Cargando…

A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases

INTRODUCTION: The current American Joint Committee on Cancer (AJCC) M1a staging of non‐small cell lung cancer (NSCLC) encompasses a wide disease spectrum, showing diverse prognosis. METHODS: Patients who diagnosed in an earlier period formed the training cohort, and those who diagnosed thereafter fo...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Hongchao, Huang, Chen, Ge, Huiqing, Chen, Qianshun, Chen, Jing, Li, Yuqiang, Chen, Haiyong, Luo, Shiyin, Zhao, Lilan, Xu, Xunyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921928/
https://www.ncbi.nlm.nih.gov/pubmed/35128839
http://dx.doi.org/10.1002/cam4.4560
_version_ 1784669419621842944
author Chen, Hongchao
Huang, Chen
Ge, Huiqing
Chen, Qianshun
Chen, Jing
Li, Yuqiang
Chen, Haiyong
Luo, Shiyin
Zhao, Lilan
Xu, Xunyu
author_facet Chen, Hongchao
Huang, Chen
Ge, Huiqing
Chen, Qianshun
Chen, Jing
Li, Yuqiang
Chen, Haiyong
Luo, Shiyin
Zhao, Lilan
Xu, Xunyu
author_sort Chen, Hongchao
collection PubMed
description INTRODUCTION: The current American Joint Committee on Cancer (AJCC) M1a staging of non‐small cell lung cancer (NSCLC) encompasses a wide disease spectrum, showing diverse prognosis. METHODS: Patients who diagnosed in an earlier period formed the training cohort, and those who diagnosed thereafter formed the validation cohort. Kaplan–Meier analysis was performed for the training cohort by dividing the M1a stage into three subgroups: (I) malignant pleural effusion (MPE) or malignant pericardial effusion (MPCE); (II) separate tumor nodules in contralateral lung (STCL); and (III) pleural tumor nodules on the ipsilateral lung (PTIL). Gender, age, histologic, N stage, grade, surgery for primary site, lymphadenectomy, M1a groups, and chemotherapy were selected as independent prognostic factors using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. And a nomogram was constructed using Cox hazard regression analysis. Accuracy and clinical practicability were separately tested by Harrell's concordance index, the receiver operating characteristic (ROC) curve, calibration plots, residual plot, the integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). RESULTS: The concordance index (0.661 for the training cohort and 0.688 for the validation cohort) and the area under the ROC curve (training cohort: 0.709 for 1‐year and 0.727 for 2‐year OS prediction; validation cohort: 0.737 for 1‐year and 0.734 for 2‐year OS prediction) indicated satisfactory discriminative ability of the nomogram. Calibration curve and DCA presented great prognostic accuracy, and clinical applicability. Its prognostic accuracy preceded the AJCC staging with evaluated NRI (1‐year: 0.327; 2‐year: 0.302) and IDI (1‐year: 0.138; 2‐year: 0.130). CONCLUSION: Our study established a nomogram for the prediction of 1‐ and 2‐year OS in patients with NSCLC diagnosed with stage M1a, facilitating healthcare workers to accurately evaluate the individual survival of M1a NSCLC patients. The accuracy and clinical applicability of this nomogram were validated.
format Online
Article
Text
id pubmed-8921928
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Blackwell Publishing Ltd
record_format MEDLINE/PubMed
spelling pubmed-89219282022-03-21 A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases Chen, Hongchao Huang, Chen Ge, Huiqing Chen, Qianshun Chen, Jing Li, Yuqiang Chen, Haiyong Luo, Shiyin Zhao, Lilan Xu, Xunyu Cancer Med Cancer Prevention INTRODUCTION: The current American Joint Committee on Cancer (AJCC) M1a staging of non‐small cell lung cancer (NSCLC) encompasses a wide disease spectrum, showing diverse prognosis. METHODS: Patients who diagnosed in an earlier period formed the training cohort, and those who diagnosed thereafter formed the validation cohort. Kaplan–Meier analysis was performed for the training cohort by dividing the M1a stage into three subgroups: (I) malignant pleural effusion (MPE) or malignant pericardial effusion (MPCE); (II) separate tumor nodules in contralateral lung (STCL); and (III) pleural tumor nodules on the ipsilateral lung (PTIL). Gender, age, histologic, N stage, grade, surgery for primary site, lymphadenectomy, M1a groups, and chemotherapy were selected as independent prognostic factors using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. And a nomogram was constructed using Cox hazard regression analysis. Accuracy and clinical practicability were separately tested by Harrell's concordance index, the receiver operating characteristic (ROC) curve, calibration plots, residual plot, the integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). RESULTS: The concordance index (0.661 for the training cohort and 0.688 for the validation cohort) and the area under the ROC curve (training cohort: 0.709 for 1‐year and 0.727 for 2‐year OS prediction; validation cohort: 0.737 for 1‐year and 0.734 for 2‐year OS prediction) indicated satisfactory discriminative ability of the nomogram. Calibration curve and DCA presented great prognostic accuracy, and clinical applicability. Its prognostic accuracy preceded the AJCC staging with evaluated NRI (1‐year: 0.327; 2‐year: 0.302) and IDI (1‐year: 0.138; 2‐year: 0.130). CONCLUSION: Our study established a nomogram for the prediction of 1‐ and 2‐year OS in patients with NSCLC diagnosed with stage M1a, facilitating healthcare workers to accurately evaluate the individual survival of M1a NSCLC patients. The accuracy and clinical applicability of this nomogram were validated. Blackwell Publishing Ltd 2022-02-06 /pmc/articles/PMC8921928/ /pubmed/35128839 http://dx.doi.org/10.1002/cam4.4560 Text en © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Prevention
Chen, Hongchao
Huang, Chen
Ge, Huiqing
Chen, Qianshun
Chen, Jing
Li, Yuqiang
Chen, Haiyong
Luo, Shiyin
Zhao, Lilan
Xu, Xunyu
A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases
title A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases
title_full A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases
title_fullStr A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases
title_full_unstemmed A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases
title_short A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases
title_sort novel lasso‐derived prognostic model predicting survival for non‐small cell lung cancer patients with m1a diseases
topic Cancer Prevention
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921928/
https://www.ncbi.nlm.nih.gov/pubmed/35128839
http://dx.doi.org/10.1002/cam4.4560
work_keys_str_mv AT chenhongchao anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT huangchen anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT gehuiqing anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT chenqianshun anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT chenjing anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT liyuqiang anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT chenhaiyong anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT luoshiyin anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT zhaolilan anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT xuxunyu anovellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT chenhongchao novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT huangchen novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT gehuiqing novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT chenqianshun novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT chenjing novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT liyuqiang novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT chenhaiyong novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT luoshiyin novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT zhaolilan novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases
AT xuxunyu novellassoderivedprognosticmodelpredictingsurvivalfornonsmallcelllungcancerpatientswithm1adiseases