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Prognostic model on overall survival in elderly nasopharyngeal carcinoma patients: a recursive partitioning analysis identifying pre-treatment risk stratification
BACKGROUND: We aimed to evaluate the optimal management for elderly patients with nasopharyngeal carcinoma (NPC) with intensity-modulated radiotherapy (IMRT). METHODS: A total of 283 elderly patients with NPC diagnosed from 2015 to 2019 were enrolled in the study. Overall survival (OS) was the prima...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10290415/ https://www.ncbi.nlm.nih.gov/pubmed/37353800 http://dx.doi.org/10.1186/s13014-023-02272-x |
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author | Li, Ying Weng, Youliang Huang, Zongwei Pan, Yuhui Cai, Sunqin Ding, Qin Wu, Zijie Chen, Xin Lu, Jun Hu, Dan Qiu, Sufang |
author_facet | Li, Ying Weng, Youliang Huang, Zongwei Pan, Yuhui Cai, Sunqin Ding, Qin Wu, Zijie Chen, Xin Lu, Jun Hu, Dan Qiu, Sufang |
author_sort | Li, Ying |
collection | PubMed |
description | BACKGROUND: We aimed to evaluate the optimal management for elderly patients with nasopharyngeal carcinoma (NPC) with intensity-modulated radiotherapy (IMRT). METHODS: A total of 283 elderly patients with NPC diagnosed from 2015 to 2019 were enrolled in the study. Overall survival (OS) was the primary endpoint. Univariate and multivariate Cox regression analyses were preformed to identify potential prognostic factors. The recursive partitioning analysis (RPA) was used for risk stratification. Kaplan-Meier survival curves were applied to evaluate the survival endpoints, and log-rank test was utilized to assess differences between groups. The prognostic index (PI) was constructed to further predict patients’ prognosis displayed by nomogram model. The area under the receiver operating characteristic (ROC) curves (AUC) and the calibration curves were applied to assess the effectiveness of the model. RESULTS: Based on RPA-based risk stratification, we demonstrated that elderly NPC patients who were treated with IC followed by RT had similar OS as those with induction chemotherapy (IC) combined with concurrent chemoradiotherapy (CCRT) in the middle- (stage I-III and pre-treatment EBV > 1840 copies/ml) and high-risk groups (stage IVA). IMRT alone may be the optimal treatment option for the low-risk group (stage I-III with pre-treatment EBV ≤ 1840 copies/ml). We established an integrated PI which was indicted with stronger prognostic power than each of the factors alone for elderly NPC patients (The AUC of PI was 0.75, 0.80, and 0.82 for 1-, 3-, 5-year prediction of OS, respectively). CONCLUSION: We present a robust model for clinical stratification which could guide individual therapy for elderly NPC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-023-02272-x. |
format | Online Article Text |
id | pubmed-10290415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102904152023-06-25 Prognostic model on overall survival in elderly nasopharyngeal carcinoma patients: a recursive partitioning analysis identifying pre-treatment risk stratification Li, Ying Weng, Youliang Huang, Zongwei Pan, Yuhui Cai, Sunqin Ding, Qin Wu, Zijie Chen, Xin Lu, Jun Hu, Dan Qiu, Sufang Radiat Oncol Research BACKGROUND: We aimed to evaluate the optimal management for elderly patients with nasopharyngeal carcinoma (NPC) with intensity-modulated radiotherapy (IMRT). METHODS: A total of 283 elderly patients with NPC diagnosed from 2015 to 2019 were enrolled in the study. Overall survival (OS) was the primary endpoint. Univariate and multivariate Cox regression analyses were preformed to identify potential prognostic factors. The recursive partitioning analysis (RPA) was used for risk stratification. Kaplan-Meier survival curves were applied to evaluate the survival endpoints, and log-rank test was utilized to assess differences between groups. The prognostic index (PI) was constructed to further predict patients’ prognosis displayed by nomogram model. The area under the receiver operating characteristic (ROC) curves (AUC) and the calibration curves were applied to assess the effectiveness of the model. RESULTS: Based on RPA-based risk stratification, we demonstrated that elderly NPC patients who were treated with IC followed by RT had similar OS as those with induction chemotherapy (IC) combined with concurrent chemoradiotherapy (CCRT) in the middle- (stage I-III and pre-treatment EBV > 1840 copies/ml) and high-risk groups (stage IVA). IMRT alone may be the optimal treatment option for the low-risk group (stage I-III with pre-treatment EBV ≤ 1840 copies/ml). We established an integrated PI which was indicted with stronger prognostic power than each of the factors alone for elderly NPC patients (The AUC of PI was 0.75, 0.80, and 0.82 for 1-, 3-, 5-year prediction of OS, respectively). CONCLUSION: We present a robust model for clinical stratification which could guide individual therapy for elderly NPC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-023-02272-x. BioMed Central 2023-06-23 /pmc/articles/PMC10290415/ /pubmed/37353800 http://dx.doi.org/10.1186/s13014-023-02272-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Li, Ying Weng, Youliang Huang, Zongwei Pan, Yuhui Cai, Sunqin Ding, Qin Wu, Zijie Chen, Xin Lu, Jun Hu, Dan Qiu, Sufang Prognostic model on overall survival in elderly nasopharyngeal carcinoma patients: a recursive partitioning analysis identifying pre-treatment risk stratification |
title | Prognostic model on overall survival in elderly nasopharyngeal carcinoma patients: a recursive partitioning analysis identifying pre-treatment risk stratification |
title_full | Prognostic model on overall survival in elderly nasopharyngeal carcinoma patients: a recursive partitioning analysis identifying pre-treatment risk stratification |
title_fullStr | Prognostic model on overall survival in elderly nasopharyngeal carcinoma patients: a recursive partitioning analysis identifying pre-treatment risk stratification |
title_full_unstemmed | Prognostic model on overall survival in elderly nasopharyngeal carcinoma patients: a recursive partitioning analysis identifying pre-treatment risk stratification |
title_short | Prognostic model on overall survival in elderly nasopharyngeal carcinoma patients: a recursive partitioning analysis identifying pre-treatment risk stratification |
title_sort | prognostic model on overall survival in elderly nasopharyngeal carcinoma patients: a recursive partitioning analysis identifying pre-treatment risk stratification |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10290415/ https://www.ncbi.nlm.nih.gov/pubmed/37353800 http://dx.doi.org/10.1186/s13014-023-02272-x |
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