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The Effect of Heterogenous Subregions in Glioblastomas on Survival Stratification: A Radiomics Analysis Using the Multimodality MRI
Intratumor heterogeneity is partly responsible for the poor prognosis of glioblastoma (GBM) patients. In this study, we aimed to assess the effect of different heterogeneous subregions of GBM on overall survival (OS) stratification. A total of 105 GBM patients were retrospectively enrolled and divid...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323415/ https://www.ncbi.nlm.nih.gov/pubmed/34318731 http://dx.doi.org/10.1177/15330338211033059 |
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author | Yin, Lulu Liu, Yan Zhang, Xi Lu, Hongbing Liu, Yang |
author_facet | Yin, Lulu Liu, Yan Zhang, Xi Lu, Hongbing Liu, Yang |
author_sort | Yin, Lulu |
collection | PubMed |
description | Intratumor heterogeneity is partly responsible for the poor prognosis of glioblastoma (GBM) patients. In this study, we aimed to assess the effect of different heterogeneous subregions of GBM on overall survival (OS) stratification. A total of 105 GBM patients were retrospectively enrolled and divided into long-term and short-term OS groups. Four MRI sequences, including contrast-enhanced T1-weighted imaging (T1C), T1, T2, and FLAIR, were collected for each patient. Then, 4 heterogeneous subregions, i.e. the region of entire abnormality (rEA), the regions of contrast-enhanced tumor (rCET), necrosis (rNec) and edema/non-contrast-enhanced tumor (rE/nCET), were manually drawn from the 4 MRI sequences. For each subregion, 50 radiomics features were extracted. The stratification performance of 4 heterogeneous subregions, as well as the performances of 4 MRI sequences, was evaluated both alone and in combination. Our results showed that rEA was superior in stratifying long-and short-term OS. For the 4 MRI sequences used in this study, the FLAIR sequence demonstrated the best performance of survival stratification based on the manual delineation of heterogeneous subregions. Our results suggest that heterogeneous subregions of GBMs contain different prognostic information, which should be considered when investigating survival stratification in patients with GBM. |
format | Online Article Text |
id | pubmed-8323415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-83234152021-08-09 The Effect of Heterogenous Subregions in Glioblastomas on Survival Stratification: A Radiomics Analysis Using the Multimodality MRI Yin, Lulu Liu, Yan Zhang, Xi Lu, Hongbing Liu, Yang Technol Cancer Res Treat Original Article Intratumor heterogeneity is partly responsible for the poor prognosis of glioblastoma (GBM) patients. In this study, we aimed to assess the effect of different heterogeneous subregions of GBM on overall survival (OS) stratification. A total of 105 GBM patients were retrospectively enrolled and divided into long-term and short-term OS groups. Four MRI sequences, including contrast-enhanced T1-weighted imaging (T1C), T1, T2, and FLAIR, were collected for each patient. Then, 4 heterogeneous subregions, i.e. the region of entire abnormality (rEA), the regions of contrast-enhanced tumor (rCET), necrosis (rNec) and edema/non-contrast-enhanced tumor (rE/nCET), were manually drawn from the 4 MRI sequences. For each subregion, 50 radiomics features were extracted. The stratification performance of 4 heterogeneous subregions, as well as the performances of 4 MRI sequences, was evaluated both alone and in combination. Our results showed that rEA was superior in stratifying long-and short-term OS. For the 4 MRI sequences used in this study, the FLAIR sequence demonstrated the best performance of survival stratification based on the manual delineation of heterogeneous subregions. Our results suggest that heterogeneous subregions of GBMs contain different prognostic information, which should be considered when investigating survival stratification in patients with GBM. SAGE Publications 2021-07-28 /pmc/articles/PMC8323415/ /pubmed/34318731 http://dx.doi.org/10.1177/15330338211033059 Text en © The Author(s) 2021 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 pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Article Yin, Lulu Liu, Yan Zhang, Xi Lu, Hongbing Liu, Yang The Effect of Heterogenous Subregions in Glioblastomas on Survival Stratification: A Radiomics Analysis Using the Multimodality MRI |
title | The Effect of Heterogenous Subregions in Glioblastomas on Survival
Stratification: A Radiomics Analysis Using the Multimodality MRI |
title_full | The Effect of Heterogenous Subregions in Glioblastomas on Survival
Stratification: A Radiomics Analysis Using the Multimodality MRI |
title_fullStr | The Effect of Heterogenous Subregions in Glioblastomas on Survival
Stratification: A Radiomics Analysis Using the Multimodality MRI |
title_full_unstemmed | The Effect of Heterogenous Subregions in Glioblastomas on Survival
Stratification: A Radiomics Analysis Using the Multimodality MRI |
title_short | The Effect of Heterogenous Subregions in Glioblastomas on Survival
Stratification: A Radiomics Analysis Using the Multimodality MRI |
title_sort | effect of heterogenous subregions in glioblastomas on survival
stratification: a radiomics analysis using the multimodality mri |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323415/ https://www.ncbi.nlm.nih.gov/pubmed/34318731 http://dx.doi.org/10.1177/15330338211033059 |
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