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Treatment outcome and prognostic analysis of advanced large cell neuroendocrine carcinoma of the lung
The optimal systemic treatment of advanced large cell neuroendocrine carcinoma (LCNEC) is still controversial. We intend to explore advanced LCNEC through SEER database, construct nomogram model of advanced LCNEC, and understand the effect of different treatment regimens on LCNEC. We collected 909 p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532410/ https://www.ncbi.nlm.nih.gov/pubmed/36195623 http://dx.doi.org/10.1038/s41598-022-18421-3 |
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author | Xia, Lu Wang, Lile Zhou, Zihan Han, Shuhua |
author_facet | Xia, Lu Wang, Lile Zhou, Zihan Han, Shuhua |
author_sort | Xia, Lu |
collection | PubMed |
description | The optimal systemic treatment of advanced large cell neuroendocrine carcinoma (LCNEC) is still controversial. We intend to explore advanced LCNEC through SEER database, construct nomogram model of advanced LCNEC, and understand the effect of different treatment regimens on LCNEC. We collected 909 patients, divided them into a training set validation set, constructed nomograms using Cox proportional hazards regression models, and evaluated nomogram discrimination and calibration by C-index and calibration curves. Kaplan–Meier will also be used to compare OS in different groups of patients and to explore the impact of different treatment regimens on advanced LCNEC. On the nomogram plotted, the nomogram predicted AUC values over time were always greater than 0.7, the C-index was 0.681 (95% CI 0.656–0.706) and 0.663 (95% CI 0.628–0.698) in the training and validation sets, respectively, and patients were divided into two groups according to risk, and a significant difference in OS was observed between the high-risk and low-risk groups in the training and validation cohorts. Different treatment analyses showed that chemotherapy is still the best treatment for advanced LCNEC. This nomogram provides a convenient and reliable tool for individual assessment and clinical decision-making of patients with advanced LCNEC. |
format | Online Article Text |
id | pubmed-9532410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95324102022-10-06 Treatment outcome and prognostic analysis of advanced large cell neuroendocrine carcinoma of the lung Xia, Lu Wang, Lile Zhou, Zihan Han, Shuhua Sci Rep Article The optimal systemic treatment of advanced large cell neuroendocrine carcinoma (LCNEC) is still controversial. We intend to explore advanced LCNEC through SEER database, construct nomogram model of advanced LCNEC, and understand the effect of different treatment regimens on LCNEC. We collected 909 patients, divided them into a training set validation set, constructed nomograms using Cox proportional hazards regression models, and evaluated nomogram discrimination and calibration by C-index and calibration curves. Kaplan–Meier will also be used to compare OS in different groups of patients and to explore the impact of different treatment regimens on advanced LCNEC. On the nomogram plotted, the nomogram predicted AUC values over time were always greater than 0.7, the C-index was 0.681 (95% CI 0.656–0.706) and 0.663 (95% CI 0.628–0.698) in the training and validation sets, respectively, and patients were divided into two groups according to risk, and a significant difference in OS was observed between the high-risk and low-risk groups in the training and validation cohorts. Different treatment analyses showed that chemotherapy is still the best treatment for advanced LCNEC. This nomogram provides a convenient and reliable tool for individual assessment and clinical decision-making of patients with advanced LCNEC. Nature Publishing Group UK 2022-10-04 /pmc/articles/PMC9532410/ /pubmed/36195623 http://dx.doi.org/10.1038/s41598-022-18421-3 Text en © The Author(s) 2022 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/) . |
spellingShingle | Article Xia, Lu Wang, Lile Zhou, Zihan Han, Shuhua Treatment outcome and prognostic analysis of advanced large cell neuroendocrine carcinoma of the lung |
title | Treatment outcome and prognostic analysis of advanced large cell neuroendocrine carcinoma of the lung |
title_full | Treatment outcome and prognostic analysis of advanced large cell neuroendocrine carcinoma of the lung |
title_fullStr | Treatment outcome and prognostic analysis of advanced large cell neuroendocrine carcinoma of the lung |
title_full_unstemmed | Treatment outcome and prognostic analysis of advanced large cell neuroendocrine carcinoma of the lung |
title_short | Treatment outcome and prognostic analysis of advanced large cell neuroendocrine carcinoma of the lung |
title_sort | treatment outcome and prognostic analysis of advanced large cell neuroendocrine carcinoma of the lung |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532410/ https://www.ncbi.nlm.nih.gov/pubmed/36195623 http://dx.doi.org/10.1038/s41598-022-18421-3 |
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