Originally published as JCO Early Release 10.1200/JCO.2006.08.2941 on November 20 2006

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Primary Central Nervous System Lymphoma: The Memorial Sloan-Kettering Cancer Center Prognostic Model

Lauren E. Abrey, Leah Ben-Porat, Katherine S. Panageas, Joachim Yahalom, Brian Berkey, Walter Curran, Christopher Schultz, Steven Leibel, Diana Nelson, Minesh Mehta, Lisa M. DeAngelis

From the Departments of Neurology, Epidemiology and Biostatistics, Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY; Radiation Therapy Oncology Group; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA; Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI; Stanford Comprehensive Cancer Center, Stanford, CA; and the Division of Radiation Oncology, Mayo Clinic, Rochester, MN

Address reprint requests to Lauren E. Abrey, MD, Department of Neurology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10021; e-mail: abreyl{at}mskcc.org

	ABSTRACT

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PURPOSE: The purpose of this study was to analyze prognostic factors for patients with newly diagnosed primary CNS lymphoma (PCNSL) in order to establish a predictive model that could be applied to the care of patients and the design of prospective clinical trials.

PATIENTS AND METHODS: Three hundred thirty-eight consecutive patients with newly diagnosed PCNSL seen at Memorial Sloan-Kettering Cancer Center (MSKCC; New York, NY) between 1983 and 2003 were analyzed. Standard univariate and multivariate analyses were performed. In addition, a formal cut point analysis was used to determine the most statistically significant cut point for age. Recursive partitioning analysis (RPA) was used to create independent prognostic classes. An external validation set obtained from three prospective Radiation Therapy Oncology Group (RTOG) PCNSL clinical trials was used to test the RPA classification.

RESULTS: Age and performance status were the only variables identified on standard multivariate analysis. Cut point analysis of age determined that patients age 50 years had significantly improved outcome compared with older patients. RPA of 282 patients identified three distinct prognostic classes: class 1 (patients < 50 years), class 2 (patients 50; Karnofsky performance score [KPS] 70) and class 3 (patients 50; KPS < 70). These three classes significantly distinguished outcome with regard to both overall and failure-free survival. Analysis of the RTOG data set confirmed the validity of this classification.

CONCLUSION: The MSKCC prognostic score is a simple, statistically powerful model with universal applicability to patients with newly diagnosed PCNSL. We recommend that it be adopted for the management of newly diagnosed patients and incorporated into the design of prospective clinical trials.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
Primary CNS lymphoma (PCNSL) is a rare variant of non-Hodgkin's lymphoma that involves the brain, leptomeninges, eyes, or spinal cord. Over the last 20 years, the treatment of PCNSL has evolved and numerous phase II clinical trials have been reported, some with promising results. However, there has not been a comparable improvement in population-based survival either in the United States¹ or Canada.² Therefore, it is critical to develop a predictive model that will serve as a guideline to determine patient prognosis and to allow appropriate therapeutic decision making. Furthermore, this model could be used to facilitate comparisons of multiple phase II trials and to apply these results to the community at large. Finally, if a phase III trial was undertaken, this model could be used to define the appropriate stratification criteria for proper trial design.

Age and performance status are the two variables that have been consistently identified as independent prognostic factors in a wide variety of studies^3,4; no other potential prognostic factors have been confirmed. Two prognostic scores for PCNSL have been proposed recently; each is based on the aggregate number of adverse factors present in an individual patient. The four point Nottingham/Barcelona score was derived from 77 consecutive patients treated on one of two clinical trials and is based on age, performance status, and multifocal or meningeal disease.⁵ The small number of patients included in this score limits the power to detect important prognostic variables. In addition, this score failed to discriminate prognosis for those patients who fell into the two middle categories and was only significant for differentiating the patients with the best and worst prognostic factors. As a result, this score has limited utility for more generalized, widespread use.

The International Extranodal Lymphoma Study Group (IELSG) devised a 5-point scoring system based on age, Eastern Cooperative Oncology Group performance status, serum lactate dehydrogenase (LDH) level, CSF total protein concentration, and involvement of deep brain structures.⁶ This score was derived from a retrospective analysis of 378 patients from 48 centers; however, only 105 patients had complete data for inclusion in the model and the median follow-up was relatively short, only 24 months.

