potential survival disparities based on race.
There were some limitations should be noted in our study. First, because of its retrospective nature, selective bias was inevitable. Second, despite the rich sample size of the SEER database, the lack of information on tumor molecular bioinformation, detailed treatment information, patient complications, and performance status limited some of our analysis. Third, as the SEER database does not provide information on the endpoint outcome of progression-free survival (PFS), we were unable to assess the FPS of the patients, but the study of CS analysis also provides some insight into the current survival pattern of this tumor. Finally, our nomogram model needs further externally validation and this CS-nomogram should update over time as treatment strategies improve.
Conclusion
The CS of elderly PCNSL patients was dynamic and nonconstant improved over time. The LASSO regression identified nine predictors and multivariate Cox regression was used to successfully construct the CS-based nomogram model with favorable prediction performance. Our novel nomogram model can provide a real-time dynamic survival estimation to assist in guiding clinical decisions and significantly reducing the psychological stress of survivors through real-time prognosis updates.
Acknowledgements
The authors would like to thank the SEER database for the availability of the data.
Author contributions
HQ and HC designed the study; HQ, ZY, LC, and HC contributed to data analysis. HQ wrote the initial draft of the manuscript; HQ, ZY, LC, and HC reviewed and edited the manuscript. All authors read and approved the manuscript.
Funding
None.
Data availability
The data sets used and/or analyzed during the current study are available from the Surveillance, Epidemiology, and End Results (SEER) database http://seer.cancer.gov/.
Declarations
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data sets used and/or analyzed during the current study are available from the Surveillance, Epidemiology, and End Results (SEER) database http://seer.cancer.gov/.