A New Approach to Highly Conformal Hippocampal-sparing Whole-brain Radiotherapy: A Feasibility Study
Bibliographic record
Abstract
Background/Aim: Hippocampal-sparing whole-brain radiotherapy (HS-WBRT) is increasingly used for multiple brain metastases. However, most studies have not reported dose conformity indices (CI). In the only study indicating the CI, conformity was low (CI=0.7). We developed a new technique to achieve a significantly higher CI and better dose coverage. Patients and Methods: Ten patients received 30 Gy of HS-WBRT for brain metastases. Three variants of treatment plans (VAR1, VAR2, VAR3) were investigated. Volumetric modulated arc therapy plans with two (2ROT) or three rotations (3ROT) were created for each variant. Plans were compared for compliance with hippocampal sparing criteria, CI (where a higher value indicates better conformity), and homogeneity index (HI, where a lower value indicates better homogeneity). Results: Best results (highest CI, lowest HI) were achieved with the VAR3-3ROT technique (a new method), which yielded a CI=0.92-0.95 and a HI=0.05-0.09. VAR3-2ROT led to a CI=0.90-0.95 and a HI=0.06-0.11. With the other techniques, CI and HI ranged between 0.77-0.87 and 0.15-0.32, respectively. Conclusion: Our new technique achieved both appropriate hippocampal sparing and very high dose conformity of ≥0.9. Significant underdosage outside the hippocampal-sparing area was avoided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".