Recursive partitioning analysis (RPA) has been one of the most successful models used to develop predictive scores with easy and widespread applicability. The Radiation Therapy Oncology Group (RTOG) has used this analytic technique successfully to create prognostic scoring algorithms for a number of different malignancies including brain metastases and glioma.^7,8 Therefore, we chose to apply this analytic technique to 338 consecutive PCNSL patients seen at Memorial Sloan-Kettering Cancer Center (MSKCC; New York, NY) over the past two decades. Data from three prospective RTOG trials for newly diagnosed patients with PCNSL were used to validate the MSKCC RPA analysis and we studied the IELSG prognostic score in our data set.

	PATIENTS AND METHODS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
Patient Characteristics
Three hundred thirty-eight immunocompetent patients with PCNSL diagnosed between 1983 and December 31, 2003 were entered into a departmental database. Patient characteristics were collected before definitive PCNSL therapy and are summarized in Appendix Table A1 (online only). The median age of our patient population was 61 years (range, 19 to 89) and the median Karnofsky performance score (KPS) at diagnosis was 70 (range, 10 to 100). Treatment information was available on 99% of patients; 79% received methotrexate-based chemotherapy and 54% received whole-brain radiotherapy as part of their initial management. At completion of initial therapy, 71% of patients had achieved a radiologic complete response. In order to validate the IELSG score, we reviewed patient charts for the following three characteristics which were not routinely captured by our database: serum LDH, CSF protein concentration, and location of tumor at diagnosis.

RTOG Validation Data Set
Data from three RTOG trials (RTOG 8315, 8806, and 9310) for PCNSL patients were used as an external validation data set for the RPA analysis derived from the MSKCC data.^9,10,11 Each of these trials was a prospective clinical trial for patients with newly diagnosed PCNSL. A total of 194 patients with a median age of 60 (range, 19 to 83) and median initial KPS of 80 (range, 40 to 100) were enrolled onto these trials. Survival data were available for all 194 patients; however, only patients on protocols 8806 and 9310 (n = 150) had adequate information available to calculate failure-free survival.

Statistical Analysis
Overall survival was calculated from the date of diagnosis to death or last follow-up. Failure-free survival was calculated from date of PCNSL diagnosis to date of relapse, progression, death, or last follow-up. Survival curves were estimated using Kaplan-Meier survival methodology. Kaplan-Meier estimates of survival time in various groups were compared using the log-rank test (discrete variables). Statistical significance of continuous variables was assessed via the Cox proportional hazards model. Variables that were statistically significant in the univariate analysis (P < .05) were included in a multivariate analysis using the Cox proportional hazards regression model. A formal cut point analysis was performed using the maximum ² with P value adjustment method to determine the age values that were most strongly associated with overall survival.¹²

Recursive Partitioning Analysis
RPA sequentially divides patients into groups that are more homogeneous in terms of prognosis in a stepwise fashion.¹³ In the first step, the algorithm selects the variable that provides the best patient split, such that each of the two subgroups are more homogeneous with respect to outcome. In the subsequent steps, each subgroup is further dichotomized into smaller and more homogeneous groups by choosing the variable that best splits that subgroup. For all steps, any of the variables of interest are a potential candidate for selection. Only patients with data available for a particular variable were used to define that particular split. When a subgroup could not be further subdivided, namely it could not be made more homogeneous, the process stopped. This resulted in a large tree and so pruning techniques were implemented to minimize overfitting. To prune the tree, a cost complexity parameter of 0.015 was used. The prognostic groups were derived from the terminal nodes of the classification tree.

IELSG Prognostic Score
The IELSG prognostic scoring system is composed of five prognostic variables: age older than 60 years, Eastern Cooperative Oncology Group performance status higher than 1 (KPS < 70), elevated serum LDH level, high CSF protein concentration, and involvement of deep regions of the brain (periventricular regions, basal ganglia, brainstem, and/or cerebellum). Each variable is assigned a value of either 0, if favorable, or 1, if unfavorable and the sum of the five variables was used to calculate the patient's prognostic score. As part of our planned analysis, the IELSG score was computed for all MSKCC patients with adequate data. A score was not calculated for any patient who did not have data for all five variables. The RTOG data did not contain information on serum LDH, CSF protein, or location of brain lesions, so the IELSG prognostic score could not be evaluated for this patient group.

