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Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

Objective: Chromosomal 1p/19q co-deletion is recognized as a diagnostic, prognostic, and predictive biomarker in lower grade glioma (LGG). This study aims to construct a radiomics signature to non-invasively predict the 1p/19q co-deletion status in LGG. Methods: Ninety-six patients with pathology-co...

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Autores principales: Kong, Ziren, Jiang, Chendan, Zhang, Yiwei, Liu, Sirui, Liu, Delin, Liu, Zeyu, Chen, Wenlin, Liu, Penghao, Yang, Tianrui, Lyu, Yuelei, Zhao, Dachun, You, Hui, Wang, Yu, Ma, Wenbin, Feng, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642873/
https://www.ncbi.nlm.nih.gov/pubmed/33192984
http://dx.doi.org/10.3389/fneur.2020.551771
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author Kong, Ziren
Jiang, Chendan
Zhang, Yiwei
Liu, Sirui
Liu, Delin
Liu, Zeyu
Chen, Wenlin
Liu, Penghao
Yang, Tianrui
Lyu, Yuelei
Zhao, Dachun
You, Hui
Wang, Yu
Ma, Wenbin
Feng, Feng
author_facet Kong, Ziren
Jiang, Chendan
Zhang, Yiwei
Liu, Sirui
Liu, Delin
Liu, Zeyu
Chen, Wenlin
Liu, Penghao
Yang, Tianrui
Lyu, Yuelei
Zhao, Dachun
You, Hui
Wang, Yu
Ma, Wenbin
Feng, Feng
author_sort Kong, Ziren
collection PubMed
description Objective: Chromosomal 1p/19q co-deletion is recognized as a diagnostic, prognostic, and predictive biomarker in lower grade glioma (LGG). This study aims to construct a radiomics signature to non-invasively predict the 1p/19q co-deletion status in LGG. Methods: Ninety-six patients with pathology-confirmed LGG were retrospectively included and randomly assigned into training (n = 78) and validation (n = 18) dataset. Three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted magnetic resonance (MR) images and T2-weighted MR images were acquired, and simulated-conventional contrast-enhanced T1 (SC-CE-T1)-weighted images were generated. One hundred and seven shape, first-order, and texture radiomics features were extracted from each imaging modality and selected using the least absolute shrinkage and selection operator on the training dataset. A 3D-radiomics signature based on 3D-CE-T1 and T2-weighted features and a simulated-conventional (SC) radiomics signature based on SC-CE-T1 and T2-weighted features were established using random forest. The radiomics signatures were validated independently and evaluated using receiver operating characteristic (ROC) curves. Tumors with IDH mutations were also separately assessed. Results: Four radiomics features were selected to construct the 3D-radiomics signature and displayed accuracies of 0.897 and 0.833, areas under the ROC curves (AUCs) of 0.940 and 0.889 in the training and validation datasets, respectively. The SC-radiomics signature was constructed with 4 features, but the AUC values were lower than that of the 3D signature. In the IDH-mutated subgroup, the 3D-radiomics signature presented AUCs of 0.950–1.000. Conclusions: The MRI-based radiomics signature can differentiate 1p/19q co-deletion status in LGG with or without predetermined IDH status. 3D-CE-T1-weighted radiomics features are more favorable than SC-CE-T1-weighted features in the establishment of radiomics signatures.
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spelling pubmed-76428732020-11-13 Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas Kong, Ziren Jiang, Chendan Zhang, Yiwei Liu, Sirui Liu, Delin Liu, Zeyu Chen, Wenlin Liu, Penghao Yang, Tianrui Lyu, Yuelei Zhao, Dachun You, Hui Wang, Yu Ma, Wenbin Feng, Feng Front Neurol Neurology Objective: Chromosomal 1p/19q co-deletion is recognized as a diagnostic, prognostic, and predictive biomarker in lower grade glioma (LGG). This study aims to construct a radiomics signature to non-invasively predict the 1p/19q co-deletion status in LGG. Methods: Ninety-six patients with pathology-confirmed LGG were retrospectively included and randomly assigned into training (n = 78) and validation (n = 18) dataset. Three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted magnetic resonance (MR) images and T2-weighted MR images were acquired, and simulated-conventional contrast-enhanced T1 (SC-CE-T1)-weighted images were generated. One hundred and seven shape, first-order, and texture radiomics features were extracted from each imaging modality and selected using the least absolute shrinkage and selection operator on the training dataset. A 3D-radiomics signature based on 3D-CE-T1 and T2-weighted features and a simulated-conventional (SC) radiomics signature based on SC-CE-T1 and T2-weighted features were established using random forest. The radiomics signatures were validated independently and evaluated using receiver operating characteristic (ROC) curves. Tumors with IDH mutations were also separately assessed. Results: Four radiomics features were selected to construct the 3D-radiomics signature and displayed accuracies of 0.897 and 0.833, areas under the ROC curves (AUCs) of 0.940 and 0.889 in the training and validation datasets, respectively. The SC-radiomics signature was constructed with 4 features, but the AUC values were lower than that of the 3D signature. In the IDH-mutated subgroup, the 3D-radiomics signature presented AUCs of 0.950–1.000. Conclusions: The MRI-based radiomics signature can differentiate 1p/19q co-deletion status in LGG with or without predetermined IDH status. 3D-CE-T1-weighted radiomics features are more favorable than SC-CE-T1-weighted features in the establishment of radiomics signatures. Frontiers Media S.A. 2020-10-22 /pmc/articles/PMC7642873/ /pubmed/33192984 http://dx.doi.org/10.3389/fneur.2020.551771 Text en Copyright © 2020 Kong, Jiang, Zhang, Liu, Liu, Liu, Chen, Liu, Yang, Lyu, Zhao, You, Wang, Ma and Feng. http://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 Neurology
Kong, Ziren
Jiang, Chendan
Zhang, Yiwei
Liu, Sirui
Liu, Delin
Liu, Zeyu
Chen, Wenlin
Liu, Penghao
Yang, Tianrui
Lyu, Yuelei
Zhao, Dachun
You, Hui
Wang, Yu
Ma, Wenbin
Feng, Feng
Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas
title Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas
title_full Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas
title_fullStr Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas
title_full_unstemmed Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas
title_short Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas
title_sort thin-slice magnetic resonance imaging-based radiomics signature predicts chromosomal 1p/19q co-deletion status in grade ii and iii gliomas
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642873/
https://www.ncbi.nlm.nih.gov/pubmed/33192984
http://dx.doi.org/10.3389/fneur.2020.551771
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