Outcome and Prognostic Factors of Primary Central Nervous System Lymphoma in Southern Thailand

Survival outcomes and treatment experience of 124 patients with primary central nervous system lymphoma

PURPOSE

Primary central nervous system lymphoma (PCNSL) is a rare malignancy of the central nervous system with high invasiveness. There is little consensus on the treatment of PCNSL. This study retrospectively studied data from PCNSL patients in a single center to summarize treatment experience and explore prognostic factors.

METHODS

Survival curves were drawn using the Kaplan-Meier method and prognostic factors were analyzed using Cox's hazards model.

RESULTS

In multivariate analysis, cerebrospinal fluid lactic acid dehydrogenase (CSF LDH; p = 0.005 and p = 0.002), neutrophil to lymphocyte ratio (NLR; p = 0.014 and p = 0.038), and completion of four cycles of induction therapy (p < 0.001and p < 0.001) were significant and independent predictors of overall survival (OS) and progression-free survival (PFS), respectively.

CONCLUSION

On the basis of this study, we propose that PCNSL patients should receive early induction therapy with sufficient cycles. Subsequent consolidation therapy can prevent relapses and improve survival. In patients with PCNSL, the independent prognostic factors for OS and PFS were CSF LDH level, NLR, and full cycles of induction therapy.

Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors

Objectives

It can be challenging in some situations to distinguish primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) based on magnetic resonance imaging (MRI) scans, especially those involving the corpus callosum. The objective of this study was to assess the diagnostic performance of deep learning (DL) models between PCNSLs and GBMs in corpus callosal tumors.

Materials and Methods

The axial T1-weighted gadolinium-enhanced MRI scans of 274 individuals with pathologically confirmed PCNSL (n = 94) and GBM (n = 180) were examined. After image pooling, pre-operative MRI scans were randomly split with an 80/20 procedure into a training dataset (n = 709) and a testing dataset (n = 177) for DL model development. Therefore, the DL model was deployed as a web application and validated with the unseen images (n = 114) and area under the receiver operating characteristic curve (AUC); other outcomes were calculated to assess the discrimination performance.

Results

The first baseline DL model had an AUC of 0.77 for PCNSL when evaluated with unseen images. The 2nd model with ridge regression regularization and the 3rd model with drop-out regularization increased an AUC of 0.83 and 0.84. In addition, the last model with data augmentation yielded an AUC of 0.57.

Conclusion

DL with regularization may provide useful diagnostic information to help doctors distinguish PCNSL from GBM.

Clinicopathological analysis and specific discriminating markers of interleukin detection in cerebrospinal fluid with primary central nervous system lymphoma: results from a retrospective study

Primary central nervous system lymphoma (PCNSL) is special extranodal malignant non-Hodgkin lymphomas. This study analyzed clinical features and prognostic factors of PCNSL and evaluated the difference of interleukin (IL) concentrations in cerebrospinal fluid (CSF) between PCNSL and systemic non-Hodgkin lymphoma (sNHL). Patients consecutive newly diagnosed with PCNSL were recruited, the demographic and clinicopathological data were retrospectively analyzed, and the potential prognostic factors for overall survival (OS) were identified with survival analysis. 27 patients with PCNSL and 21 patients with sNHL collected CSF IL-5, IL-6, and IL-10 concentrations at diagnosis. The difference in interleukin (IL) concentrations in two diseases was analyzed to evaluate the value of IL concentrations. A total of 64 patients with PCNSL were enrolled, the median age was 54.50 years (range 16-85 years); male: female ratio was 1.91. Headache was the most common complaint symptom involved in 42.19% (27/64) of patients. Diffuse large B-cell lymphoma (DLBCL) accounted for 89.06% (57/64) of patients; other uncommon types accounted for 3.13% (2/64). In prognostic analysis, multiple lesions and Ki67 ≥ 75% expression exhibited a worse prognosis(P = 0.041), and patients with autologous hematopoietic stem cell transplantation (auto-HSCT) treatment presented superior OS (P < 0.05). In multivariate analysis, BCL2 expression was revealed as an unfavorable prognostic marker, and auto-HSCT was revealed as a favorable prognostic marker. CSF IL-10 concentration in patients with PCNSL was significantly higher than sNHL (P = 0.000) and excluded other histopathology of NHL; IL-10 value was still significantly different between DLBCL of PCNSL and sDLBCL (P = 0.003). In ROC curve analysis, the cutoff value of IL-10 was 0.43 pg/mL for the diagnosis value of PCNSL, sensitivity was 96.3%, specificity was 66.67%, and AUC was 0.84 (0.71-0.96). Although IL-6 concentration did not differ in the two groups, IL-10/IL-6 ratio was meaningful, with a cutoff value of 0.21, sensitivity of 81.48%, specificity of 80.95%, and AUC of 0.83 (0.71-0.95). This study highlights the characteristics of patients with PCNSL, potential prognostic makers also have been explained. CSF interleukin (IL) concentrations revealed IL-10 levels, and IL-10/IL-6 ratio may represent a useful biomarker in the differential diagnosis of PCNSL and sNHL.

Survival prediction and conditional survival of primary central nervous system lymphoma: A population-based study

BACKGROUND

Primary central nervous system lymphoma is a rare and highly aggressive type of non-Hodgkin lymphoma. This study used population-based data to evaluate the clinical characteristics and prognostic factors of primary central nervous system lymphoma and develop a prediction model to estimate survival.

METHODS

Patients' data were extracted from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors were identified using univariate and multivariate Cox regression analyses. Conditional survival estimates were calculated as CS(x y) = S(x + y)/S(X), and a nomogram was built to predict patient prognosis.

RESULTS

In total, 2563 patients with primary central nervous system lymphoma were included. Multivariate Cox analysis showed that age at diagnosis, sex, histology, tumor site, surgery, chemotherapy, and marital status were independent prognostic factors of overall survival. The 1-year conditional survival increased with time, and our nomogram model showed favorable discriminative ability.

CONCLUSION

At the population level, our study found that gross total resection and chemotherapy improved the prognosis of patients with primary central nervous system lymphoma. However, the prognosis of black patients was poor. Conditional survival provided a more accurate and dynamic survival estimate. Moreover, our nomogram had a good performance and could help predict the overall survival of these patients.