Radiation dose to circumscribed brain regions and neurocognitive function in patients with meningioma
Bibliographic record
Abstract
Abstract Background Although radiation (RT) is standard treatment for many brain tumors, it may contribute to neurocognitive decline. The objective of this study was to investigate associations between RT dose to circumscribed brain regions and specific neurocognitive domains in patients with meningioma. Methods We undertook a retrospective study of 40 patients with meningioma who received RT and underwent an in-depth clinical neurocognitive assessment. Radiation dosimetry characteristics were delineated based on treatment planning computerized tomography co-registered with contrast-enhanced 3D T1-weighted magnetic resonance imaging. Principal components analysis was applied to organize neurocognitive test scores into factors, and multivariate multiple linear regression models were undertaken to examine if RT dose to circumscribed brain regions is associated with specific neurocognitive outcomes. Results Radiation dose to brain regions was associated with neurocognitive functions across a number of domains. High dose to the parietal-occipital region was associated with slower visuomotor processing speed (mean dose, β = −1.100, P = .017; dose to 50% of the region [D50], β = −0.697, P = .049). In contrast, high dose to the dorsal frontal region was associated with faster visuomotor processing speed (mean dose, β = 0.001, P = .036). Conclusions These findings suggest that RT delivered to brain regions (ie, parietal-occipital areas) may contribute to poor neurocognitive outcomes. Given that modern radiotherapy techniques allow for precise targeting of dose delivered to brain regions, prospective trials examining relations between dose and neurocognitive functions are warranted to confirm these preliminary results.
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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 | 0.001 | 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) | 0.001 | 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".