Intra‐arc binary collimation with dynamic axes trajectory optimization for the SRS treatment of multiple metastases with multiple prescriptions
Bibliographic record
Abstract
PURPOSE: This work generates multi-metastases cranial stereotactic radiosurgery/radiotherapy (SRS/SRT) plans using a novel treatment planning technique in which dynamic couch, collimator, and gantry trajectories are used with periodic binary target collimation. The performance of this planning technique is evaluated against conventional volumetric arc therapy (VMAT) planning in terms of various dose and plan quality metrics. METHODS: A 3D cost space (referred to herein as the combined optimization of dynamic axes or CODA cube) was calculated based on an overlap between targets and organs-at-risk (OARs) and uncollimated areas between targets (island blocking) for all combinations of couch, gantry, and collimator angles. Gradient descent through the cube was applied to determine dynamic trajectories. At each control point (CP), each target can either be conformally treated or blocked by the multi-leaf collimator (referred to as intra-arc binary collimation, iABC). Simulated annealing was used to optimize the collimation patterns throughout the arcs as well as the monitor units (MUs) delivered at each CP. Seven previously treated VMAT plans were selected for comparison against the CODA-iABC planning technique. Two CODA-iABC plans were developed: a single gantry arc plan and a plan with one gantry arc and two couch arcs. Plan quality comparison metrics included maximum and mean dose to OARs (brainstem, chiasm, optic nerves, eyes, and lenses), the volume of normal brain receiving 12 Gy (V12Gy), total MUs, target conformity, and dose-gradient index. RESULTS: Treatment plans generated with 1-arc CODA-iABC plans delivered an average of 21% and 30% higher maximum and mean doses to brainstem, respectively, when compared to VMAT plans. Treatment plans generated with 3-arc CODA-iABC used an average of 24% fewer MUs and resulted in an average reduction of 48% maximum dose and 50% mean dose to the OARs, when compared to VMAT. Target conformity values were worse in both CODA-iABC plans than VMAT by an average of 35% and 15%, respectively. There are no significant differences in V12Gy for all three planning techniques; however, 3-arc CODA-iABC is more effective at reducing dose to normal brain in the low-dose region (<12 Gy). CONCLUSION: CODA-iABC is a novel planning technique that has been developed to automatically generate patient-specific multi-axis trajectories for multiple metastases cranial SRS/SRT. This work has demonstrated the feasibility of planning with this novel method. The 1-arc CODA-iABC planning technique is slightly dosimetric inferior to VMAT. With an increased sampling of a three-dimensional CODA cube by using a 3-arc CODA-iABC technique, there was improved total dose sparing to all the OARs and increased MU efficiency, but with a cost in target conformity, when compared to VMAT.
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 | 0.000 | 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".