Cargando…

A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma

BACKGROUND: Methylprednisolone is recommended as the front-line therapy for radiation-induced brain necrosis (RN) after radiotherapy for nasopharyngeal carcinoma. However, some patients fail to benefit from methylprednisolone or even progress. This study aimed to develop and validate a radiomic mode...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhuo, Xiaohuang, Zhao, Huiying, Chen, Meiwei, Mu, Youqing, Li, Yi, Cai, Jinhua, Li, Honghong, Xu, Yongteng, Tang, Yamei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9979431/
https://www.ncbi.nlm.nih.gov/pubmed/36859353
http://dx.doi.org/10.1186/s13014-023-02235-2
_version_ 1784899724198805504
author Zhuo, Xiaohuang
Zhao, Huiying
Chen, Meiwei
Mu, Youqing
Li, Yi
Cai, Jinhua
Li, Honghong
Xu, Yongteng
Tang, Yamei
author_facet Zhuo, Xiaohuang
Zhao, Huiying
Chen, Meiwei
Mu, Youqing
Li, Yi
Cai, Jinhua
Li, Honghong
Xu, Yongteng
Tang, Yamei
author_sort Zhuo, Xiaohuang
collection PubMed
description BACKGROUND: Methylprednisolone is recommended as the front-line therapy for radiation-induced brain necrosis (RN) after radiotherapy for nasopharyngeal carcinoma. However, some patients fail to benefit from methylprednisolone or even progress. This study aimed to develop and validate a radiomic model to predict the response to methylprednisolone in RN. METHODS: Sixty-six patients receiving methylprednisolone were enrolled. In total, 961 radiomic features were extracted from the pre-treatment magnetic resonance imagings of the brain. Least absolute shrinkage and selection operator regression was then applied to construct the radiomics signature. Combined with independent clinical predictors, a radiomics model was built with multivariate logistic regression analysis. Discrimination, calibration and clinical usefulness of the model were assessed. The model was internally validated using 10-fold cross-validation. RESULTS: The radiomics signature consisted of 16 selected features and achieved favorable discrimination performance. The radiomics model incorporating the radiomics signature and the duration between radiotherapy and RN diagnosis, yielded an AUC of 0.966 and an optimism-corrected AUC of 0.967 via 10-fold cross-validation, which also revealed good discrimination. Calibration curves showed good agreement. Decision curve analysis confirmed the clinical utility of the model. CONCLUSIONS: The presented radiomics model can be conveniently used to facilitate individualized prediction of the response to methylprednisolone in patients with RN. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-023-02235-2.
format Online
Article
Text
id pubmed-9979431
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-99794312023-03-03 A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma Zhuo, Xiaohuang Zhao, Huiying Chen, Meiwei Mu, Youqing Li, Yi Cai, Jinhua Li, Honghong Xu, Yongteng Tang, Yamei Radiat Oncol Research BACKGROUND: Methylprednisolone is recommended as the front-line therapy for radiation-induced brain necrosis (RN) after radiotherapy for nasopharyngeal carcinoma. However, some patients fail to benefit from methylprednisolone or even progress. This study aimed to develop and validate a radiomic model to predict the response to methylprednisolone in RN. METHODS: Sixty-six patients receiving methylprednisolone were enrolled. In total, 961 radiomic features were extracted from the pre-treatment magnetic resonance imagings of the brain. Least absolute shrinkage and selection operator regression was then applied to construct the radiomics signature. Combined with independent clinical predictors, a radiomics model was built with multivariate logistic regression analysis. Discrimination, calibration and clinical usefulness of the model were assessed. The model was internally validated using 10-fold cross-validation. RESULTS: The radiomics signature consisted of 16 selected features and achieved favorable discrimination performance. The radiomics model incorporating the radiomics signature and the duration between radiotherapy and RN diagnosis, yielded an AUC of 0.966 and an optimism-corrected AUC of 0.967 via 10-fold cross-validation, which also revealed good discrimination. Calibration curves showed good agreement. Decision curve analysis confirmed the clinical utility of the model. CONCLUSIONS: The presented radiomics model can be conveniently used to facilitate individualized prediction of the response to methylprednisolone in patients with RN. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-023-02235-2. BioMed Central 2023-03-01 /pmc/articles/PMC9979431/ /pubmed/36859353 http://dx.doi.org/10.1186/s13014-023-02235-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhuo, Xiaohuang
Zhao, Huiying
Chen, Meiwei
Mu, Youqing
Li, Yi
Cai, Jinhua
Li, Honghong
Xu, Yongteng
Tang, Yamei
A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma
title A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma
title_full A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma
title_fullStr A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma
title_full_unstemmed A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma
title_short A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma
title_sort radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9979431/
https://www.ncbi.nlm.nih.gov/pubmed/36859353
http://dx.doi.org/10.1186/s13014-023-02235-2
work_keys_str_mv AT zhuoxiaohuang aradiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT zhaohuiying aradiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT chenmeiwei aradiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT muyouqing aradiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT liyi aradiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT caijinhua aradiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT lihonghong aradiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT xuyongteng aradiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT tangyamei aradiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT zhuoxiaohuang radiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT zhaohuiying radiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT chenmeiwei radiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT muyouqing radiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT liyi radiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT caijinhua radiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT lihonghong radiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT xuyongteng radiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma
AT tangyamei radiomicsmodelforpredictingtheresponsetomethylprednisoloneinbrainnecrosisafterradiotherapyfornasopharyngealcarcinoma