The management of elderly patients suffering from primary central nervous system (CNS) lymphoma, who represent a rapidly growing population, is challenging. Despite the advances made in PCNSL treatment, the prognosis in older patients remains unsatisfactory. The high risk of systemic and CNS toxicity induced by a high-dose chemotherapy regimen and radiation therapy, respectively, limits the use of consolidation phase treatments in elderly patients and contributes to the poor outcome of these patients. Here, we review the current treatment strategies and ongoing trials proposed for elderly PCNSL patients.
Purpose of reviewPrimary central nervous system lymphoma (PCNSL) is a rare and aggressive extranodal diffuse large B cell lymphoma. Despite its apparent immunopathological homogeneity, PCNSL displays a wide variability in outcome. Identifying prognostic factors is of importance for patient stratification and clinical decision-making. The purpose of this review is to focus on the clinical, neuroradiological and biological variables correlated with the prognosis at the time of diagnosis in immunocompetent patients.Recent findingsAge and performance status remain the most consistent clinical prognostic factors. The current literature suggests that neurocognitive dysfunction is an independent predictor of poor outcome. Cumulating data support the prognostic value of increased interleukin-10 level in the cerebrospinal fluid (CSF), in addition to its interest as a diagnostic biomarker. Advances in neuroimaging and in omics have identified several semi-quantitative radiological features (apparent diffusion restriction measures, dynamic contrast-enhanced perfusion MRI (pMRI) pattern and 18F-fluorodeoxyglucose metabolism) and molecular genetic alterations with prognostic impact in PCNSL.SummaryValidation of new biologic and neuroimaging markers in prospective studies is required before integrating future prognostic scoring systems. In the era of radiomic, large clinicoradiological and molecular databases are needed to develop multimodal artificial intelligence algorithms for the prediction of accurate outcome.
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