Prognostic factors in primary central nervous system lymphoma. Issue 6 (12th November 2022)

• Record Type: Journal Article
• Authors: Morales-Martinez, Andrea; Nichelli, Lucia; Hernandez-Verdin, Isaias; Houillier, Caroline; Alentorn, Agustí; Hoang-Xuan, Khê
• Is Part Of: Current opinion in oncology. Volume 34:Issue 6(2022)
• Page Start: 676
• Page End: 684
• Publication Date: 2022-11-12
• Subjects: immunocompetent -- primary central nervous system lymphoma -- prognosis -- risk factors; Oncology -- Periodicals; 616.994
• DOI: 10.1097/CCO.0000000000000896
• Languages: English
• ISSNs: 1040-8746

Abstract

Abstract : Purpose of review: Primary 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 findings: Age 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. Summary: Validation 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.