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An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers
Background: Systemic inflammatory indicators are clinically significant in guiding diffuse large B-cell lymphoma (DLBCL) prognosis. However, which inflammatory markers are the best predictors of DLBCL prognosis is still unclear. In this study, we aimed to create a nomogram based on the best inflamma...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408319/ https://www.ncbi.nlm.nih.gov/pubmed/37551117 http://dx.doi.org/10.1177/15330338231180785 |
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author | Liu, Yajiao Sheng, Li Hua, Haiying Zhou, Jingfen Zhao, Ying Wang, Bei |
author_facet | Liu, Yajiao Sheng, Li Hua, Haiying Zhou, Jingfen Zhao, Ying Wang, Bei |
author_sort | Liu, Yajiao |
collection | PubMed |
description | Background: Systemic inflammatory indicators are clinically significant in guiding diffuse large B-cell lymphoma (DLBCL) prognosis. However, which inflammatory markers are the best predictors of DLBCL prognosis is still unclear. In this study, we aimed to create a nomogram based on the best inflammatory markers and clinical indicators to predict the overall survival of patients with DLBCL. Patients and methods: We analyzed data from 423 DLBCL patients from two institutions and divided them into a training set, an internal validation set, and an external validation set (n = 228, 97, and 98, respectively). The least absolute shrinkage and selection operator and Cox regression analysis were used to develop nomograms. We assessed model fit using the Akaike information criterion and Bayesian information criterion. The concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's predictive performance and clinical net benefit and compared with the International Prognostic Index (IPI) and National Comprehensive Cancer Network (NCCN)-IPI. Results: The inclusion variables for the nomogram model were age, Eastern Cooperative Oncology Group performance status, lactate dehydrogenase level, the systemic immune-inflammation index (SII), the prognostic nutritional index (PNI), and β-2 microglobulin (β-2 MG) level. In the training cohort, the nomogram showed better goodness of fit than the IPI and NCCN-IPI. The C-index of the nomogram (0.804, 95% CI: 0.751-0.857) outperformed the IPI (0.690, 95% CI: 0.629-0.751) and NCCN-IPI (0.691, 95% CI: 0.632-0.750). The calibration curve, ROC curve, and DCA curve analysis showed that the nomogram has satisfactory predictive power and clinical utility. Similar results were found in the validation cohort. Conclusion: The nomogram integrated with the clinical characteristics and inflammatory markers is beneficial to predict the prognosis of patients with DLBCL. |
format | Online Article Text |
id | pubmed-10408319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-104083192023-08-09 An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers Liu, Yajiao Sheng, Li Hua, Haiying Zhou, Jingfen Zhao, Ying Wang, Bei Technol Cancer Res Treat Original Article Background: Systemic inflammatory indicators are clinically significant in guiding diffuse large B-cell lymphoma (DLBCL) prognosis. However, which inflammatory markers are the best predictors of DLBCL prognosis is still unclear. In this study, we aimed to create a nomogram based on the best inflammatory markers and clinical indicators to predict the overall survival of patients with DLBCL. Patients and methods: We analyzed data from 423 DLBCL patients from two institutions and divided them into a training set, an internal validation set, and an external validation set (n = 228, 97, and 98, respectively). The least absolute shrinkage and selection operator and Cox regression analysis were used to develop nomograms. We assessed model fit using the Akaike information criterion and Bayesian information criterion. The concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's predictive performance and clinical net benefit and compared with the International Prognostic Index (IPI) and National Comprehensive Cancer Network (NCCN)-IPI. Results: The inclusion variables for the nomogram model were age, Eastern Cooperative Oncology Group performance status, lactate dehydrogenase level, the systemic immune-inflammation index (SII), the prognostic nutritional index (PNI), and β-2 microglobulin (β-2 MG) level. In the training cohort, the nomogram showed better goodness of fit than the IPI and NCCN-IPI. The C-index of the nomogram (0.804, 95% CI: 0.751-0.857) outperformed the IPI (0.690, 95% CI: 0.629-0.751) and NCCN-IPI (0.691, 95% CI: 0.632-0.750). The calibration curve, ROC curve, and DCA curve analysis showed that the nomogram has satisfactory predictive power and clinical utility. Similar results were found in the validation cohort. Conclusion: The nomogram integrated with the clinical characteristics and inflammatory markers is beneficial to predict the prognosis of patients with DLBCL. SAGE Publications 2023-08-08 /pmc/articles/PMC10408319/ /pubmed/37551117 http://dx.doi.org/10.1177/15330338231180785 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Article Liu, Yajiao Sheng, Li Hua, Haiying Zhou, Jingfen Zhao, Ying Wang, Bei An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers |
title | An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers |
title_full | An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers |
title_fullStr | An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers |
title_full_unstemmed | An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers |
title_short | An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers |
title_sort | externally validated nomogram for predicting the overall survival of patients with diffuse large b-cell lymphoma based on clinical characteristics and systemic inflammatory markers |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408319/ https://www.ncbi.nlm.nih.gov/pubmed/37551117 http://dx.doi.org/10.1177/15330338231180785 |
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