Cargando…

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 patie...

Descripción completa

Detalles Bibliográficos
Autores principales: Wu, Zhentian, Wang, Chenyi, Lyu, Yao, Lin, Zheshen, Lu, Ming, Wang, Shixiong, Wang, Bingxuan, Yang, Na, Li, Yeye, Wang, Jianhong, Duan, Xiaohui, Zhang, Na, Gao, Jing, Zhang, Yuan, Hao, Miaowang, Wang, Zhe, Gao, Guangxun, Liang, Rong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086228/
https://www.ncbi.nlm.nih.gov/pubmed/37056341
http://dx.doi.org/10.3389/fonc.2023.1104425
_version_ 1785022103966187520
author Wu, Zhentian
Wang, Chenyi
Lyu, Yao
Lin, Zheshen
Lu, Ming
Wang, Shixiong
Wang, Bingxuan
Yang, Na
Li, Yeye
Wang, Jianhong
Duan, Xiaohui
Zhang, Na
Gao, Jing
Zhang, Yuan
Hao, Miaowang
Wang, Zhe
Gao, Guangxun
Liang, Rong
author_facet Wu, Zhentian
Wang, Chenyi
Lyu, Yao
Lin, Zheshen
Lu, Ming
Wang, Shixiong
Wang, Bingxuan
Yang, Na
Li, Yeye
Wang, Jianhong
Duan, Xiaohui
Zhang, Na
Gao, Jing
Zhang, Yuan
Hao, Miaowang
Wang, Zhe
Gao, Guangxun
Liang, Rong
author_sort Wu, Zhentian
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10086228
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-100862282023-04-12 A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis Wu, Zhentian Wang, Chenyi Lyu, Yao Lin, Zheshen Lu, Ming Wang, Shixiong Wang, Bingxuan Yang, Na Li, Yeye Wang, Jianhong Duan, Xiaohui Zhang, Na Gao, Jing Zhang, Yuan Hao, Miaowang Wang, Zhe Gao, Guangxun Liang, Rong Front Oncol Oncology 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. Frontiers Media S.A. 2023-03-28 /pmc/articles/PMC10086228/ /pubmed/37056341 http://dx.doi.org/10.3389/fonc.2023.1104425 Text en Copyright © 2023 Wu, Wang, Lyu, Lin, Lu, Wang, Wang, Yang, Li, Wang, Duan, Zhang, Gao, Zhang, Hao, Wang, Gao and Liang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Wu, Zhentian
Wang, Chenyi
Lyu, Yao
Lin, Zheshen
Lu, Ming
Wang, Shixiong
Wang, Bingxuan
Yang, Na
Li, Yeye
Wang, Jianhong
Duan, Xiaohui
Zhang, Na
Gao, Jing
Zhang, Yuan
Hao, Miaowang
Wang, Zhe
Gao, Guangxun
Liang, Rong
A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis
title A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis
title_full A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis
title_fullStr A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis
title_full_unstemmed A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis
title_short A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis
title_sort novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: a real-world data analysis
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086228/
https://www.ncbi.nlm.nih.gov/pubmed/37056341
http://dx.doi.org/10.3389/fonc.2023.1104425
work_keys_str_mv AT wuzhentian anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangchenyi anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT lyuyao anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT linzheshen anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT luming anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangshixiong anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangbingxuan anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT yangna anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT liyeye anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangjianhong anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT duanxiaohui anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT zhangna anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT gaojing anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT zhangyuan anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT haomiaowang anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangzhe anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT gaoguangxun anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT liangrong anovelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wuzhentian novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangchenyi novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT lyuyao novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT linzheshen novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT luming novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangshixiong novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangbingxuan novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT yangna novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT liyeye novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangjianhong novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT duanxiaohui novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT zhangna novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT gaojing novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT zhangyuan novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT haomiaowang novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT wangzhe novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT gaoguangxun novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis
AT liangrong novelinflammationrelatedprognosticmodelforpredictingtheoverallsurvivalofprimarycentralnervoussystemlymphomaarealworlddataanalysis