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Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features
Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366472/ https://www.ncbi.nlm.nih.gov/pubmed/35965511 http://dx.doi.org/10.3389/fonc.2022.848846 |
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author | Liu, Dongming Chen, Jiu Ge, Honglin Hu, Xinhua Yang, Kun Liu, Yong Hu, Guanjie Luo, Bei Yan, Zhen Song, Kun Xiao, Chaoyong Zou, Yuanjie Zhang, Wenbin Liu, Hongyi |
author_facet | Liu, Dongming Chen, Jiu Ge, Honglin Hu, Xinhua Yang, Kun Liu, Yong Hu, Guanjie Luo, Bei Yan, Zhen Song, Kun Xiao, Chaoyong Zou, Yuanjie Zhang, Wenbin Liu, Hongyi |
author_sort | Liu, Dongming |
collection | PubMed |
description | Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors. |
format | Online Article Text |
id | pubmed-9366472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93664722022-08-12 Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features Liu, Dongming Chen, Jiu Ge, Honglin Hu, Xinhua Yang, Kun Liu, Yong Hu, Guanjie Luo, Bei Yan, Zhen Song, Kun Xiao, Chaoyong Zou, Yuanjie Zhang, Wenbin Liu, Hongyi Front Oncol Oncology Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors. Frontiers Media S.A. 2022-07-28 /pmc/articles/PMC9366472/ /pubmed/35965511 http://dx.doi.org/10.3389/fonc.2022.848846 Text en Copyright © 2022 Liu, Chen, Ge, Hu, Yang, Liu, Hu, Luo, Yan, Song, Xiao, Zou, Zhang and Liu https://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 | Oncology Liu, Dongming Chen, Jiu Ge, Honglin Hu, Xinhua Yang, Kun Liu, Yong Hu, Guanjie Luo, Bei Yan, Zhen Song, Kun Xiao, Chaoyong Zou, Yuanjie Zhang, Wenbin Liu, Hongyi Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
title | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
title_full | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
title_fullStr | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
title_full_unstemmed | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
title_short | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
title_sort | differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366472/ https://www.ncbi.nlm.nih.gov/pubmed/35965511 http://dx.doi.org/10.3389/fonc.2022.848846 |
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