	RESULTS

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Overall Survival and Failure-Free Survival
Two hundred four patients died and the median overall survival was 37 months (95% CI, 31 to 42). The 1-, 2-, and 5-year survival rates were 76% (95% CI, 71% to 81%), 63% (95% CI, 58% to 68%), and 37% (95% CI, 31% to 43%), respectively. The median follow-up for surviving patients was 35 months (range, 0 to 229 months; Appendix Fig A1 online only). Increased age, low KPS, hemiparesis, altered mentation, and decreased creatinine clearance were significant predictors of worse overall survival in the univariate analysis. Deep brain location, serum LDH, and CSF total protein were not statistically significant in the univariate analysis (Appendix Table A2 online only); the year of diagnosis was not a significant predictor of survival. In the multivariate Cox regression model, only age and KPS were significant predictors of overall survival.

Two hundred fifty-seven patients experienced treatment failure (progression, relapse, or death) and the median failure-free survival from the date of diagnosis of PCNSL was 17 months (95% CI, 12 to 21; Fig A1). The failure-free survival at 1, 2, and 5 years was 57% (95% CI, 51% to 62%), 41% (95% CI, 36% to 47%), and 21% (95% CI, 16% to 26%), respectively. Similar to the findings for overall survival, increased age, symptoms of hemiparesis, and low KPS were significant predictors of failure-free survival in the univariate analysis. (Systemic involvement was also significantly associated with failure-free survival.) In the multivariate Cox regression model, only age and KPS were significant predictors of failure-free survival.

Cut point analysis determined that 50 years of age was the most significant point to determine prognosis; patients age 50 or younger had a substantially improved prognosis as opposed to those age 51 or older (P < .001). The two most frequently reported cut offs used in dichotomizing PCNSL patients by age are 50 years and 60 years. Therefore, we also looked at the patients divided into three groups: age 50 or younger, ages 51 to 60, and age older than 60. Patients who were 50 or younger had a significantly better prognosis than those who were in their 50s and those who were older than 60. However, there was no significant survival difference between patients in their 50s and patients older than 60 (P = .21). This provides additional support for using age 50 as a cut point.

RPA Analysis
Two hundred eighty-two patients were analyzed using RPA methodology; patients with documented systemic involvement on PSNCL staging or missing data critical to analyzing a split were excluded. RPA analysis of the MSKCC data set classified patients into three distinct prognostic groups (Table 1 and Fig 1). The first prognostic split was based on age with those older than 50 years having worse survival than those 50 years old. Patients 50 years of age had the best prognosis with a median survival of 8.5 years (95% CI, 4.7 to 16.8) and no other variable could segregate this group further; these patients were defined as class 1. In patients older than 50, the most significant variable that affected survival was a KPS 70. Older patients with a KPS 70 had a median survival of 3.2 years (95% CI, 2.6 to 4.3) and these patients became class 2. The survival of class 2 patients was significantly different from class 1 (P < .001), and those who were older than 50 and had a KPS less than 70 (P < .001). Older patients with a KPS less than 70 had the worst prognosis with a median survival of only 1.1 years (95% CI, 0.7 to 1.6), and these patients became class 3. These three groups defined the terminal subclassification of 282 patients when all necessary data were available. The prognostic score was also significantly associated with the median failure-free survival for classes 1, 2, and 3, which were 2.0 years (95% CI, 1.4 to 4.3), 1.8 years (95% CI, 1.2 to 2.3), and 0.6 years (95% CI, 0.4 to 1.0), respectively (Fig 2).

View larger version (10K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 1. Kaplan-Meier curve showing overall survival of the 282 Memorial Sloan-Kettering Cancer Center (MSKCC; New York, NY) primary CNS lymphoma patients stratified by recursive partitioning analysis classification. Age younger than 50, class 1; age older than 50 and Karnofsky performance score (KPS) higher than 70, class 2; age older than 50 and KPS less than 70, class 3.

View larger version (10K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 2. Kaplan-Meier curve showing failure-free survival of the 282 Memorial Sloan-Kettering Cancer Center (MSKCC; New York, NY) primary CNS lymphoma patients stratified by recursive partitioning analysis classification. Age younger than 50, class 1; age older than 50 and Karnofsky performance score (KPS) higher than 70, class 2; age older than 50 and KPS less than 70, class 3.

View larger version (10K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 3. Kaplan-Meier curve showing overall survival of the 194 Radiation Therapy Oncology Group (RTOG) primary CNS lymphoma patients stratified by recursive partitioning analysis classification. Age younger than 50, class 1; age older than 50 and Karnofsky performance score (KPS) higher than 70, class 2; age older than 50 and KPS less than 70, class 3.

