SU‐E‐T‐669: Dose Interplay Effects in Stereotactic Radiosurgery (SRS) of Multiple Brain Lesions
Bibliographic record
Abstract
Purpose: Volumetric modulated arc therapy (VMAT) has enabled rapid treatments of multiple brain tumors with a single or few isocenters. We investigated inter‐lesion dose interplay effects for such a treatment and compared against standard multi‐isocenteric Gamma Knife (GK) or dynamic‐conformal‐arc (DCA) SRS deliveries. Methods: A patient case with 12 intracranial targets and simulated cases with 2–60 targets in the brain parenchyma were used for the study. For the patient case, all targets and organs‐at‐risk were contoured by a senior clinician. A subset of 3, 6, 9 and 12 targets were then planned at different institutions for GK, DCA and VMAT SRS. Identical dose‐volume constraints to the targets and critical structures were applied. Each target was prescribed with 20 Gy covering at least 99% of the target volume. Relationships between the mean 4‐Gy to 12‐Gy isodose volumes per lesion versus increasing number of lesions were analyzed for each modality. Results: For all the cases, 12‐Gy isodose volumes per lesion exhibited negligible dependence with the increasing number of targets for GK SRS and DCA deliveries. However, for VMAT delivery, a strong statistically significant dependence with increasing number of targets was found at all levels of isodose volumes. For example, the increase in the 12‐Gy volumes of the patient case was 0.068+/−0.016 cc/lesion (p=0.05) for the VMAT delivery in contrast to 0.0+/−0.0 cc/lesion (p < 0.0001) for both GK and DCA deliveries. The increase in the 4‐Gy isodose volumes was 2.79+/−0.40 cc/lesion (p=0.02) for the VMAT delivery, and 2.13+/−0.15 cc/lesion (p=0.005) for the DCA delivery in contrast to 0.08+/−0.29 cc/lesion (p=0.10) for the GK delivery. Conclusion: Significant dose interplay effects were found for single‐or few‐isocenter VMAT SRS of multiple lesions, somewhat for multi‐isocenteric DCA SRS, but nearly negligible for GK SRS treatments.
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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 | 0.002 |
| 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".