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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...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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 |
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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 |
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