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

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Abstract
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.

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