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Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients wit...

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Autores principales: Li, Hailin, Huang, Weiyuan, Wang, Siwen, Balasubramanian, Priya S., Wu, Gang, Fang, Mengjie, Xie, Xuebin, Zhang, Jie, Dong, Di, Tian, Jie, Chen, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689317/
https://www.ncbi.nlm.nih.gov/pubmed/38036750
http://dx.doi.org/10.1186/s42492-023-00149-0
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author Li, Hailin
Huang, Weiyuan
Wang, Siwen
Balasubramanian, Priya S.
Wu, Gang
Fang, Mengjie
Xie, Xuebin
Zhang, Jie
Dong, Di
Tian, Jie
Chen, Feng
author_facet Li, Hailin
Huang, Weiyuan
Wang, Siwen
Balasubramanian, Priya S.
Wu, Gang
Fang, Mengjie
Xie, Xuebin
Zhang, Jie
Dong, Di
Tian, Jie
Chen, Feng
author_sort Li, Hailin
collection PubMed
description Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (v(e)), volume fraction of plasma space (v(p)), volume transfer constant (K(trans)), and reverse reflux rate constant (k(ep)) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%–52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s42492-023-00149-0.
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spelling pubmed-106893172023-12-02 Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma Li, Hailin Huang, Weiyuan Wang, Siwen Balasubramanian, Priya S. Wu, Gang Fang, Mengjie Xie, Xuebin Zhang, Jie Dong, Di Tian, Jie Chen, Feng Vis Comput Ind Biomed Art Original Article Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (v(e)), volume fraction of plasma space (v(p)), volume transfer constant (K(trans)), and reverse reflux rate constant (k(ep)) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%–52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s42492-023-00149-0. Springer Nature Singapore 2023-12-01 /pmc/articles/PMC10689317/ /pubmed/38036750 http://dx.doi.org/10.1186/s42492-023-00149-0 Text en © The Author(s) 2023 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 Original Article
Li, Hailin
Huang, Weiyuan
Wang, Siwen
Balasubramanian, Priya S.
Wu, Gang
Fang, Mengjie
Xie, Xuebin
Zhang, Jie
Dong, Di
Tian, Jie
Chen, Feng
Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma
title Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma
title_full Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma
title_fullStr Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma
title_full_unstemmed Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma
title_short Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma
title_sort comprehensive integrated analysis of mr and dce-mr radiomics models for prognostic prediction in nasopharyngeal carcinoma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689317/
https://www.ncbi.nlm.nih.gov/pubmed/38036750
http://dx.doi.org/10.1186/s42492-023-00149-0
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