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Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma

To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced mag...

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Autores principales: Li, Wen-zhu, Wu, Gang, Li, Tian-sheng, Dai, Gan-mian, Liao, Yu-ting, Yang, Qian-yu, Chen, Feng, Huang, Wei-yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9627984/
https://www.ncbi.nlm.nih.gov/pubmed/36338711
http://dx.doi.org/10.3389/fonc.2022.955866
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author Li, Wen-zhu
Wu, Gang
Li, Tian-sheng
Dai, Gan-mian
Liao, Yu-ting
Yang, Qian-yu
Chen, Feng
Huang, Wei-yuan
author_facet Li, Wen-zhu
Wu, Gang
Li, Tian-sheng
Dai, Gan-mian
Liao, Yu-ting
Yang, Qian-yu
Chen, Feng
Huang, Wei-yuan
author_sort Li, Wen-zhu
collection PubMed
description To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (K(trans) ) and extracellular extravascular volume (V(e) ) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan–Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from K(trans) , Ve, and K(trans) +V(e) images, respectively). The combined Radscore (k) (trans) (+Ve) (C-index, 0.703, 95% confidence interval [CI]: 0.571–0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscore (k) (trans) (C-index, 0.693; 95% CI, 0.560–0.826) and Radscore (Ve) (C-index, 0.614; 95% CI, 0.481–0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscore (k) (trans) (+Ve) and risk factors (C-index, 0.732; 95% CI: 0.599–0.864) was better than Radscore (k) (trans) (+Ve) in predicting PFS in patients with advanced NPC. A lower Radscore (k) (trans) (+Ve) (HR 3.5584, 95% CI 2.1341–5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262–4.854), lower T stage (HR 1.4365, 95% CI 0.6745–3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899–1.267) were associated with longer PFS. Kaplan–Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC.
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spelling pubmed-96279842022-11-03 Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma Li, Wen-zhu Wu, Gang Li, Tian-sheng Dai, Gan-mian Liao, Yu-ting Yang, Qian-yu Chen, Feng Huang, Wei-yuan Front Oncol Oncology To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (K(trans) ) and extracellular extravascular volume (V(e) ) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan–Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from K(trans) , Ve, and K(trans) +V(e) images, respectively). The combined Radscore (k) (trans) (+Ve) (C-index, 0.703, 95% confidence interval [CI]: 0.571–0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscore (k) (trans) (C-index, 0.693; 95% CI, 0.560–0.826) and Radscore (Ve) (C-index, 0.614; 95% CI, 0.481–0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscore (k) (trans) (+Ve) and risk factors (C-index, 0.732; 95% CI: 0.599–0.864) was better than Radscore (k) (trans) (+Ve) in predicting PFS in patients with advanced NPC. A lower Radscore (k) (trans) (+Ve) (HR 3.5584, 95% CI 2.1341–5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262–4.854), lower T stage (HR 1.4365, 95% CI 0.6745–3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899–1.267) were associated with longer PFS. Kaplan–Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC. Frontiers Media S.A. 2022-10-13 /pmc/articles/PMC9627984/ /pubmed/36338711 http://dx.doi.org/10.3389/fonc.2022.955866 Text en Copyright © 2022 Li, Wu, Li, Dai, Liao, Yang, Chen and Huang 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
Li, Wen-zhu
Wu, Gang
Li, Tian-sheng
Dai, Gan-mian
Liao, Yu-ting
Yang, Qian-yu
Chen, Feng
Huang, Wei-yuan
Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma
title Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma
title_full Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma
title_fullStr Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma
title_full_unstemmed Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma
title_short Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma
title_sort dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9627984/
https://www.ncbi.nlm.nih.gov/pubmed/36338711
http://dx.doi.org/10.3389/fonc.2022.955866
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