A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis
BackgroundPrimary 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 patient...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1104425/full |
| _version_ | 1797858975205556224 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Zhentian Wu |
| collection | DOAJ |
| description | BackgroundPrimary 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.MethodsWe 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.ResultsCompared 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.ConclusionsIntegrating 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. |
| first_indexed | 2024-04-09T21:22:04Z |
| format | Article |
| id | doaj.art-d22933ea2c5b4abb88ec6a72b8f1e87e |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T21:22:04Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-d22933ea2c5b4abb88ec6a72b8f1e87e2023-03-28T05:06:27ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-03-011310.3389/fonc.2023.11044251104425A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysisZhentian Wu0Chenyi Wang1Yao Lyu2Zheshen Lin3Ming Lu4Shixiong Wang5Bingxuan Wang6Na Yang7Yeye Li8Jianhong Wang9Xiaohui Duan10Na Zhang11Jing Gao12Yuan Zhang13Miaowang Hao14Zhe Wang15Guangxun Gao16Rong Liang17Department of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Respiratory, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Pathology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Hematology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaBackgroundPrimary 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.MethodsWe 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.ResultsCompared 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.ConclusionsIntegrating 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.https://www.frontiersin.org/articles/10.3389/fonc.2023.1104425/fullPCNSLprognostic modelnomogramrisk stratificationSIRI |
| spellingShingle | 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 A novel inflammation-related prognostic model for predicting the overall survival of primary central nervous system lymphoma: A real-world data analysis Frontiers in Oncology PCNSL prognostic model nomogram risk stratification SIRI |
| 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 | PCNSL prognostic model nomogram risk stratification SIRI |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1104425/full |
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