Stereotactic radiosurgery optimization with hippocampal-sparing in patients treated for brain metastases
Bibliographic record
Abstract
Background and purpose Cranial irradiation is associated with significant neurocognitive sequelae, secondary to radiation-induced damage to hippocampal cells. It has been shown that hippocampal-sparing (HS) leads to modest benefit in neurocognitive function in patients with brain metastases, but further improvement is possible. We hypothesized that improved benefits could be seen using HS in patients treated with stereotactic radiation (HS-SRS). Our study evaluated whether the hippocampal dose could be significantly reduced in the treatment of brain metastases using SRS, while maintaining target coverage. Materials and methods Sixty SRS plans were re-planned to minimize dose to the hippocampus while maintaining target coverage. Patients with metastases within 5 mm of the hippocampus were excluded. Minimum, mean, maximum and dose to 40% (mean equivalent dose in 2 Gy per fraction, EQD 2 to the hippocampus) were compared between SRS and HS-SRS plans. Median number of brain metastases was two. Results Compared to baseline SRS plans, hippocampal-sparing plans demonstrated D min was reduced by 35%, from 0.4 Gy to 0.3 Gy (p-value 0.02). Similarly, D max was reduced by 55%, from 8.2 Gy to 3.6 Gy, D mean by 52%, from 1.6 Gy to 0.5 Gy, and D 40 by 50%, from 1.8 Gy to 0.9 Gy (p-values <0.001). Conclusions Our study demonstrated that further reduction of hippocampal doses of more than 50% is possible in the treatment of brain metastases with SRS using dose optimization. This could result in significantly improved neurocognitive outcomes for patients treated for brain metastases.
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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 | 0.000 |
| 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".