View larger version (9K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 4. Kaplan-Meier curve showing failure-free survival of the 150 Radiation Therapy Oncology Group (RTOG) primary CNS lymphoma patients stratified by recursive partitioning analysis classification. Age younger than 50, class 1; age older than 50 and Karnofsky performance score (KPS) higher than 70, class 2; age older than 50 and KPS less than 70, class 3.

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
PCNSL is a rare disease that has only recently been the focus of systematic therapeutic investigation. Its rare incidence has made the prospective study of large populations impossible, so numerous phase II clinical trials have been conducted. However, it has been difficult to compare their outcomes. Patient selection bias remains a critical concern in any phase II study, and no successful phase III trial of patients with PCNSL has been completed. Prognostic factors exert a powerful effect on outcome of PCNSL patients and can affect the interpretation of clinical studies, particularly phase II trials. Therefore, it is critical to establish a reproducible and validated prognostic score that can be utilized by all PCNSL investigators. The MSKCC prognostic score meets these criteria and divides an otherwise heterogeneous population into three clear prognostic groups that predict failure free and overall survival regardless of treatment. Furthermore, the MSKCC prognostic score was validated using data collected from three prospective RTOG PCNSL trials.

The MSKCC prognostic score has the advantage of simplicity and widespread applicability. Virtually every clinical trial reported or conducted will include information regarding age and performance status. While both age and performance status have been widely reported and accepted as the two most consistent prognostic variables in PCNSL, there has not been a prior definition of the exact age or KPS needed to determine prognosis. Age 50 and age 60 have been reported previously as representing a prognostic separation in different series,^6,14 but to the best of our knowledge this is the first report to analyze age as a continuous variable with a cut point analysis to determine which age is best to discriminate prognosis. Furthermore, our analysis failed to confirm any variable other than age and KPS on multivariate testing.

Beyond age and KPS, a number of other prognostic factors have been proposed including those used in the IELSG prognostic score. Unfortunately, many of these variables are not uniformly obtained or reported for patients with PCNSL and, as a result, many patients cannot be categorized using the IELSG score. Application of the IELSG prognostic score to the MSKCC data set showed a statistically significant prediction of survival; however, we were only able to confirm a statistically significant difference between patients with none to one negative prognostic factors and patients with two to five negative prognostic factors. This may be a consequence of the longer follow-up available in the MSKCC data set. The median follow-up of patients included in the IELSG analysis was only 2 years and no patient had follow-up beyond 3 years. If we had truncated our analysis at 3 years, it is likely that we would have found a statistically significant result among the three IELSG subgroups (Fig A2).

The major limitation of analyzing the MSKCC data set is that it represents a large single institution series, which may have an inherent selection bias resulting in better than usual outcome or prognostic factors. However, the median age of our patients was similar to that reported in most large series, including population-based reports.^1,2,15-17 The median KPS did not appear to be inflated as patients with a KPS as low as 10 were included in the analysis. Furthermore, the validation of the MSKCC prognostic score using the prospective data collected from multicenter RTOG trials strongly substantiates this approach.

In conclusion, the MSKCC PCNSL RPA classification represents an important new predictive model for patients with newly diagnosed PCNSL. We would propose adopting this score in the design and reporting of future clinical trials.

	Appendix

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 

View larger version (9K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig A1. Kaplan-Meier curve showing failure-free and overall survival for all 338 Memorial Sloan-Kettering Cancer Center (MSKCC; New York, NY) primary CNS lymphoma (PCNSL) patients.

View larger version (8K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig A2. Kaplan-Meier curve showing overall survival of 113 Memorial Sloan-Kettering Cancer Center (MSKCC; New York, NY) patients stratified using the International Extranodal Lymphoma Study Group prognostic score.

