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A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study
OBJECTIVE: Thyroid cancer (TC) is a common malignancy with a poor prognosis with aging. However, no accurate predictive survival model exists for patients with geriatric TC.We aimed to establish prediction models of prognosis in elderly TC. METHODS: We retrospectively reviewed the clinicopathology c...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780441/ https://www.ncbi.nlm.nih.gov/pubmed/36568078 http://dx.doi.org/10.3389/fendo.2022.1038041 |
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author | Zhang, Ting-ting Zeng, Jing Yang, Yan Wang, Jin-jing Kang, Yao-jie Zhang, Dong-he Liu, Xiao-zhu Chen, Kang Wang, Xuan Fang, Yi |
author_facet | Zhang, Ting-ting Zeng, Jing Yang, Yan Wang, Jin-jing Kang, Yao-jie Zhang, Dong-he Liu, Xiao-zhu Chen, Kang Wang, Xuan Fang, Yi |
author_sort | Zhang, Ting-ting |
collection | PubMed |
description | OBJECTIVE: Thyroid cancer (TC) is a common malignancy with a poor prognosis with aging. However, no accurate predictive survival model exists for patients with geriatric TC.We aimed to establish prediction models of prognosis in elderly TC. METHODS: We retrospectively reviewed the clinicopathology characteristics of patients with geriatric TC in the Surveillance, Epidemiology, and End Results database (SEER) from 2004 to 2018. The risk predictors used to build the nomograms were derived from the Cox proportional risk regression. These nomograms were used to predict 1-, 3-, and 5-year overall survival and cancer-specific survival in elderly patients with TC. The accuracy and discriminability of the new model were evaluated by the consistency index (C-index) and calibration curve. The clinical applicability value of the model was assessed using the decision curve analysis. RESULTS: We used the SEER database to include 16475 patients with geriatric TC diagnosed from 2004 to 2018. The patients from 2004 to 2015 were randomly sorted out on a scale of 7:3. They were classified into a training group (n = 8623) and a validation group (n = 3669). Patients with TC diagnosed in 2016–2018 were classified into external validation groups (n = 4183). The overall survival nomogram consisted of 10 variables (age, gender, marital status, histologic type, grade, TNM stage, surgery status, and tumor size). A cancer-specific survival nomogram consisted of eight factors (age, tumor size, grade, histologic type, surgery, and TNM stage). The C-index values for the training, validation, and external validation groups were 0.775 (95% confidence interval [CI] 0.785–0.765), 0.776 (95% CI 0.792–0.760), and 0.895(95% CI 0.873–0.917), respectively. The overall survival was consistent with a nomogram based on the calibration curve. Besides, the decision curve analysis showed excellent clinical application value of the nomogram. Additionally, we found that surgery could improve the prognosis of patients with geriatric at high-risk (P < 0.001) but not those at low-risk (P = 0.069). CONCLUSION: This was the first study to construct predictive survival nomograms for patients with geriatric TC. The well-established nomograms and the actual results could guide follow-up management strategies. |
format | Online Article Text |
id | pubmed-9780441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97804412022-12-24 A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study Zhang, Ting-ting Zeng, Jing Yang, Yan Wang, Jin-jing Kang, Yao-jie Zhang, Dong-he Liu, Xiao-zhu Chen, Kang Wang, Xuan Fang, Yi Front Endocrinol (Lausanne) Endocrinology OBJECTIVE: Thyroid cancer (TC) is a common malignancy with a poor prognosis with aging. However, no accurate predictive survival model exists for patients with geriatric TC.We aimed to establish prediction models of prognosis in elderly TC. METHODS: We retrospectively reviewed the clinicopathology characteristics of patients with geriatric TC in the Surveillance, Epidemiology, and End Results database (SEER) from 2004 to 2018. The risk predictors used to build the nomograms were derived from the Cox proportional risk regression. These nomograms were used to predict 1-, 3-, and 5-year overall survival and cancer-specific survival in elderly patients with TC. The accuracy and discriminability of the new model were evaluated by the consistency index (C-index) and calibration curve. The clinical applicability value of the model was assessed using the decision curve analysis. RESULTS: We used the SEER database to include 16475 patients with geriatric TC diagnosed from 2004 to 2018. The patients from 2004 to 2015 were randomly sorted out on a scale of 7:3. They were classified into a training group (n = 8623) and a validation group (n = 3669). Patients with TC diagnosed in 2016–2018 were classified into external validation groups (n = 4183). The overall survival nomogram consisted of 10 variables (age, gender, marital status, histologic type, grade, TNM stage, surgery status, and tumor size). A cancer-specific survival nomogram consisted of eight factors (age, tumor size, grade, histologic type, surgery, and TNM stage). The C-index values for the training, validation, and external validation groups were 0.775 (95% confidence interval [CI] 0.785–0.765), 0.776 (95% CI 0.792–0.760), and 0.895(95% CI 0.873–0.917), respectively. The overall survival was consistent with a nomogram based on the calibration curve. Besides, the decision curve analysis showed excellent clinical application value of the nomogram. Additionally, we found that surgery could improve the prognosis of patients with geriatric at high-risk (P < 0.001) but not those at low-risk (P = 0.069). CONCLUSION: This was the first study to construct predictive survival nomograms for patients with geriatric TC. The well-established nomograms and the actual results could guide follow-up management strategies. Frontiers Media S.A. 2022-12-09 /pmc/articles/PMC9780441/ /pubmed/36568078 http://dx.doi.org/10.3389/fendo.2022.1038041 Text en Copyright © 2022 Zhang, Zeng, Yang, Wang, Kang, Zhang, Liu, Chen, Wang and Fang 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 | Endocrinology Zhang, Ting-ting Zeng, Jing Yang, Yan Wang, Jin-jing Kang, Yao-jie Zhang, Dong-he Liu, Xiao-zhu Chen, Kang Wang, Xuan Fang, Yi A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study |
title | A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study |
title_full | A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study |
title_fullStr | A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study |
title_full_unstemmed | A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study |
title_short | A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study |
title_sort | visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: a population-based study |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780441/ https://www.ncbi.nlm.nih.gov/pubmed/36568078 http://dx.doi.org/10.3389/fendo.2022.1038041 |
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