SU-E-T-895: A Comparison of RapidArc® and HybridArc® Treatment Plans for Cranial SRS/SRT
Bibliographic record
Abstract
Purpose: This work evaluates two arc therapy techniques: HybridArc (Brainlab, AG), and RapidArc (Varian Medical Systems) for treating cranial tumors with stereotactic radiosurgery/radiotherapy (SRS/SRT). Methods: HybridArc is a new treatment delivery technique which combines optimized dynamic conformal arc therapy with fixed port IMRT. The result is a single dose distribution calculated by summing the distribution from dynamic arcs with the distribution from a number of fixed-port IMRT beams. In a retrospective study of crainial SRS/SRT patients, we compared the dosimetric results of a single 350 degree RapidArc dose distribution, with a single 350 degree HybridArc distribution containing three fixed-port IMRT fields. For each case, a comparison of target and OAR dose volume histograms, maximum and minimum target dose, and conformity index was used to evaluate each planning technique. For each technique the same target dose constraints and OAR maximum dose constraints were used. Results: Both the RapidArc and HybridArc produced comparable plans for a single iteration of their respective optimization routines, however, the HybridArc plans showed superior coverage by the 95% isodose surface and a lower maximum dose, compared with RapidArc. The conformity indices for the minimum covering isodose were improved for the HybridArc distributions thus providing much more sparing of normal tissues. The HybridArc plan is computed in under a minute as compared with 10–15 minutes for RapidArc which greatly improves planning efficiency. Conclusions: For cranial lesions, the new HybridArc technique from Brainlab produces a more conformal distribution than RapidArc in the case of a single arc. The short calculation time of the HybridArc algorithm allows a HybridArc plan to be produced in one tenth of the time compared to RapidArc.
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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 | 0.001 | 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) | 0.003 | 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".