SU‐E‐T‐785: Evaluation of HybridArc‐‐a Novel Treatment Planning and Delivery Approach
Bibliographic record
Abstract
Purpose: This investigation focuses on possible dosimetric and efficiency advantages of HybridArc—a novel treatment planning approach combining optimized dynamic arcs with IMRT beams. Application of this technique to two disparate sites, complex cranial tumors and prostate, was examined. Methods: HybridArc plans were compared to either dynamic conformal arc (DCA) or IMRT plans, in order to determine whether HybridArc offers a synergy through combination of these two techniques. Plans were compared with regard to target volume dose conformity, target volume dose homogeneity, sparing of proximal organs at risk, normal tissue sparing and Monitor Unit (MU) efficiency. Results: HybridArc produced improved and comparable dose conformity for cranial and prostate cases, respectively, compared to IMRT. Using the DCA technique produced inferior results on average in this regard, for both sites. For prostate cases, HybridArc also offered the advantage of improved dose homogeneity in the target volume compared to IMRT. Both arc‐based techniques distribute peripheral dose over larger volumes of normal tissue compared to IMRT, while HybridArc involved slightly greater volumes of normal tissues compared to DCA. Compared to IMRT, cranial cases required 38% more MUs, while for prostate cases, MUs were reduced by 7%. Conclusions: HybridArc is capable of improving dose conformity and dose homogeneity for cranial and prostate cases, respectively. MU efficiency may depend on the complexity of the case. This work results from a collaboration with Brainlab, AG but no financial support has been received by this company during the course of the investigation.
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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 | 0.001 | 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".