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

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.

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.

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