TY - JOUR
T1 - Measuring decline in white matter integrity after systemic treatment for breast cancer
T2 - Omitting skeletonization enhances sensitivity
AU - Mzayek, Y.
AU - de Ruiter, M.B.
AU - Oldenburg, H.S.A.
AU - Reneman, L.
AU - Schagen, S.B.
N1 - Funding Information: This study was funded by the Dutch Cancer Society (KWF 2009?4284). We are indebted to all patients and controls, as well as physicians and nurses of the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, VU University Medical Center, Flevoziekenhuis, Reinier de Graaf Gasthuis and Academic Medical Center, for providing patients for this study and the research assistants for helping collecting the data. The authors thank Rozemarijn Mattiesing for help with data analysis and?the RHPC facility of the Netherlands Cancer Institute for providing computational resources. Funding Information: This study was funded by the Dutch Cancer Society (KWF 2009–4284). We are indebted to all patients and controls, as well as physicians and nurses of the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, VU University Medical Center, Flevoziekenhuis, Reinier de Graaf Gasthuis and Academic Medical Center, for providing patients for this study and the research assistants for helping collecting the data. The authors thank Rozemarijn Mattiesing for help with data analysis and the RHPC facility of the Netherlands Cancer Institute for providing computational resources. Publisher Copyright: © 2020, The Author(s).
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Advanced Normalization Tools (ANTs)
KW - Advanced normalization tools
KW - Cancer related cognitive impairment
KW - Cancer related cognitive impairment (CRCI)
KW - Diffusion Tensor Imaging
KW - Diffusion tensor imaging (DTI)
KW - Skeletonization
KW - Tract based spatial statistics
KW - Tract based spatial statistics (TBSS)
UR - http://www.scopus.com/inward/record.url?scp=85088518565&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/s11682-020-00319-1
DO - https://doi.org/10.1007/s11682-020-00319-1
M3 - Article
C2 - 32705463
SN - 1931-7557
VL - 15
SP - 1191
EP - 1200
JO - Brain Imaging and Behavior
JF - Brain Imaging and Behavior
IS - 3
ER -