Cargando…

Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study

PURPOSE: To investigate the capability of an Magnetic resonance imaging (MRI) radiomics model based on pretreatment texture features in predicting the short-term efficacy of recombinant human endostatin (RHES) plus concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC). METHODS: We r...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Lixuan, Yang, Zongxiang, Kang, Min, Ren, Hao, Jiang, Muliang, Tang, Cheng, Hu, Yao, Shen, Mingjun, Lin, Huashan, Long, Liling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134146/
https://www.ncbi.nlm.nih.gov/pubmed/37094106
http://dx.doi.org/10.1177/15330338231160619
_version_ 1785031696102457344
author Huang, Lixuan
Yang, Zongxiang
Kang, Min
Ren, Hao
Jiang, Muliang
Tang, Cheng
Hu, Yao
Shen, Mingjun
Lin, Huashan
Long, Liling
author_facet Huang, Lixuan
Yang, Zongxiang
Kang, Min
Ren, Hao
Jiang, Muliang
Tang, Cheng
Hu, Yao
Shen, Mingjun
Lin, Huashan
Long, Liling
author_sort Huang, Lixuan
collection PubMed
description PURPOSE: To investigate the capability of an Magnetic resonance imaging (MRI) radiomics model based on pretreatment texture features in predicting the short-term efficacy of recombinant human endostatin (RHES) plus concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC). METHODS: We retrospectively enrolled 65 patients newly diagnosed as having NPC and treated with RHES + CCRT. A total of 144 texture features were extracted from the MRI before RHES + CCRT treatment of all the NPC patients. The maximum relevance minimum redundancy (mRMR) method was used to remove redundant, irrelevant texture features, and calculate the Rad score of the primary tumor. Multivariable logistic regression was used to select the most predictive features subset, and prediction models were constructed. The performance of the 3 models in predicting the early response of RHES + CCRT for NPC was explored. RESULTS: The diagnostic efficiency of combined model and radiomics model in distinguishing between the effective and the ineffective groups of patients was found to be moderate. The area under the ROC curve (AUC) of the combined model and radiomics model was 0.74 (95% confidence interval [CI]: 0.62-0.86) and 0.71 (95% CI: 0.58-0.84), respectively, with both being higher than the AUC of the clinics model (0.63, 95% CI: 0.49-0.78). Compared with the radiomics model, the combined model showed marginally improved diagnostic performance in predicting RHES + CCRT treatment response. The accuracy of combined model and radiomics model for RHES + CCRT response assessment in NPC were higher than those of the clinics model (0.723, 0.723 vs 0.677). CONCLUSION: The pretreatment MRI-based radiomics may be a noninvasive and effective method for the prediction of RHES + CCRT early response in patients with NPC.
format Online
Article
Text
id pubmed-10134146
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-101341462023-04-28 Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study Huang, Lixuan Yang, Zongxiang Kang, Min Ren, Hao Jiang, Muliang Tang, Cheng Hu, Yao Shen, Mingjun Lin, Huashan Long, Liling Technol Cancer Res Treat Original Article PURPOSE: To investigate the capability of an Magnetic resonance imaging (MRI) radiomics model based on pretreatment texture features in predicting the short-term efficacy of recombinant human endostatin (RHES) plus concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC). METHODS: We retrospectively enrolled 65 patients newly diagnosed as having NPC and treated with RHES + CCRT. A total of 144 texture features were extracted from the MRI before RHES + CCRT treatment of all the NPC patients. The maximum relevance minimum redundancy (mRMR) method was used to remove redundant, irrelevant texture features, and calculate the Rad score of the primary tumor. Multivariable logistic regression was used to select the most predictive features subset, and prediction models were constructed. The performance of the 3 models in predicting the early response of RHES + CCRT for NPC was explored. RESULTS: The diagnostic efficiency of combined model and radiomics model in distinguishing between the effective and the ineffective groups of patients was found to be moderate. The area under the ROC curve (AUC) of the combined model and radiomics model was 0.74 (95% confidence interval [CI]: 0.62-0.86) and 0.71 (95% CI: 0.58-0.84), respectively, with both being higher than the AUC of the clinics model (0.63, 95% CI: 0.49-0.78). Compared with the radiomics model, the combined model showed marginally improved diagnostic performance in predicting RHES + CCRT treatment response. The accuracy of combined model and radiomics model for RHES + CCRT response assessment in NPC were higher than those of the clinics model (0.723, 0.723 vs 0.677). CONCLUSION: The pretreatment MRI-based radiomics may be a noninvasive and effective method for the prediction of RHES + CCRT early response in patients with NPC. SAGE Publications 2023-04-24 /pmc/articles/PMC10134146/ /pubmed/37094106 http://dx.doi.org/10.1177/15330338231160619 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Article
Huang, Lixuan
Yang, Zongxiang
Kang, Min
Ren, Hao
Jiang, Muliang
Tang, Cheng
Hu, Yao
Shen, Mingjun
Lin, Huashan
Long, Liling
Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study
title Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study
title_full Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study
title_fullStr Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study
title_full_unstemmed Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study
title_short Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study
title_sort performance of pretreatment mri-based radiomics in recombinant human endostatin plus concurrent chemoradiotherapy response prediction in nasopharyngeal carcinoma: a retrospective study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134146/
https://www.ncbi.nlm.nih.gov/pubmed/37094106
http://dx.doi.org/10.1177/15330338231160619
work_keys_str_mv AT huanglixuan performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy
AT yangzongxiang performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy
AT kangmin performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy
AT renhao performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy
AT jiangmuliang performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy
AT tangcheng performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy
AT huyao performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy
AT shenmingjun performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy
AT linhuashan performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy
AT longliling performanceofpretreatmentmribasedradiomicsinrecombinanthumanendostatinplusconcurrentchemoradiotherapyresponsepredictioninnasopharyngealcarcinomaaretrospectivestudy