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A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study
BACKGROUND: The number of elderly patients with early renal cell carcinoma (RCC) is on the rise. However, there is still a lack of accurate prediction models for the prognosis of early RCC in elderly patients. It is necessary to establish a new nomogram to predict the prognosis of elderly patients w...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8845298/ https://www.ncbi.nlm.nih.gov/pubmed/35164796 http://dx.doi.org/10.1186/s12967-022-03287-w |
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author | Wang, Jinkui Tang, Jie Chen, Tiaoyao Yue, Song Fu, Wanting Xie, Zulong Liu, Xiaozhu |
author_facet | Wang, Jinkui Tang, Jie Chen, Tiaoyao Yue, Song Fu, Wanting Xie, Zulong Liu, Xiaozhu |
author_sort | Wang, Jinkui |
collection | PubMed |
description | BACKGROUND: The number of elderly patients with early renal cell carcinoma (RCC) is on the rise. However, there is still a lack of accurate prediction models for the prognosis of early RCC in elderly patients. It is necessary to establish a new nomogram to predict the prognosis of elderly patients with early RCC. METHODS: The data of patients aged above 65 years old with TNM stage I and II RCC were downloaded from the SEER database between 2010 and 2018. The patients from 2010 to 2017 were randomly assigned to the training cohort (n = 7233) and validation cohort (n = 3024). Patient data in 2018(n = 1360) was used for external validation. We used univariable and multivariable Cox regression model to evaluate independent prognostic factors and constructed a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) rates of patients with early-stage RCC. Multiple parameters were used to validate the nomogram, including the consistency index (C-index), the calibration plots, the area under the receiver operator characteristics (ROC) curve, and the decision curve analysis (DCA). RESULTS: The study included a total of 11,617 elderly patients with early RCC. univariable and multivariable Cox regression analysis based on predictive variables such as age, sex, histologic type, Fuhrman grade, T stage, surgery type, tumors number, tumor size, and marriage were included to establish a nomogram. The C-index of the training cohort and validation cohort were 0.748 (95% CI: 0.760–0.736) and 0.744 (95% CI: 0.762–0.726), respectively. In the external validation cohort, C-index was 0.893 (95% CI: 0.928–0.858). The calibration plots basically coincides with the diagonal, indicating that the observed OS was almost equal to the predicted OS. It was shown in DCA that the nomogram has more important clinical significance than the traditional TNM stage. CONCLUSION: A novel nomogram was developed to assess the prognosis of an elderly patient with early RCC and to predict prognosis and formulate treatment and follow-up strategies. |
format | Online Article Text |
id | pubmed-8845298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88452982022-02-16 A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study Wang, Jinkui Tang, Jie Chen, Tiaoyao Yue, Song Fu, Wanting Xie, Zulong Liu, Xiaozhu J Transl Med Research BACKGROUND: The number of elderly patients with early renal cell carcinoma (RCC) is on the rise. However, there is still a lack of accurate prediction models for the prognosis of early RCC in elderly patients. It is necessary to establish a new nomogram to predict the prognosis of elderly patients with early RCC. METHODS: The data of patients aged above 65 years old with TNM stage I and II RCC were downloaded from the SEER database between 2010 and 2018. The patients from 2010 to 2017 were randomly assigned to the training cohort (n = 7233) and validation cohort (n = 3024). Patient data in 2018(n = 1360) was used for external validation. We used univariable and multivariable Cox regression model to evaluate independent prognostic factors and constructed a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) rates of patients with early-stage RCC. Multiple parameters were used to validate the nomogram, including the consistency index (C-index), the calibration plots, the area under the receiver operator characteristics (ROC) curve, and the decision curve analysis (DCA). RESULTS: The study included a total of 11,617 elderly patients with early RCC. univariable and multivariable Cox regression analysis based on predictive variables such as age, sex, histologic type, Fuhrman grade, T stage, surgery type, tumors number, tumor size, and marriage were included to establish a nomogram. The C-index of the training cohort and validation cohort were 0.748 (95% CI: 0.760–0.736) and 0.744 (95% CI: 0.762–0.726), respectively. In the external validation cohort, C-index was 0.893 (95% CI: 0.928–0.858). The calibration plots basically coincides with the diagonal, indicating that the observed OS was almost equal to the predicted OS. It was shown in DCA that the nomogram has more important clinical significance than the traditional TNM stage. CONCLUSION: A novel nomogram was developed to assess the prognosis of an elderly patient with early RCC and to predict prognosis and formulate treatment and follow-up strategies. BioMed Central 2022-02-14 /pmc/articles/PMC8845298/ /pubmed/35164796 http://dx.doi.org/10.1186/s12967-022-03287-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Wang, Jinkui Tang, Jie Chen, Tiaoyao Yue, Song Fu, Wanting Xie, Zulong Liu, Xiaozhu A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study |
title | A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study |
title_full | A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study |
title_fullStr | A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study |
title_full_unstemmed | A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study |
title_short | A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study |
title_sort | web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8845298/ https://www.ncbi.nlm.nih.gov/pubmed/35164796 http://dx.doi.org/10.1186/s12967-022-03287-w |
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