Cargando…

A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population

BACKGROUND: The aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables (TNM staging, etc.) to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC)....

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Lijie, Liu, Ailing, Wang, Zhiheng, Xu, Ning, Zhou, Dandan, Qu, Tao, Liu, Guiyuan, Wang, Jingtao, Yang, Fujun, Guo, Xiaolei, Chi, Weiwei, Xue, Fuzhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237399/
https://www.ncbi.nlm.nih.gov/pubmed/35774128
http://dx.doi.org/10.3389/fonc.2022.816766
_version_ 1784736777263644672
author Wang, Lijie
Liu, Ailing
Wang, Zhiheng
Xu, Ning
Zhou, Dandan
Qu, Tao
Liu, Guiyuan
Wang, Jingtao
Yang, Fujun
Guo, Xiaolei
Chi, Weiwei
Xue, Fuzhong
author_facet Wang, Lijie
Liu, Ailing
Wang, Zhiheng
Xu, Ning
Zhou, Dandan
Qu, Tao
Liu, Guiyuan
Wang, Jingtao
Yang, Fujun
Guo, Xiaolei
Chi, Weiwei
Xue, Fuzhong
author_sort Wang, Lijie
collection PubMed
description BACKGROUND: The aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables (TNM staging, etc.) to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC). METHODS: A total of 1,480 patients with clinical data and pretreatment CT images during January 2013 and May 2018 were enrolled in this study. We randomly assigned the patients into training (N = 1036) and validation cohorts (N = 444). We extracted 1,288 quantitative features from the CT images of each patient. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was applied in feature selection and radiomics signature building. The radiomics nomogram used for the prognosis prediction was built by combining the radiomics signature and clinical variables that were derived from clinical data. Calibration ability and discrimination ability were analyzed in both training and validation cohorts. RESULTS: Eleven radiomics features were selected by LASSO Cox regression derived from CT images, and the radiomics signature was built in the training cohort. The radiomics signature was significantly associated with NSCLC patients’ OS (HR = 3.913, p < 0.01). The radiomics nomogram combining the radiomics signature with six clinical variables (age, sex, chronic obstructive pulmonary disease, T stage, N stage, and M stage) had a better prognostic performance than the clinical nomogram both in the training cohort (C-index, 0.861, 95% CI: 0.843–0.879 vs. C-index, 0.851, 95% CI: 0.832–0.870; p < 0.001) and in the validation cohort (C-index, 0.868, 95% CI: 0.841–0.896 vs. C-index, 0.854, 95% CI: 0.824–0.884; p = 0.002). The calibration curves demonstrated optimal alignment between the prediction and actual observation. CONCLUSION: The established radiomics nomogram could act as a noninvasive prediction tool for individualized survival prognosis estimation in patients with NSCLC. The radiomics signature derived from CT images may help clinicians in decision-making and hold promise to be adopted in the patient care setting as well as the clinical trial setting.
format Online
Article
Text
id pubmed-9237399
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-92373992022-06-29 A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population Wang, Lijie Liu, Ailing Wang, Zhiheng Xu, Ning Zhou, Dandan Qu, Tao Liu, Guiyuan Wang, Jingtao Yang, Fujun Guo, Xiaolei Chi, Weiwei Xue, Fuzhong Front Oncol Oncology BACKGROUND: The aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables (TNM staging, etc.) to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC). METHODS: A total of 1,480 patients with clinical data and pretreatment CT images during January 2013 and May 2018 were enrolled in this study. We randomly assigned the patients into training (N = 1036) and validation cohorts (N = 444). We extracted 1,288 quantitative features from the CT images of each patient. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was applied in feature selection and radiomics signature building. The radiomics nomogram used for the prognosis prediction was built by combining the radiomics signature and clinical variables that were derived from clinical data. Calibration ability and discrimination ability were analyzed in both training and validation cohorts. RESULTS: Eleven radiomics features were selected by LASSO Cox regression derived from CT images, and the radiomics signature was built in the training cohort. The radiomics signature was significantly associated with NSCLC patients’ OS (HR = 3.913, p < 0.01). The radiomics nomogram combining the radiomics signature with six clinical variables (age, sex, chronic obstructive pulmonary disease, T stage, N stage, and M stage) had a better prognostic performance than the clinical nomogram both in the training cohort (C-index, 0.861, 95% CI: 0.843–0.879 vs. C-index, 0.851, 95% CI: 0.832–0.870; p < 0.001) and in the validation cohort (C-index, 0.868, 95% CI: 0.841–0.896 vs. C-index, 0.854, 95% CI: 0.824–0.884; p = 0.002). The calibration curves demonstrated optimal alignment between the prediction and actual observation. CONCLUSION: The established radiomics nomogram could act as a noninvasive prediction tool for individualized survival prognosis estimation in patients with NSCLC. The radiomics signature derived from CT images may help clinicians in decision-making and hold promise to be adopted in the patient care setting as well as the clinical trial setting. Frontiers Media S.A. 2022-06-14 /pmc/articles/PMC9237399/ /pubmed/35774128 http://dx.doi.org/10.3389/fonc.2022.816766 Text en Copyright © 2022 Wang, Liu, Wang, Xu, Zhou, Qu, Liu, Wang, Yang, Guo, Chi and Xue 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 Oncology
Wang, Lijie
Liu, Ailing
Wang, Zhiheng
Xu, Ning
Zhou, Dandan
Qu, Tao
Liu, Guiyuan
Wang, Jingtao
Yang, Fujun
Guo, Xiaolei
Chi, Weiwei
Xue, Fuzhong
A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population
title A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population
title_full A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population
title_fullStr A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population
title_full_unstemmed A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population
title_short A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population
title_sort prognostic model of non-small cell lung cancer with a radiomics nomogram in an eastern chinese population
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237399/
https://www.ncbi.nlm.nih.gov/pubmed/35774128
http://dx.doi.org/10.3389/fonc.2022.816766
work_keys_str_mv AT wanglijie aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT liuailing aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT wangzhiheng aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT xuning aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT zhoudandan aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT qutao aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT liuguiyuan aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT wangjingtao aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT yangfujun aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT guoxiaolei aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT chiweiwei aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT xuefuzhong aprognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT wanglijie prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT liuailing prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT wangzhiheng prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT xuning prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT zhoudandan prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT qutao prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT liuguiyuan prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT wangjingtao prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT yangfujun prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT guoxiaolei prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT chiweiwei prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation
AT xuefuzhong prognosticmodelofnonsmallcelllungcancerwitharadiomicsnomograminaneasternchinesepopulation