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

摘要

Chemotherapy for non-central nervous system cancers is associated with abnormalities in brain structure and function. Diffusion tensor imaging (DTI) allows for studyingin vivomicrostructural 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.