A Novel Prognostic Marker for Primary CNS Lymphoma: Lactate Dehydrogenase-to-Lymphocyte Ratio Improves Stratification of Patients Within the Low and Intermediate MSKCC Risk Groups

Pre-treatment systemic inflammation response index and systemic immune inflammation in patients with primary central nerve system lymphoma as a useful prognostic indicator

PURPOSE

The systemic inflammation response index (SIRI) and systemic immune-inflammation index (SII) are based on neutrophil, monocyte, platelet, and lymphocyte counts. The SIRI and SII are used to predict the survival of patients with malignant tumors. It is well known that the inflammatory immune response is closely related to cancer occurrence and progression. In the present study, we evaluated the potential prognostic significance of SIRI and SII in patients with primary central nervous system lymphoma (PCNSL).

METHODS

Fifty-eight consecutive patients were enrolled in this study between November 2006 and May 2022. Among the 58 patients, 47 patients with sufficient blood test data and follow-up were analyzed. The patients with steroid intake at the time point of the blood test and higher C-reactive protein were excluded.

RESULTS

The median follow-up and survival times were 31 and 36 months, respectively. The optimal cutoff SIRI value was based on the receiver operating characteristic curve (ROC) for overall survival (OS) and stratified patients into low (< 1.43 × 109/L, n = 22) and high (≥ 1.43 × 109/L, n = 25) SIRI groups. The optimal cutoff SII value based on the ROC for OS stratified patients into low (< 694.9, n = 28) and high (≥ 694.9, n = 19) SII groups. A low SIRI value was associated with longer OS (p = 0.006). Furthermore, a low SII value was associated with longer OS (p = 0.044). The prognostic factors associated with prolonged survival in univariate analysis using the Cox proportional hazard model were age < 65 years, low SIRI, and low SII. The multivariate analysis demonstrated that age < 65 years and low SIRI independently predicted longer OS.

CONCLUSION

Simple, less expensive, and routinely ordered preoperative blood count assessments such as SIRI and SII predict the OS of patients with PCNSL. This study demonstrated that PCNSL is associated with pre-treatment systemic immune-inflammation states.

Relevance of different prognostic scores in primary CNS lymphoma in the era of intensified treatment regimens: A retrospective, multicenter analysis of 174 patients

OBJECTIVES

Treatment intensification (including consolidative high-dose chemotherapy with autologous stem cell transplantation [HDT-ASCT]) significantly improved outcome in primary central nervous system lymphoma (PCNSL) patients.

METHODS

We conducted a multicenter, retrospective analysis of newly diagnosed PCNSL patients, treated with intensified treatment regimens. The following scores were evaluated in terms of overall survival (OS) and progression-free survival (PFS): Memorial Sloan-Kettering Cancer Center (MSKCC), International Extranodal Lymphoma Study Group (IELSG), and three-factor (3F) prognostic score. Further, all scores were comparatively investigated for model quality and concordance.

RESULTS

Altogether, 174 PCNSL patients were included. One hundred and five patients (60.3%) underwent HDT-ASCT. Two-year OS and 2-year PFS for the entire population were 73.3% and 48.5%, respectively. The MSKCC (p = .003) and 3F score (p < .001), but not the IELSG score (p = .06), had the discriminatory power to identify different risk groups for OS. In regard to concordance, the 3F score (C-index [0.71]) outperformed both the MSKCC (C-index [0.64]) and IELSG (C-index [0.53]) score. Moreover, the superiority of the 3F score was shown for PFS, successfully stratifying patients in three risk groups, which also resulted in the highest C-index (0.66).

CONCLUSION

The comparative analysis of established PCNSL risk scores affirm the clinical utility of the 3F score stratifying the widest prognostic spectrum among PCNSL patients treated with intensified treatment approaches.

C-reactive protein levels, the prognostic nutritional index, and the lactate dehydrogenase-to-lymphocyte ratio are important prognostic factors in primary central nervous system lymphoma: a single-center study of 223 patients