	Authors' Disclosures of Potential Conflicts of Interest

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

	Author Contributions

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 

Conception and design: Lauren E. Abrey, Leah Ben-Porat, Katherine S. Panageas, Joachim Yahalom, Lisa M. DeAngelis Financial support: Lauren E. Abrey, Lisa M. DeAngelis Administrative support: Lauren E. Abrey, Walter Curran, Lisa M. DeAngelis Provision of study materials or patients: Lauren E. Abrey, Brian Berkey, Walter Curran, Christopher Schultz, Steven Leibel, Diana Nelson, Minish Mehta, Lisa M. DeAngelis Collection and assembly of data: Lauren E. Abrey, Leah Ben-Porat, Brian Berkey Data analysis and interpretation: Lauren E. Abrey, Leah Ben-Porat, Katherine S. Panageas, Joachim Yahalom, Lisa M. DeAngelis Manuscript writing: Lauren E. Abrey, Leah Ben-Porat, Lisa M. DeAngelis Final approval of manuscript: Lauren E. Abrey, Leah Ben-Porat, Katherine S. Panageas, Joachim Yahalom, Brian Berkey, Walter Curran, Christopher Schultz, Steven Leibel, Diana Nelson, Minish Mehta, Lisa M. DeAngelis

 

	NOTES

 
published online ahead of print at www.jco.org on November 20, 2006.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

	REFERENCES

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
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3. Corry J, Smith JG, Wirth A, et al: Primary central nervous system lymphoma: Age and performance status are more important than treatment modality. Int J Radiat Oncol Biol Phys 41:615-620, 1998[CrossRef][Medline]

4. Abrey LE, Yahalom J, DeAngelis LM: Treatment for primary CNS lymphoma: The next step. J Clin Oncol 18:3144-3150, 2000[Abstract/Free Full Text]

5. Bessell EM, Graus F, Lopez-Guillermo A, et al: Primary non-Hodgkin's lymphoma of the CNS treated with CHOD/BVAM or BVAM chemotherapy before radiotherapy: Long-term survival and prognostic factors. Int J Radiat Oncol Biol Phys 59:501-508, 2004[CrossRef][Medline]

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7. Scott CB, Scarantino C, Urtasun R: Validation and predictive power of Radiation Therapy Oncology Group (RTOG) recursive partitioning analysis classes for malignant glioma patients: A report using RTOG 90-06. Int J Radiat Oncol Biol Phys 40:51-55, 1998[CrossRef][Medline]

8. Gaspar LE, Scott C, Murray K, et al: Validation of the RTOG recursive partitioning analysis (RPA) classification for brain metastases. Int J Radiat Oncol Biol Phys 47:1001-1006, 2000[CrossRef][Medline]

9. DeAngelis LM, Seiferheld W, Schold SC, et al: Radiation Therapy Oncology Group Study 93-10 combination chemotherapy and radiotherapy for primary central nervous system lymphoma: Radiation Therapy Oncology Group study 93-10. J Clin Oncol 20:4643-4648, 2002[Abstract/Free Full Text]

10. Nelson DF, Martz KL, Bonner H, et al: Non-Hodgkin's lymphoma of the brain: Can high dose, large volume radiation therapy improve survival? Report on a prospective trial by the Radiation Therapy Oncology Group (RTOG): RTOG 8315. Int J Radiat Oncol Biol Phys 23:9-17, 1992[Medline]

11. Schultz C, Scott C, Sherman W, et al: Preirradiation chemotherapy with cyclophosphamide, doxorubicin, vincristine, and dexamethasone for primary CNS lymphomas: Initial report of radiation therapy oncology group protocol 88-06. J Clin Oncol 14:556-564, 1996[Abstract/Free Full Text]

12. Mazumdar M, Glassman JR: Categorizing a prognostic variable: Review of methods, code for easy implementation and applications to decision-making about cancer treatments. Stat Med 19:113-132, 2000[CrossRef][Medline]

13. Breiman L, Friedman JH, Olshen RA, et al: Classification and regressions trees. Belmont, CA, Wadsworth Inc, 1984

14. DeAngelis LM, Yahalom J, Heinemann MH: Primary CNS lymphoma: Combined treatment with chemotherapy and radiotherapy. Neurology 40:80-86, 1990[Abstract/Free Full Text]

15. Pels H, Schmidt-Wolf IG, Glasmacher A, et al: Primary central nervous system lymphoma: Results of a pilot and phase II study of systemic and intraventricular chemotherapy with deferred radiotherapy. J Clin Oncol 21:4489-4495, 2003[Abstract/Free Full Text]

16. O'Brien P, Roos D, Pratt G, et al: Phase II multicenter study of brief single-agent methotrexate followed by irradiation in primary CNS lymphoma. J Clin Oncol 18:519-526, 2000[Abstract/Free Full Text]

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Submitted July 19, 2006; accepted October 2, 2006.

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