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(TS)(2)WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients

Glioblastoma (GBM) brain tumor is the most aggressive white matter (WM) invasive cerebral primary neoplasm. Due to its inherently heterogeneous appearance and shape, previous studies pursued either the segmentation precision of the tumors or qualitative analysis of the impact of brain tumors on WM i...

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Autores principales: Zhong, Liming, Li, Tengfei, Shu, Hai, Huang, Chao, Johnson, Jason Michael, Schomer, Donald F, Liu, Ho-Ling, Feng, Qianjin, Yang, Wei, Zhu, Hongtu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688588/
https://www.ncbi.nlm.nih.gov/pubmed/32931941
http://dx.doi.org/10.1016/j.neuroimage.2020.117368
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author Zhong, Liming
Li, Tengfei
Shu, Hai
Huang, Chao
Johnson, Jason Michael
Schomer, Donald F
Liu, Ho-Ling
Feng, Qianjin
Yang, Wei
Zhu, Hongtu
author_facet Zhong, Liming
Li, Tengfei
Shu, Hai
Huang, Chao
Johnson, Jason Michael
Schomer, Donald F
Liu, Ho-Ling
Feng, Qianjin
Yang, Wei
Zhu, Hongtu
author_sort Zhong, Liming
collection PubMed
description Glioblastoma (GBM) brain tumor is the most aggressive white matter (WM) invasive cerebral primary neoplasm. Due to its inherently heterogeneous appearance and shape, previous studies pursued either the segmentation precision of the tumors or qualitative analysis of the impact of brain tumors on WM integrity with manual delineation of tumors. This paper aims to develop a comprehensive analytical pipeline, called (TS)(2)WM, to integrate both the superior performance of brain tumor segmentation and the impact of GBM tumors on the WM integrity via tumor segmentation and tract statistics using the diffusion tensor imaging (DTI) technique. The (TS)(2)WM consists of three components: (i) A dilated densely connected convolutional network (D(2)C(2)N) for automatically segment GBM tumors, (ii) A modified structural connectome processing pipeline to characterize the connectivity pattern of WM bundles, (iii) A multivariate analysis to delineate the local and global associations between different DTI-related measurements and clinical variables on both brain tumors and language-related regions of interest. Among those, the proposed D(2)C(2)N model achieves competitive tumor segmentation accuracy compared with many state-of-the-art tumor segmentation methods. Significant differences in various DTI-related measurements at the streamline, weighted network, and binary network levels (e.g., diffusion properties along major fiber bundles) were found in tumor-related, language-related, and hand motor-related brain regions in 62 GBM patients as compared to healthy subjects from the Human Connectome Project.
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spelling pubmed-76885882021-12-01 (TS)(2)WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients Zhong, Liming Li, Tengfei Shu, Hai Huang, Chao Johnson, Jason Michael Schomer, Donald F Liu, Ho-Ling Feng, Qianjin Yang, Wei Zhu, Hongtu Neuroimage Article Glioblastoma (GBM) brain tumor is the most aggressive white matter (WM) invasive cerebral primary neoplasm. Due to its inherently heterogeneous appearance and shape, previous studies pursued either the segmentation precision of the tumors or qualitative analysis of the impact of brain tumors on WM integrity with manual delineation of tumors. This paper aims to develop a comprehensive analytical pipeline, called (TS)(2)WM, to integrate both the superior performance of brain tumor segmentation and the impact of GBM tumors on the WM integrity via tumor segmentation and tract statistics using the diffusion tensor imaging (DTI) technique. The (TS)(2)WM consists of three components: (i) A dilated densely connected convolutional network (D(2)C(2)N) for automatically segment GBM tumors, (ii) A modified structural connectome processing pipeline to characterize the connectivity pattern of WM bundles, (iii) A multivariate analysis to delineate the local and global associations between different DTI-related measurements and clinical variables on both brain tumors and language-related regions of interest. Among those, the proposed D(2)C(2)N model achieves competitive tumor segmentation accuracy compared with many state-of-the-art tumor segmentation methods. Significant differences in various DTI-related measurements at the streamline, weighted network, and binary network levels (e.g., diffusion properties along major fiber bundles) were found in tumor-related, language-related, and hand motor-related brain regions in 62 GBM patients as compared to healthy subjects from the Human Connectome Project. 2020-09-12 2020-12 /pmc/articles/PMC7688588/ /pubmed/32931941 http://dx.doi.org/10.1016/j.neuroimage.2020.117368 Text en This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
spellingShingle Article
Zhong, Liming
Li, Tengfei
Shu, Hai
Huang, Chao
Johnson, Jason Michael
Schomer, Donald F
Liu, Ho-Ling
Feng, Qianjin
Yang, Wei
Zhu, Hongtu
(TS)(2)WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients
title (TS)(2)WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients
title_full (TS)(2)WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients
title_fullStr (TS)(2)WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients
title_full_unstemmed (TS)(2)WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients
title_short (TS)(2)WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients
title_sort (ts)(2)wm: tumor segmentation and tract statistics for assessing white matter integrity with applications to glioblastoma patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688588/
https://www.ncbi.nlm.nih.gov/pubmed/32931941
http://dx.doi.org/10.1016/j.neuroimage.2020.117368
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