Constrained optimization towards marker-based tumor tracking in VMAT
Bibliographic record
Abstract
Abstract This study proposes that incorporating marker-based visibility constraints into the optimization of volumetric modulated arc therapy (VMAT) will generate treatment plans which not only ensure a higher chance of successfully applying real-time tumor tracking techniques, but also simultaneously satisfy dosimetric objectives. This was applied clinically and investigated for multiple disease sites (10 prostate, 5 liver, and 5 lung) using a radiotherapy optimization software ( MonArc ), where these new constraints were added to conventional dosimetric constraints. For all the investigated sites, three fiducial markers were located inside or around the planning target volume (PTV), and VMAT plans were created for each patient. We modified MonArc to analyze the multi-leaf collimator (MLC) beam’s-eye-view at all control points in the gantry arc, while including marker-based visibility constraints of type ‘hard’ (i.e. requiring 100% visibility of all markers, HC) and ‘soft’ (i.e. penalizes visibility for one marker [SC I ] or two markers [SC II ] only) in the optimization process. Dose distributions resulting from the constrained plans (HC, SC I , and SC II ) were compared to the non-constrained plan (NC—plans optimized without visibility constraints) using several quantitative dose metrics including the conformity index, homogeneity index, doses to PTV and to organs-at-risk (OAR). Generally, the NC plan produced the best PTV dose conformity and the least OAR doses for the entire patient datasets, followed by the SC and then HC plans, with all the optimization approaches typically achieving acceptable dose metrics. Across the three disease sites, visibility of all three markers in MLC apertures increased from 32% to 100% of available control points as visibility constraints strengthened. Although dose metrics showed some deterioration for constrained plans (−6% for SC I up to −15% for HC using the PTV average index), the required dosimetric objectives were still satisfied in at least 90% of patients. In conclusion, we demonstrated that marker and tumour visibility constraints can be incorporated with dosimetric objectives to produce treatment plans satisfying both objectives, which should ensure greater success when applying real-time tracking for VMAT delivery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".