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

Zhentian Wu; Chenyi Wang; Yao Lyu; Zheshen Lin; Ming Lu; Shixiong Wang; Bingxuan Wang; Na Yang; Yeye Li; Jianhong Wang; Xiaohui Duan; Na Zhang; Jing Gao; Yuan Zhang; Miaowang Hao; Zhe Wang; Guangxun Gao; Rong Liang

doi:10.3389/fonc.2023.1104425

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

Front Oncol. 2023 Mar 28:13:1104425. doi: 10.3389/fonc.2023.1104425. eCollection 2023.

Authors

Zhentian Wu¹, Chenyi Wang², Yao Lyu¹, Zheshen Lin¹, Ming Lu¹, Shixiong Wang¹, Bingxuan Wang¹, Na Yang¹, Yeye Li¹, Jianhong Wang¹, Xiaohui Duan¹, Na Zhang¹, Jing Gao³, Yuan Zhang⁴, Miaowang Hao³, Zhe Wang⁵, Guangxun Gao¹, Rong Liang¹

Affiliations

¹ Department of Hematology, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi, China.
² Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi, China.
³ Department of Hematology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, China.
⁴ Department of Respiratory, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, China.
⁵ Department of Pathology, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi, China.

Abstract

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.

Keywords: PCNSL; SIRI; nomogram; prognostic model; risk stratification.

Grants and funding

This study was supported by the Major Clinical Technology Innovation Project (No. XJZT19Z28).