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The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas

BACKGROUND: The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. PURPOSE: To explore the value of a radiomic model b...

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Autores principales: Niu, Lei, Feng, Wei-hua, Duan, Chong-feng, Liu, Ying-chao, Liu, Ji-hua, Liu, Xue-jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604586/
https://www.ncbi.nlm.nih.gov/pubmed/33163535
http://dx.doi.org/10.1155/2020/4630218
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author Niu, Lei
Feng, Wei-hua
Duan, Chong-feng
Liu, Ying-chao
Liu, Ji-hua
Liu, Xue-jun
author_facet Niu, Lei
Feng, Wei-hua
Duan, Chong-feng
Liu, Ying-chao
Liu, Ji-hua
Liu, Xue-jun
author_sort Niu, Lei
collection PubMed
description BACKGROUND: The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. PURPOSE: To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma. MATERIALS AND METHODS: A retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV). Patients were divided into a primary dataset and a validation dataset at a ratio of 7 : 3 using a stratified random sampling; radiomic features were extracted using A.K. (Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses. Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation. ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model. RESULTS: The radiomic model showed good discrimination in both the primary dataset (AUC = 0.87, 95% CI: 0.754 to 0.855, ACC = 0.798, sensitivity = 85.5%, specificity = 75.4%, positive predictive value = 0.734, and negative predictive value = 0.867) and the validation dataset (AUC = 0.86, 95% CI: 0.690 to 0.913, ACC = 0.789, sensitivity = 91.3%, specificity = 69.0%, positive predictive value = 0.700, and negative predictive value = 0.909). CONCLUSION: The radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma.
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spelling pubmed-76045862020-11-05 The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas Niu, Lei Feng, Wei-hua Duan, Chong-feng Liu, Ying-chao Liu, Ji-hua Liu, Xue-jun Biomed Res Int Research Article BACKGROUND: The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. PURPOSE: To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma. MATERIALS AND METHODS: A retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV). Patients were divided into a primary dataset and a validation dataset at a ratio of 7 : 3 using a stratified random sampling; radiomic features were extracted using A.K. (Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses. Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation. ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model. RESULTS: The radiomic model showed good discrimination in both the primary dataset (AUC = 0.87, 95% CI: 0.754 to 0.855, ACC = 0.798, sensitivity = 85.5%, specificity = 75.4%, positive predictive value = 0.734, and negative predictive value = 0.867) and the validation dataset (AUC = 0.86, 95% CI: 0.690 to 0.913, ACC = 0.789, sensitivity = 91.3%, specificity = 69.0%, positive predictive value = 0.700, and negative predictive value = 0.909). CONCLUSION: The radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma. Hindawi 2020-10-24 /pmc/articles/PMC7604586/ /pubmed/33163535 http://dx.doi.org/10.1155/2020/4630218 Text en Copyright © 2020 Lei Niu et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Niu, Lei
Feng, Wei-hua
Duan, Chong-feng
Liu, Ying-chao
Liu, Ji-hua
Liu, Xue-jun
The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas
title The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas
title_full The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas
title_fullStr The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas
title_full_unstemmed The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas
title_short The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas
title_sort value of enhanced mr radiomics in estimating the idh1 genotype in high-grade gliomas
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604586/
https://www.ncbi.nlm.nih.gov/pubmed/33163535
http://dx.doi.org/10.1155/2020/4630218
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