Normal Brain Sparing With Increasing Number of Beams and Isocenters in Volumetric-Modulated Arc Beam Radiosurgery of Multiple Brain Metastases
Bibliographic record
Abstract
Recent studies have reported about the application of volumetric-modulated arc radiotherapy in the treatment of multiple brain metastases. One of the key concerns for these radiosurgical treatments lies in the integral dose within the normal brain tissue, as it has been shown to increase with increasing number of brain tumors treated. In this study, we investigate the potential to improve normal brain tissue sparing specific to volumetric-modulated arc radiotherapy by increasing the number of isocenters and arc beams. Adopting a multi-institutional benchmark study protocol of planning multiple brain metastases via a radiosurgical apparatus, a flattening filter-free TrueBeam RapidArc delivery system (Varian Oncology, Palo Alto, California) was used for a volumetric-modulated arc radiotherapy treatment planning study, where treatment plans for target combinations of N = 1, 3, 6, 9, and 12 targets were developed with increasing numbers of isocenters and arc beams. The treatment plans for each target combination were compared dosimetrically among each other and against the reference Gamma Knife treatment plan from the original benchmark study. We observed that as the number of isocenters or arc beams increased, the normal brain isodose volumes such as 12- to 4-Gy on average decreased by up to 15% for all the studied cases. However, when the best volumetric-modulated arc radiotherapy normal brain isodose volumes were compared against the corresponding reference Gamma Knife values, volumetric-modulated arc radiotherapy remained 100% to 200% higher than those of Gamma Knife for all target combinations. The study results, particularly for the solitary (N = 1) metastases case, directly challenged the general notion of dose equivalence among current radiosurgical modalities. In conclusion, multiple isocenter and multiple arc beam delivery solutions are capable of decreasing normal brain irradiation exposure for volumetric-modulated arc radiotherapy. However, there is further technological development in need for volumetric-modulated arc radiotherapy before similar dosimetric treatment plans could be achievable when compared to Gamma Knife radiosurgery.
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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 | 0.000 | 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 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".