Primary central nervous system lymphoma (PCNSL) is a rare and highly aggressive type of extranodal non-Hodgkin lymphoma (NHL), and the prognosis is poor. Currently, the most used prognostic models are the Memorial Sloan-Kettering Cancer Center (MSKCC) and International Extranodal Lymphoma Study Group (IELSG) scores; however, their predictive effects are changing with increasing incidence and changing treatment regimens. A growing body of evidence has demonstrated that inflammatory and nutritional markers are factors that can determine tumor prognosis. Therefore, the aim of this study was to identify and validate novel prognostic factors for PCNSL. Clinical information was collected from 223 patients with PCNSL. Patients younger than 18 years of age were excluded. Progression-free survival (PFS) and overall survival (OS) were used as endpoints, and receiver operating characteristic (ROC) curve analyses were conducted to determine the cutoff values for the inflammatory indicators. Correlations between variables and PFS or OS were assessed using univariate and multivariate analyses, and positive indicators were selected for survival analysis. A prognostic nutritional index (PNI) < 49.38 was associated with worse PFS (p = 0.003), and outcomes significantly differed between patients with a PNI ≥ 49.38 and < 49.38 (p < 0.001). Age < 60 years (p < 0.001) and C-reactive protein (CRP) levels < 3.14 (p = 0.001) were associated with better OS. In elderly patients (≥ 60 years), a lactate dehydrogenase-to-lymphocyte ratio (LLR) < 95.69 (p = 0.021) was associated with better OS, and the outcome significantly differed between patients with an LLR ≥ 95.69 and LLR < 95.69 (p = 0.015). The PNI and CRP levels are prognostic factors for PCNSL, and CRP was the first time shown to be a prognosis factor of PCNSL. In elderly patients with PCNSL, the LLR can predict prognosis.

Extranodal lymphoma: pathogenesis, diagnosis and treatment

Approximately 30% of lymphomas occur outside the lymph nodes, spleen, or bone marrow, and the incidence of extranodal lymphoma has been rising in the past decade. While traditional chemotherapy and radiation therapy can improve survival outcomes for certain patients, the prognosis for extranodal lymphoma patients remains unsatisfactory. Extranodal lymphomas in different anatomical sites often have distinct cellular origins, pathogenic mechanisms, and clinical manifestations, significantly influencing their diagnosis and treatment. Therefore, it is necessary to provide a comprehensive summary of the pathogenesis, diagnosis, and treatment progress of extranodal lymphoma overall and specifically for different anatomical sites. This review summarizes the current progress in the common key signaling pathways in the development of extranodal lymphomas and intervention therapy. Furthermore, it provides insights into the pathogenesis, diagnosis, and treatment strategies of common extranodal lymphomas, including gastric mucosa-associated lymphoid tissue (MALT) lymphoma, mycosis fungoides (MF), natural killer/T-cell lymphoma (nasal type, NKTCL-NT), and primary central nervous system lymphoma (PCNSL). Additionally, as PCNSL is one of the extranodal lymphomas with the worst prognosis, this review specifically summarizes prognostic indicators and discusses the challenges and opportunities related to its clinical applications. The aim of this review is to assist clinical physicians and researchers in understanding the current status of extranodal lymphomas, enabling them to make informed clinical decisions that contribute to improving patient prognosis.

A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis

Background

Primary central nervous system lymphoma (PCNSL) is a type of extranodal non-Hodgkin lymphoma. Although there are widely used prognostic scores, their accuracy and practicality are insufficient. Thus, a novel prognostic prediction model was developed for risk stratification of PCNSL patients in our research.

Methods

We retrospectively collected 122 patients with PCNSL from two medical centers in China from January 2010 to June 2022. Among them, 72 patients were used as the development cohort to construct a new model, and 50 patients were used for the validation. Then, by using univariate and multivariate Cox regression analsis and Lasso analysis, the Xijing model was developed and composed of four variables, including lesion number, β2-microglobulin (β2-MG), systemic inflammation response index (SIRI) and Karnofsky performance status (KPS). Finally, we evaluated the Xijing model through internal and external validation.

Results

Compared with the original prognostic scores, the Xijing model has an overall improvement in predicting the prognosis of PCNSL according to the time-dependent area under the curve (AUC), Harrell’s concordance index (C-index), decision curve analysis (DCA), integrated discrimination improvement (IDI) and continuous net reclassification index (NRI). For overall survival (OS) and progression-free survival (PFS), the Xijing model can divide PCNSL patients into three groups, and shows more accurate stratification ability. In addition, the Xijing model can still stratify and predict prognosis similarly better in the elderly with PCNSL and subgroups received high-dose methotrexate (HD-MTX) or Bruton’s tyrosine kinase inhibitors (BTKi). Finally, external validation confirmed the above results.

Conclusions

Integrating four prognostic factors, including imaging findings, tumor burden, systemic inflammation response index, and comprehensive physical condition, we provided a novel prognostic model for PCNSL based on real-world data and evaluated its predictive capacity.

Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma

Background

The prognostic roles of clinical and laboratory markers have been exploited to model risk in patients with primary CNS lymphoma, but these approaches do not fully explain the observed variation in outcome. To date, neuroimaging or molecular information is not used. The aim of this study was to determine the utility of radiomic features to capture clinically relevant phenotypes, and to link those to molecular profiles for enhanced risk stratification.

Methods

In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005-2018) and an external validation site in South Korea (44 patients, 2013-2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines.

Results

The radiomic risk score, consisting of 20 mostly textural features, was a strong and independent predictor of survival (multivariate hazard ratio = 6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients.

Conclusions

The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.