A study on the changes of white matter microstructure in breast cancer patients undergoing chemotherapy based on DTI technology
Bibliographic record
Abstract
BackgroundBreast cancer is a prevalent cancer affecting women globally, with incidence rates rising rapidly.PurposeTo examine the diffusion tensor imaging (DTI) data of patients with breast cancer before and after chemotherapy through tract-based spatial statistical analysis (TBSS).Material and MethodsCognitive and neuropsychological tests and whole-brain DTI were administered to patients with breast cancer who did not receive postoperative chemotherapy (C-) or received postoperative chemotherapy (C+) and healthy controls (HCs). Structural differences across groups were compared through fractional anisotropy (FA), mean diffusivity rate (MD), radial diffusivity tensor (RD), and axial diffusivity tensor (AD). Spearman's correlation analysis was employed to explore the association of FA, MD, RD, and AD values in different brain regions with the results of cognitive and neuropsychological tests, as well as the relationship between DTI parameters and cognitive performance as measured by the Montreal Cognitive Assessment (MoCA) scores.ResultsCompared with the C- group, the C + group exhibited significant reduced FA values and increased MD and RD values in the genu of the corpus callosum, bilateral anterior and superior corona radiata, left posterior thalamic radiation, left external capsule, and bilateral superior longitudinal fasciculus. Spearman's correlation analysis showed a notable association between reduced FA values in specific regions and decreased cognitive performance, as measured by MoCA scores.ConclusionThese findings suggest that the alterations in white matter microstructure induced by breast cancer chemotherapy may contribute to cognitive decline. Further research is warranted to strengthen evidence for this relationship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".