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MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma

OBJECTIVE: Tumor residue after concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients often predicts poor prognosis. Thus, the objective of this retrospective study is to develop a nomogram that combines magnetic resonance (MRI) radiomics features and clinical features to pre...

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Autores principales: Wu, Mengxing, Xu, Weilin, Fei, Yinjiao, Li, Yurong, Yuan, Jinling, Qiu, Lei, Zhang, Yumeng, Chen, Guanhua, Cheng, Yu, Cao, Yuandong, Sun, Xinchen, Zhou, Shu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10225671/
https://www.ncbi.nlm.nih.gov/pubmed/37256173
http://dx.doi.org/10.3389/fonc.2023.1192953
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author Wu, Mengxing
Xu, Weilin
Fei, Yinjiao
Li, Yurong
Yuan, Jinling
Qiu, Lei
Zhang, Yumeng
Chen, Guanhua
Cheng, Yu
Cao, Yuandong
Sun, Xinchen
Zhou, Shu
author_facet Wu, Mengxing
Xu, Weilin
Fei, Yinjiao
Li, Yurong
Yuan, Jinling
Qiu, Lei
Zhang, Yumeng
Chen, Guanhua
Cheng, Yu
Cao, Yuandong
Sun, Xinchen
Zhou, Shu
author_sort Wu, Mengxing
collection PubMed
description OBJECTIVE: Tumor residue after concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients often predicts poor prognosis. Thus, the objective of this retrospective study is to develop a nomogram that combines magnetic resonance (MRI) radiomics features and clinical features to predict the early response of locally advanced nasopharyngeal carcinoma (LA-NPC). METHODS: A total of 91 patients with LA-NPC were included in this study. Patients were randomly divided into training and validation cohorts at a ratio of 3:1. Univariate and multivariate analyses were performed on the clinical parameters of the patients to select clinical features to build a clinical model. In the training cohort, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used to select radiomics features for construction of a radiomics model. The logistic regression algorithm was then used to combine the clinical features with the radiomics features to construct the clinical radiomics nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical radiomics nomogram. RESULTS: Platelet lymphocyte ratio (PLR) and nasopharyngeal tumor volume were identified as independent predictors of early response in patients with locally advanced nasopharyngeal carcinoma. A total of 5502 radiomics features were extracted, from which 25 radiomics features were selected to construct the radiomics model. The clinical radiomics nomogram demonstrated the highest AUC in both the training and validation cohorts (training cohort 0.975 vs 0.973 vs 0.713; validation cohort 0.968 vs 0.952 vs 0.706). The calibration curve and DCA indicated good predictive performance for the nomogram. CONCLUSION: A clinical radiomics nomogram, which combines clinical features with radiomics features based on MRI, can predict early tumor regression in patients with LA-NPC. The performance of the nomogram is superior to that of either the clinical model or radiomics model alone. Therefore, it can be used to identify patients without CR at an early stage and provide guidance for personalized therapy.
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spelling pubmed-102256712023-05-30 MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma Wu, Mengxing Xu, Weilin Fei, Yinjiao Li, Yurong Yuan, Jinling Qiu, Lei Zhang, Yumeng Chen, Guanhua Cheng, Yu Cao, Yuandong Sun, Xinchen Zhou, Shu Front Oncol Oncology OBJECTIVE: Tumor residue after concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients often predicts poor prognosis. Thus, the objective of this retrospective study is to develop a nomogram that combines magnetic resonance (MRI) radiomics features and clinical features to predict the early response of locally advanced nasopharyngeal carcinoma (LA-NPC). METHODS: A total of 91 patients with LA-NPC were included in this study. Patients were randomly divided into training and validation cohorts at a ratio of 3:1. Univariate and multivariate analyses were performed on the clinical parameters of the patients to select clinical features to build a clinical model. In the training cohort, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used to select radiomics features for construction of a radiomics model. The logistic regression algorithm was then used to combine the clinical features with the radiomics features to construct the clinical radiomics nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical radiomics nomogram. RESULTS: Platelet lymphocyte ratio (PLR) and nasopharyngeal tumor volume were identified as independent predictors of early response in patients with locally advanced nasopharyngeal carcinoma. A total of 5502 radiomics features were extracted, from which 25 radiomics features were selected to construct the radiomics model. The clinical radiomics nomogram demonstrated the highest AUC in both the training and validation cohorts (training cohort 0.975 vs 0.973 vs 0.713; validation cohort 0.968 vs 0.952 vs 0.706). The calibration curve and DCA indicated good predictive performance for the nomogram. CONCLUSION: A clinical radiomics nomogram, which combines clinical features with radiomics features based on MRI, can predict early tumor regression in patients with LA-NPC. The performance of the nomogram is superior to that of either the clinical model or radiomics model alone. Therefore, it can be used to identify patients without CR at an early stage and provide guidance for personalized therapy. Frontiers Media S.A. 2023-05-15 /pmc/articles/PMC10225671/ /pubmed/37256173 http://dx.doi.org/10.3389/fonc.2023.1192953 Text en Copyright © 2023 Wu, Xu, Fei, Li, Yuan, Qiu, Zhang, Chen, Cheng, Cao, Sun and Zhou 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
Wu, Mengxing
Xu, Weilin
Fei, Yinjiao
Li, Yurong
Yuan, Jinling
Qiu, Lei
Zhang, Yumeng
Chen, Guanhua
Cheng, Yu
Cao, Yuandong
Sun, Xinchen
Zhou, Shu
MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma
title MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma
title_full MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma
title_fullStr MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma
title_full_unstemmed MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma
title_short MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma
title_sort mri-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10225671/
https://www.ncbi.nlm.nih.gov/pubmed/37256173
http://dx.doi.org/10.3389/fonc.2023.1192953
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