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Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years

This study aimed to establish a nomogram for the prognostic prediction of patients with early-onset lung cancer (EOLC) in both overall survival (OS) and cancer-specific survival (CSS). EOLC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results (...

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Autores principales: Dai, Lili, Wang, Wei, Liu, Qi, Xia, Tongjia, Wang, Qikui, Chen, Qingqing, Zhu, Ning, Cheng, Yu, Yan, Ying, Shu, Jun, Qu, Kaixin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8112561/
https://www.ncbi.nlm.nih.gov/pubmed/33091332
http://dx.doi.org/10.17305/bjbms.2020.5079
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author Dai, Lili
Wang, Wei
Liu, Qi
Xia, Tongjia
Wang, Qikui
Chen, Qingqing
Zhu, Ning
Cheng, Yu
Yan, Ying
Shu, Jun
Qu, Kaixin
author_facet Dai, Lili
Wang, Wei
Liu, Qi
Xia, Tongjia
Wang, Qikui
Chen, Qingqing
Zhu, Ning
Cheng, Yu
Yan, Ying
Shu, Jun
Qu, Kaixin
author_sort Dai, Lili
collection PubMed
description This study aimed to establish a nomogram for the prognostic prediction of patients with early-onset lung cancer (EOLC) in both overall survival (OS) and cancer-specific survival (CSS). EOLC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and further divided into training and validation sets randomly. The prognostic nomobgram for predicting 3-, 5- and 10-years OS and CSS was established based on the relative clinical variables determined by the multivariate Cox analysis results. Furthermore, the predictive performance of nomogram was assessed by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) curve. A total of 1,822 EOLC patients were selected and randomized into a training cohort (1,275, 70%) and a validation cohort (547, 30%). The nomograms were established based on the statistical results of Cox analysis. In training set, the C-indexes for OS and CSS prediction were 0.797 (95% confidence interval [CI]: 0.773-0.818) and 0.794 (95% CI: 0.771-0.816). Significant agreement in the calibration curves was noticed in the nomogram models. The results of ROC and DCA indicated nomograms possessed better predict performance compared with TNM-stage and SEER-stage. And the area under the curve (AUC) of the nomogram for OS and CSS prediction in ROC analysis were 0.766 (95% CI: 0.745-0.787) and 0.782 (95% CI: 0.760-0.804) respectively. The prognostic nomogram provided an accurate prediction of 3-, 5-, and 10-year OS and CSS of EOLC patients which contributed clinicians to optimize individualized treatment plans.
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spelling pubmed-81125612021-06-03 Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years Dai, Lili Wang, Wei Liu, Qi Xia, Tongjia Wang, Qikui Chen, Qingqing Zhu, Ning Cheng, Yu Yan, Ying Shu, Jun Qu, Kaixin Bosn J Basic Med Sci Research Article This study aimed to establish a nomogram for the prognostic prediction of patients with early-onset lung cancer (EOLC) in both overall survival (OS) and cancer-specific survival (CSS). EOLC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and further divided into training and validation sets randomly. The prognostic nomobgram for predicting 3-, 5- and 10-years OS and CSS was established based on the relative clinical variables determined by the multivariate Cox analysis results. Furthermore, the predictive performance of nomogram was assessed by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) curve. A total of 1,822 EOLC patients were selected and randomized into a training cohort (1,275, 70%) and a validation cohort (547, 30%). The nomograms were established based on the statistical results of Cox analysis. In training set, the C-indexes for OS and CSS prediction were 0.797 (95% confidence interval [CI]: 0.773-0.818) and 0.794 (95% CI: 0.771-0.816). Significant agreement in the calibration curves was noticed in the nomogram models. The results of ROC and DCA indicated nomograms possessed better predict performance compared with TNM-stage and SEER-stage. And the area under the curve (AUC) of the nomogram for OS and CSS prediction in ROC analysis were 0.766 (95% CI: 0.745-0.787) and 0.782 (95% CI: 0.760-0.804) respectively. The prognostic nomogram provided an accurate prediction of 3-, 5-, and 10-year OS and CSS of EOLC patients which contributed clinicians to optimize individualized treatment plans. Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina 2021-06 /pmc/articles/PMC8112561/ /pubmed/33091332 http://dx.doi.org/10.17305/bjbms.2020.5079 Text en Copyright: © The Author(s) (2021) https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License
spellingShingle Research Article
Dai, Lili
Wang, Wei
Liu, Qi
Xia, Tongjia
Wang, Qikui
Chen, Qingqing
Zhu, Ning
Cheng, Yu
Yan, Ying
Shu, Jun
Qu, Kaixin
Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years
title Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years
title_full Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years
title_fullStr Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years
title_full_unstemmed Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years
title_short Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years
title_sort development and validation of prognostic nomogram for lung cancer patients below the age of 45 years
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8112561/
https://www.ncbi.nlm.nih.gov/pubmed/33091332
http://dx.doi.org/10.17305/bjbms.2020.5079
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