Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Clinics |
Texto Completo: | https://www.revistas.usp.br/clinics/article/view/214031 |
Resumo: | Objective: To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. Data and methods: The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. Results: Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. Conclusion: A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately. |
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Clinics |
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Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR imagesGlioma of the brainMagnetic resonance imagingIsocitrate dehydrogenaseRadiomicsGenotypeObjective: To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. Data and methods: The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. Results: Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. Conclusion: A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately.Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo2023-06-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/clinics/article/view/21403110.1016/j.clinsp.2023.100238Clinics; Vol. 78 (2023); 100238Clinics; v. 78 (2023); 100238Clinics; Vol. 78 (2023); 1002381980-53221807-5932reponame:Clinicsinstname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/clinics/article/view/214031/196268Copyright (c) 2023 Clinicsinfo:eu-repo/semantics/openAccessZheng, JinjingDong, HaiboLi, MingLin, XueyaoWang, Chaochao2023-07-06T13:05:40Zoai:revistas.usp.br:article/214031Revistahttps://www.revistas.usp.br/clinicsPUBhttps://www.revistas.usp.br/clinics/oai||clinics@hc.fm.usp.br1980-53221807-5932opendoar:2023-07-06T13:05:40Clinics - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images |
title |
Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images |
spellingShingle |
Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images Zheng, Jinjing Glioma of the brain Magnetic resonance imaging Isocitrate dehydrogenase Radiomics Genotype |
title_short |
Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images |
title_full |
Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images |
title_fullStr |
Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images |
title_full_unstemmed |
Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images |
title_sort |
Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images |
author |
Zheng, Jinjing |
author_facet |
Zheng, Jinjing Dong, Haibo Li, Ming Lin, Xueyao Wang, Chaochao |
author_role |
author |
author2 |
Dong, Haibo Li, Ming Lin, Xueyao Wang, Chaochao |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Zheng, Jinjing Dong, Haibo Li, Ming Lin, Xueyao Wang, Chaochao |
dc.subject.por.fl_str_mv |
Glioma of the brain Magnetic resonance imaging Isocitrate dehydrogenase Radiomics Genotype |
topic |
Glioma of the brain Magnetic resonance imaging Isocitrate dehydrogenase Radiomics Genotype |
description |
Objective: To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. Data and methods: The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. Results: Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. Conclusion: A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-06-22 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.revistas.usp.br/clinics/article/view/214031 10.1016/j.clinsp.2023.100238 |
url |
https://www.revistas.usp.br/clinics/article/view/214031 |
identifier_str_mv |
10.1016/j.clinsp.2023.100238 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.revistas.usp.br/clinics/article/view/214031/196268 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Clinics info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Clinics |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo |
publisher.none.fl_str_mv |
Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo |
dc.source.none.fl_str_mv |
Clinics; Vol. 78 (2023); 100238 Clinics; v. 78 (2023); 100238 Clinics; Vol. 78 (2023); 100238 1980-5322 1807-5932 reponame:Clinics instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Clinics |
collection |
Clinics |
repository.name.fl_str_mv |
Clinics - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
||clinics@hc.fm.usp.br |
_version_ |
1800222767408218112 |