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Measuring decline in white matter integrity after systemic treatment for breast cancer: omitting skeletonization enhances sensitivity

Chemotherapy for non-central nervous system cancers is associated with abnormalities in brain structure and function. Diffusion tensor imaging (DTI) allows for studying in vivo microstructural changes in brain white matter. Tract-based spatial statistics (TBSS) is a widely used processing pipeline i...

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Autores principales: Mzayek, Yasmin, de Ruiter, Michiel B., Oldenburg, Hester S. A., Reneman, Liesbeth, Schagen, Sanne B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286227/
https://www.ncbi.nlm.nih.gov/pubmed/32705463
http://dx.doi.org/10.1007/s11682-020-00319-1
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author Mzayek, Yasmin
de Ruiter, Michiel B.
Oldenburg, Hester S. A.
Reneman, Liesbeth
Schagen, Sanne B.
author_facet Mzayek, Yasmin
de Ruiter, Michiel B.
Oldenburg, Hester S. A.
Reneman, Liesbeth
Schagen, Sanne B.
author_sort Mzayek, Yasmin
collection PubMed
description Chemotherapy for non-central nervous system cancers is associated with abnormalities in brain structure and function. Diffusion tensor imaging (DTI) allows for studying in vivo microstructural changes in brain white matter. Tract-based spatial statistics (TBSS) is a widely used processing pipeline in which DTI data are typically normalized to a generic DTI template and then ‘skeletonized’ to compensate for misregistration effects. However, this approach greatly reduces the overall white matter volume that is subjected to statistical analysis, leading to information loss. Here, we present a re-analysis of longitudinal data previously analyzed with standard TBSS (Menning et al., BIB 2018, 324–334). For our current approach, we constructed a pipeline with an optimized registration method in Advanced Normalization Tools (ANTs) where DTI data are registered to a study-specific, high-resolution T1 template and the skeletonization step is omitted. In a head to head comparison, we show that with our novel approach breast cancer survivors who had received chemotherapy plus or minus endocrine therapy (BC + SYST, n = 26) showed a global decline in overall FA that was not present in breast cancer survivors who did not receive systemic therapy (BC-SYST, n = 23) or women without a cancer diagnosis (no cancer controls, NC, n = 30). With the standard TBSS approach we did not find any group differences. Moreover, voxel-based analysis for our novel pipeline showed a widespread decline in FA in the BC + SYST compared to the NC group. Interestingly, the BC-SYST group also showed a decline in FA compared to the NC group, although in much less voxels. These results were not found with the standard TBSS approach. We demonstrate that a modified processing pipeline makes DTI data more sensitive to detecting changes in white matter integrity in non-CNS cancer patients after treatment, particularly chemotherapy.
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spelling pubmed-82862272021-07-20 Measuring decline in white matter integrity after systemic treatment for breast cancer: omitting skeletonization enhances sensitivity Mzayek, Yasmin de Ruiter, Michiel B. Oldenburg, Hester S. A. Reneman, Liesbeth Schagen, Sanne B. Brain Imaging Behav Original Research Chemotherapy for non-central nervous system cancers is associated with abnormalities in brain structure and function. Diffusion tensor imaging (DTI) allows for studying in vivo microstructural changes in brain white matter. Tract-based spatial statistics (TBSS) is a widely used processing pipeline in which DTI data are typically normalized to a generic DTI template and then ‘skeletonized’ to compensate for misregistration effects. However, this approach greatly reduces the overall white matter volume that is subjected to statistical analysis, leading to information loss. Here, we present a re-analysis of longitudinal data previously analyzed with standard TBSS (Menning et al., BIB 2018, 324–334). For our current approach, we constructed a pipeline with an optimized registration method in Advanced Normalization Tools (ANTs) where DTI data are registered to a study-specific, high-resolution T1 template and the skeletonization step is omitted. In a head to head comparison, we show that with our novel approach breast cancer survivors who had received chemotherapy plus or minus endocrine therapy (BC + SYST, n = 26) showed a global decline in overall FA that was not present in breast cancer survivors who did not receive systemic therapy (BC-SYST, n = 23) or women without a cancer diagnosis (no cancer controls, NC, n = 30). With the standard TBSS approach we did not find any group differences. Moreover, voxel-based analysis for our novel pipeline showed a widespread decline in FA in the BC + SYST compared to the NC group. Interestingly, the BC-SYST group also showed a decline in FA compared to the NC group, although in much less voxels. These results were not found with the standard TBSS approach. We demonstrate that a modified processing pipeline makes DTI data more sensitive to detecting changes in white matter integrity in non-CNS cancer patients after treatment, particularly chemotherapy. Springer US 2020-07-23 2021 /pmc/articles/PMC8286227/ /pubmed/32705463 http://dx.doi.org/10.1007/s11682-020-00319-1 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Research
Mzayek, Yasmin
de Ruiter, Michiel B.
Oldenburg, Hester S. A.
Reneman, Liesbeth
Schagen, Sanne B.
Measuring decline in white matter integrity after systemic treatment for breast cancer: omitting skeletonization enhances sensitivity
title Measuring decline in white matter integrity after systemic treatment for breast cancer: omitting skeletonization enhances sensitivity
title_full Measuring decline in white matter integrity after systemic treatment for breast cancer: omitting skeletonization enhances sensitivity
title_fullStr Measuring decline in white matter integrity after systemic treatment for breast cancer: omitting skeletonization enhances sensitivity
title_full_unstemmed Measuring decline in white matter integrity after systemic treatment for breast cancer: omitting skeletonization enhances sensitivity
title_short Measuring decline in white matter integrity after systemic treatment for breast cancer: omitting skeletonization enhances sensitivity
title_sort measuring decline in white matter integrity after systemic treatment for breast cancer: omitting skeletonization enhances sensitivity
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286227/
https://www.ncbi.nlm.nih.gov/pubmed/32705463
http://dx.doi.org/10.1007/s11682-020-00319-1
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