Multimodal imaging with magnetization transfer and diffusion tensor imaging reveals evidence of myelin damage in children and youth treated for a brain tumor
Bibliographic record
Abstract
Abstract Background The microstructural damage underlying compromise of white matter following treatment for pediatric brain tumors is unclear. We use multimodal imaging employing advanced diffusion tensor imaging (DTI) and magnetization transfer imaging (MTI) MRI methods to examine chronic microstructural damage to white matter in children and adolescents treated for pediatric brain tumor. Notably, MTI may be more sensitive to macromolecular content, including myelin, than DTI. Methods Fifty patients treated for brain tumors (18 treated with surgery ± chemotherapy and 32 treated with surgery followed by cranial–spinal radiation; time from diagnosis to scan ~6 years) and 45 matched healthy children completed both MTI and DTI scans. Voxelwise and region-of-interest approaches were employed to compare white matter microstructure metrics (magnetization transfer ratio (MTR); DTI— fractional anisotropy [FA], radial diffusivity [RD], axial diffusivity [AD], mean diffusivity [MD]) between patients and healthy controls. Results MTR was decreased across multiple white matter tracts in patients when compared to healthy children, P < .001. These differences were observed for both patients treated with radiation and those treated with only surgery, P < .001. We also found that children and adolescents treated for brain tumors exhibit decreased FA and increased RD/AD/MD compared to their healthy counterparts in several white matter regions, Ps < .02. Finally, we observed that MTR and DTI metrics were related to multiple white matter tracts in patients, Ps < .01, but not healthy control children. Conclusions Our findings provide evidence that the white matter damage observed in patients years after treatment of pediatric posterior fossa tumors, likely reflects myelin disruption.
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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 | 0.001 |
| 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) | 0.000 | 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".