Hippocampal subfields volumes and episodic memory in breast cancer patients before and after chemotherapy
Bibliographic record
Abstract
Chemotherapy for breast cancer is likely to cause structural brain changes, particularly in the hippocampus, which plays a key role in memory. Alterations in hippocampal subfields have not been fully described. This study aims to investigate changes in hippocampal subfield volumes in Breast cancer patients before and after chemotherapy, compared to healthy controls. Nineteen patients with breast cancer were evaluated before adjuvant therapy (T1), at one month (T2), and at one-year post-chemotherapy (T3). Healthy controls (n=23) underwent assessments at T1 and T3. Episodic memory retrieval and hippocampal subfield volumes were quantified using high-resolution proton density-weighted images segmented with HippUnfold software. Mixed-model analyses compared hippocampal volume changes at T1 between patients and healthy controls, longitudinally within the patient group (T1, T2, T3), and between the patient group and HC (T1, T3). Associations between memory retrieval scores and hippocampal subfield volumes were evaluated using general linear models. Across all assessments, patients performed worse than healthy controls. Subiculum volume was higher in patients compared to healthy controls at T1. No significant difference in memory abilities and hippocampal volume was found after chemotherapy compared to either before nor to the control group. No association between subfield volumes and episodic memory retrieval scores was observed. The effects are linked to cancer rather than chemotherapy, as no hippocampal volume changes or memory decline occurred post-treatment. Larger subiculum volume may be the result of neuroinflammation. Episodic memory deficits, independent of chemotherapy, suggest cancer-related cognitive impairment and could involved other brain regions or mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".