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Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma
BACKGROUND: To investigate associations between lower-grade glioma (LGG) mRNA-based subtypes (R1-R4) and MR features. METHODS: mRNA-based subtyping was obtained from the LGG dataset in The Cancer Genome Atlas (TCGA). We identified matching patients (n = 145) in The Cancer Imaging Archive (TCIA) who...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7322922/ https://www.ncbi.nlm.nih.gov/pubmed/32600353 http://dx.doi.org/10.1186/s12883-020-01838-6 |
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author | Liu, Zhenyin Zhang, Jing |
author_facet | Liu, Zhenyin Zhang, Jing |
author_sort | Liu, Zhenyin |
collection | PubMed |
description | BACKGROUND: To investigate associations between lower-grade glioma (LGG) mRNA-based subtypes (R1-R4) and MR features. METHODS: mRNA-based subtyping was obtained from the LGG dataset in The Cancer Genome Atlas (TCGA). We identified matching patients (n = 145) in The Cancer Imaging Archive (TCIA) who underwent MR imaging. The associations between mRNA-based subtypes and MR features were assessed. RESULTS: In the TCGA-LGG dataset, patients with the R2 subtype had the shortest median OS months (P < 0.05). The time-dependent ROC for the R2 subtype was 0.78 for survival at 12 months, 0.76 for survival at 24 months, and 0.76 for survival at 36 months. In the TCIA-LGG dataset, 41 (23.7%) R1 subtype, 40 (23.1%) R2 subtype, 19 (11.0%) R3 subtype and 45 (26.0%) R4 subtype cases were identified. Multivariate analysis revealed that enhancing margin (ill-defined, OR: 9.985; P = 0.003) and T1 + C/T2 mismatch (yes, OR: 0.091; P = 0.023) were associated with the R1 subtype (AUC: 0.708). The average accuracy of the ten-fold cross validation was 71%. Proportion of contrast-enhanced (CE) tumour (> 5%, OR: 14.733; P < 0.001) and necrosis/cystic changes (yes, OR: 0.252; P = 0.009) were associated with the R2 subtype (AUC: 0.832). The average accuracy of the ten-fold cross validation was 82%. Haemorrhage (yes, OR: 8.55; P < 0.001) was positively associated with the R3 subtype (AUC: 0.689). The average accuracy of the ten-fold cross validation was 87%. Proportion of CE tumour (> 5%, OR: 0.14; P < 0.001) was negatively associated with the R4 subtype (AUC: 0.672). The average accuracy of the ten-fold cross validation was 71%. For the prediction of the R2 subtype, the nomogram showed good discrimination and calibration. Decision curve analysis demonstrated that prediction with the R2 model was clinically useful. CONCLUSIONS: Patients with the R2 subtype had the worst prognosis. We demonstrated that MRI features can identify distinct LGG mRNA-based molecular subtypes. |
format | Online Article Text |
id | pubmed-7322922 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73229222020-06-30 Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma Liu, Zhenyin Zhang, Jing BMC Neurol Research Article BACKGROUND: To investigate associations between lower-grade glioma (LGG) mRNA-based subtypes (R1-R4) and MR features. METHODS: mRNA-based subtyping was obtained from the LGG dataset in The Cancer Genome Atlas (TCGA). We identified matching patients (n = 145) in The Cancer Imaging Archive (TCIA) who underwent MR imaging. The associations between mRNA-based subtypes and MR features were assessed. RESULTS: In the TCGA-LGG dataset, patients with the R2 subtype had the shortest median OS months (P < 0.05). The time-dependent ROC for the R2 subtype was 0.78 for survival at 12 months, 0.76 for survival at 24 months, and 0.76 for survival at 36 months. In the TCIA-LGG dataset, 41 (23.7%) R1 subtype, 40 (23.1%) R2 subtype, 19 (11.0%) R3 subtype and 45 (26.0%) R4 subtype cases were identified. Multivariate analysis revealed that enhancing margin (ill-defined, OR: 9.985; P = 0.003) and T1 + C/T2 mismatch (yes, OR: 0.091; P = 0.023) were associated with the R1 subtype (AUC: 0.708). The average accuracy of the ten-fold cross validation was 71%. Proportion of contrast-enhanced (CE) tumour (> 5%, OR: 14.733; P < 0.001) and necrosis/cystic changes (yes, OR: 0.252; P = 0.009) were associated with the R2 subtype (AUC: 0.832). The average accuracy of the ten-fold cross validation was 82%. Haemorrhage (yes, OR: 8.55; P < 0.001) was positively associated with the R3 subtype (AUC: 0.689). The average accuracy of the ten-fold cross validation was 87%. Proportion of CE tumour (> 5%, OR: 0.14; P < 0.001) was negatively associated with the R4 subtype (AUC: 0.672). The average accuracy of the ten-fold cross validation was 71%. For the prediction of the R2 subtype, the nomogram showed good discrimination and calibration. Decision curve analysis demonstrated that prediction with the R2 model was clinically useful. CONCLUSIONS: Patients with the R2 subtype had the worst prognosis. We demonstrated that MRI features can identify distinct LGG mRNA-based molecular subtypes. BioMed Central 2020-06-29 /pmc/articles/PMC7322922/ /pubmed/32600353 http://dx.doi.org/10.1186/s12883-020-01838-6 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article Liu, Zhenyin Zhang, Jing Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma |
title | Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma |
title_full | Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma |
title_fullStr | Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma |
title_full_unstemmed | Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma |
title_short | Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma |
title_sort | radiogenomics correlation between mr imaging features and mrna-based subtypes in lower-grade glioma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7322922/ https://www.ncbi.nlm.nih.gov/pubmed/32600353 http://dx.doi.org/10.1186/s12883-020-01838-6 |
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