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Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study

Backgrounds: Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rarely high-grade neuroendocrine carcinoma of the lung with features of both small cell and non-small cell lung cancer. In this study, we aim to construct a prognostic nomogram that integrates the clinical features and treatment...

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Autores principales: Mu, Xiaoli, Pu, Dan, Zhu, Yajuan, Zhou, Yixin, Wu, Qiang, Liu, Qing, Yin, Liyuan, Li, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299657/
https://www.ncbi.nlm.nih.gov/pubmed/37373819
http://dx.doi.org/10.3390/jcm12124126
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author Mu, Xiaoli
Pu, Dan
Zhu, Yajuan
Zhou, Yixin
Wu, Qiang
Liu, Qing
Yin, Liyuan
Li, Yan
author_facet Mu, Xiaoli
Pu, Dan
Zhu, Yajuan
Zhou, Yixin
Wu, Qiang
Liu, Qing
Yin, Liyuan
Li, Yan
author_sort Mu, Xiaoli
collection PubMed
description Backgrounds: Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rarely high-grade neuroendocrine carcinoma of the lung with features of both small cell and non-small cell lung cancer. In this study, we aim to construct a prognostic nomogram that integrates the clinical features and treatment options to predict disease-specific survival (DSS). Methods: A total of 713 patients diagnosed with LCNEC were from the US National Cancer Institute’s Surveillance Epidemiology and End Results (SEER) registry between 2010–2016. Cox proportional hazards analysis was conducted to choose the significant predictors of DSS. External validation was performed using 77 patients with LCNEC in the West China Hospital Sichuan University between 2010–2018. The predictive accuracy and discriminative capability were estimated by the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC) curve. The clinical applicability of the nomogram was verified through the decision curve analysis (DCA). Additionally, we conducted a subgroup analysis of data available in the external cohort that may impact prognosis but was not recorded in the SEER database. Results: Six independent risk factors for DSS were identified and integrated into the nomogram. The nomogram achieved good C- indexes of 0.803 and 0.767 in the training and validation group, respectively. Moreover, the calibration curves for the probability of survival showed good agreement between prediction by nomogram and actual observation in 1-, 3- and 5-year DSS. The ROC curves demonstrated the prediction accuracy of the established nomogram (all Area Under Curve (AUC) > 0.8). DCA exhibited the favorable clinical applicability of the nomogram in the prediction of LCNEC survival. A risk classification system was built which could perfectly classify LCNEC patients into high-, medium- and low-risk groups (p < 0.001). The survival analysis conducted on the West China Hospital cohort indicated that whole brain radiation therapy (WBRT), prophylactic cranial irradiation (PCI), surgical procedures, tumor grade, Ki-67, and PD-L1 expression were not significantly associated with DSS. Conclusion: This study has effectively developed a prognostic nomogram and a corresponding risk stratification system, which demonstrate promising potential for predicting the DSS of patients with LCNEC.
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spelling pubmed-102996572023-06-28 Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study Mu, Xiaoli Pu, Dan Zhu, Yajuan Zhou, Yixin Wu, Qiang Liu, Qing Yin, Liyuan Li, Yan J Clin Med Article Backgrounds: Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rarely high-grade neuroendocrine carcinoma of the lung with features of both small cell and non-small cell lung cancer. In this study, we aim to construct a prognostic nomogram that integrates the clinical features and treatment options to predict disease-specific survival (DSS). Methods: A total of 713 patients diagnosed with LCNEC were from the US National Cancer Institute’s Surveillance Epidemiology and End Results (SEER) registry between 2010–2016. Cox proportional hazards analysis was conducted to choose the significant predictors of DSS. External validation was performed using 77 patients with LCNEC in the West China Hospital Sichuan University between 2010–2018. The predictive accuracy and discriminative capability were estimated by the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC) curve. The clinical applicability of the nomogram was verified through the decision curve analysis (DCA). Additionally, we conducted a subgroup analysis of data available in the external cohort that may impact prognosis but was not recorded in the SEER database. Results: Six independent risk factors for DSS were identified and integrated into the nomogram. The nomogram achieved good C- indexes of 0.803 and 0.767 in the training and validation group, respectively. Moreover, the calibration curves for the probability of survival showed good agreement between prediction by nomogram and actual observation in 1-, 3- and 5-year DSS. The ROC curves demonstrated the prediction accuracy of the established nomogram (all Area Under Curve (AUC) > 0.8). DCA exhibited the favorable clinical applicability of the nomogram in the prediction of LCNEC survival. A risk classification system was built which could perfectly classify LCNEC patients into high-, medium- and low-risk groups (p < 0.001). The survival analysis conducted on the West China Hospital cohort indicated that whole brain radiation therapy (WBRT), prophylactic cranial irradiation (PCI), surgical procedures, tumor grade, Ki-67, and PD-L1 expression were not significantly associated with DSS. Conclusion: This study has effectively developed a prognostic nomogram and a corresponding risk stratification system, which demonstrate promising potential for predicting the DSS of patients with LCNEC. MDPI 2023-06-19 /pmc/articles/PMC10299657/ /pubmed/37373819 http://dx.doi.org/10.3390/jcm12124126 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mu, Xiaoli
Pu, Dan
Zhu, Yajuan
Zhou, Yixin
Wu, Qiang
Liu, Qing
Yin, Liyuan
Li, Yan
Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study
title Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study
title_full Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study
title_fullStr Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study
title_full_unstemmed Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study
title_short Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study
title_sort lung large cell neuroendocrine carcinoma: a population-based retrospective cohort study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299657/
https://www.ncbi.nlm.nih.gov/pubmed/37373819
http://dx.doi.org/10.3390/jcm12124126
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